ALBERT

Description: Multiyear climate predictions provide climate outlooks from years to a decade in advance. As multiyear temperature predictions become more mainstream and skillful, guidance is needed to assist practitioners who wish to explore this maturing field. This paper demonstrates the process and considerations of incorporating multiyear temperature predictions into water resources planning. Multiyear temperature predictions from the Community Earth System Model Decadal Prediction Large Ensemble are presented as discrete and probabilistic products and are used to force two common hydrologic modeling approaches: conceptual and empirical. The approaches are demonstrated to simulate streamflow in the upper Colorado River basin watershed in Colorado, where diagnostics show that increasing temperatures are associated with decreasing streamflows. Using temperature information for lead years 2–6, two analyses are performed: (i) a retrospective hindcast for the climatological period (1981–2010) and (ii) a blind forecast for 2011–15. For the retrospective hindcast, including temperature information improved the percent error as compared with climatology. For the blind forecast, the multiyear temperature prediction for warming was skillful, but the corresponding multiyear average streamflow predictions from both approaches were counterintuitive: with the predicted warming, the multiyear average streamflow was predicted to be lower than the climatological mean; however, the observed multiyear average streamflow was higher than the climatological mean. This was due to above-average precipitation during the prediction time frame, particularly for one of the years. With that year removed, the multiyear streamflow average became lower than the climatological mean. Temperature provides a marginal source of streamflow predictability, but there will be substantial uncertainty until prediction skill for year-to-year climate variability, especially for precipitation, increases.

Description: In the present work, the trend of extreme rainfall indices in the Macro-Metropolis of São Paulo (MMSP) was analyzed and correlated with largescale climatic oscillations. A cluster analysis divided a set of rain gauge stations into three homogeneous regions within MMSP, according to the annual cycle of rainfall. The entire MMSP presented an increase in the total annual rainfall, from 1940 to 2016, of 3 mm per year on average, according to Mann-Kendall test. However, there is evidence that the more urbanized areas have a greater increase in the frequency and magnitude of extreme events, while coastal and mountainous areas, and regions outside large urban areas, have increasing rainfall in a better-distributed way throughout the year. The evolution of extreme rainfall (95th percentile) is significantly correlated with climatic indices. In the center-north part of the MMSP, the combination of Pacific Decadal Oscillation (PDO) and Antarctic Oscillation (AAO) explains 45% of the P95th increase during the wet season. In turn, in southern MMSP, the Temperature of South Atlantic (TSA), the AAO, the El Niño South Oscillation (ENSO) and the Multidecadal Oscillation of the North Atlantic (AMO) better explain the increase in extreme rainfall (R2 = 0.47). However, the same is not observed during the dry season, in which the P95th variation was only negatively correlated with the AMO, undergoing a decrease from the ‘70s until the beginning of this century. The occurrence of rainy anomalous months proved to be more frequent and associated with climatic indices than dry months.

Description: High-resolution, bias-corrected climate data is necessary for climate impact studies at local scales. Gridded historical data is convenient for bias-correction but may contain biases resulting from interpolation. Long-term, quality-controlled station data represent true climatological measurements, but as the distribution of climate stations is irregular, station data are challenging to incorporate into downscaling and bias-correction approaches. Here, we compared six novel methods for constructing full-coverage, high-resolution, bias-corrected climate products using daily maximum temperature simulations from a regional climate model (RCM). Only station data were used for bias-correction. We quantified performance of the six methods with the root-mean-square-error (RMSE) and Perkins skill score (PSS) and used two ANOVA models to analyze how performance varied among methods. We validated the six methods using two calibration periods of observed data (1980-1989 and 1980-2014) and two testing sets of RCM data (1990-2014 and 1980-2014). RMSE for all methods varied throughout the year and was larger in cold months, while PSS was more consistent. Quantile-mapping bias-correction techniques substantially improved PSS, while simple linear transfer functions performed best in improving RMSE. For the 1980-1989 calibration period, simple quantile-mapping techniques outperformed empirical quantile mapping (EQM) in improving PSS. When calibration and testing time periods were equivalent, EQM resulted in the largest improvements in PSS. No one method produced substantial improvements in both RMSE and PSS. Our results indicate that simple quantile-mapping techniques are less prone to overfitting than EQM and are suitable for processing future climate model output, while EQM is ideal for bias-correcting historical climate model output.

Description: In this study, we simulate the magnitude of urban heat islands (UHIs) during heat wave (HWs) in two cities with contrasting climates (Boston and Phoenix) using the Weather Research and Forecasting (WRF) model and quantify their drivers with a newly developed attribution method. During the daytime, a surface UHI (SUHI) is found in Boston mainly caused by the higher urban surface resistance (rs) that reduces the latent heat flux, and the higher urban aerodynamic resistance (ra) that inhibits convective heat transfer between the urban surface and the lower atmosphere. In contrast, a surface urban cool island (SUCI) is found in Phoenix mainly due to the lower urban ra that facilitates convective heat transfer. In terms of near-surface air UHI (AUHI), there is almost no daytime AUHI in either city. At night, a SUHI and an AUHI are identified in Boston due to the stronger release of heat storage in urban areas. In comparison, the lower urban ra in Phoenix enhances convective heat transfer from the atmosphere to the urban surface at night, leading to a positive SUHI but no AUHI. Our study highlights that the magnitude of UHIs or UCIs is strongly controlled by urban-rural differences in terms of aerodynamic features, vegetation and moisture conditions, and heat storage, which show contrasting characteristics in different regions.

Description: Variations in wind resources affect the reliability and feasibility of wind energy. At longer timescales, modes within the climate system and externally forced variability become important as the decades-long lifetimes of wind installations and upfront investment costs are considered. Understanding the influence of teleconnections may yield important insights for skillful seasonal predictions. In this study, several modes of variability, including the Arctic Oscillation (AO), the El Niño-Southern Oscillation (ENSO) and the global surface solar flux, are assessed for their influence on wind energy anomalies in the Upper Midwest (40-52°N, 87-105°W). Monthly wind energy is calculated using extrapolated 80-meter wind fields from reanalysis data for the period 1980-2018. A multiple linear regression analysis is conducted for the monthly turbine energy output anomalies (TEOA) against the effects of synoptic patterns and pressure gradients, as well as the teleconnection indices, for each grid cell and season, yielding information on the spatial and temporal variations in influence throughout the region. The regression model indicated that each of the factors had significant influences on wind energy, though the effects varied spatially and by season. Periods of extremely low production are often embedded in prolonged declines over several months which were the result of a combination of synoptic variability and significant phases of the teleconnections such as large El Niño events, negative AO episodes and volcanically-induced reductions in surface solar flux. Monthly TEOA are found to vary by up to 37 percent, amounting to ±130 MWh and tens of thousands of dollars per turbine.

Description: This study presents spatial models (i.e., thin plate spatially continuous spline surfaces) of adjusted precipitation for Canada at daily, pentad (5-day), and monthly time scales from 1900 to 2015. The input data include manual observations from 3346 stations that were adjusted previously to correct for snow water equivalent (SWE) conversion and various gauge-related issues. In addition to the 42,331 models for daily total precipitation and 1392 monthly total precipitation models 8395 pentad models were developed for the first time, depicting mean precipitation for 73 pentads annually. For much of Canada, mapped precipitation values from this study were higher than those from the corresponding unadjusted models (i.e., models fitted to the unadjusted data), reflecting predominantly the effects of the adjustments to the input data. Error estimates compared favourably to the corresponding unadjusted models. For example, Root generalized cross validation (GCV) estimate (a measure of predictive error) at the daily time scale was 3.6 mm on average for the 1960 to 2003 period as compared to 3.7 mm for the unadjusted models over the same period. There was a dry bias in the predictions relative to recorded values of between 1% and 6.7% of the average precipitations amounts for all time scales. Mean absolute predictive errors of the daily, pentad, and monthly models were 2.5 mm (52.7%), 0.9 mm (37.4%), and 11.2 mm (19.3%), respectively. In general, the model skill was closely tied to the density of the station network. The current adjusted models are available in grid form at ~2-10 km resolutions.

Description: An integrated technique combining global climate model (GCM) simulation results and a statistical time series forecasting model (the autoregressive integrated moving average ARIMA model) was developed to bring together the climate change signal from GCMs to city-level historical observations as an approach to obtain location-specific temperature and precipitation projections. This approach assumes that regional temperature and precipitation time series reflect a combination of an underlying climate change signal series and a regional-deviation-from-the-signal series. An ensemble of GCMs is used to describe and provide the climate change signal, and the ARIMA model is used to model and project the regional deviation. Qualitative and quantitative assessments were conducted for evaluating the projection performance of the hybrid GCM-ARIMA (G-ARIMA) model. The results indicate that the G-ARIMA model can provide projected city-specific daily temperature and precipitation series comparable to historical observations and can have improved projection accuracy for several assessed annual indices compared to a commonly used downscaled projection product. The G-ARIMA model is subject to some limitations and uncertainties from the GCM-provided climate change signal. A notable feature of the G-ARIMA model is the efficiency with which projections can be updated when new observations become available, thus facilitating updating of regional temperature and precipitations projections. Given the increasing need for and use of location-specific climate projections in practical engineering applications, the G-ARIMA model is an option for regional temperature and precipitation projection for such applications.

Description: A high-resolution (4 km) regional climate simulation conducted with the Weather Research and Forecast (WRF) model is used to investigate potential impacts of global warming on skiing conditions in the interior western United States (IWUS). Recent past and near-future climate conditions are compared. The past climate period is from November 1981 to October 2011. The future climate applies to a 30-year period centered on 2050. A pseudo global warming approach is used, with the driver re-analysis dataset perturbed by the CMIP5 ensemble mean model guidance. Using the 30-year retrospective simulation, a vertical adjustment technique is used to determine meteorological parameters in the complex terrain where ski areas are located. For snow water equivalent (SWE), Snow Telemetry sites close to ski areas are used to validate the technique and apply a correction to SWE in ski areas. The vulnerability to climate change is assessed for 71 ski areas in the IWUS considering SWE, artificially produced snow, temperature, and rain. 20 of the ski areas will tend to have fewer than 100 days per season with sufficient natural and artificial snow for skiing. These ski areas are located at either low elevations or low latitudes making these areas the most vulnerable to climate change. Throughout the snow season, natural SWE decreases significantly at the low elevations and low latitudes. At higher elevations changes in SWE are predicted to not be significant in the mid-season. In mid-February, SWE decreases by 11.8% at the top elevations of ski areas while it decreases by 25.8% at the base elevations.

Description: This manuscript describes a software suite that can be used for objective evaluation of tropical cyclones (TCs) in gridded climate data. Using cyclone trajectories derived from 6-hourly data, a comprehensive set of metrics is defined to systematically compare and contrast products to one another. In addition to annual TC climatologies, attention is paid to spatial and temporal patterns of storm occurrence and intensity. Assessment can be performed either on the global scale or regional domains. Simple to visualize ‘scorecards’ allow for rapid credibility assessment. We showcase three key findings enabled by this suite. First, we compare the representation of TCs in seven current-generation global reanalyses and conclude that higher resolution models and those with TC-specific assimilation contain more accurate storm climatologies. Second, using a free-running Earth system model (ESM) we find that full basin refinement is required in variable-resolution configurations to adequately simulate North Atlantic TC frequency. Upstream refinement over northern Africa offers little benefit in simulating storm occurrence but spatial genesis patterns are improved. Finally, we show that TCs simulated by ESMs can be highly sensitive to individual parameterizations in climate models, with North Atlantic TC metrics varying greatly depending on version of the Morrison-Gettelman microphysics package.

Description: Latent and sensible heat fluxes over the oceans are believed to play an important role in the genesis and evolution of marine-based extratropical cyclones (ETCs) and affect rapid cyclogenesis. Observations of ocean surface heat fluxes are limited from existing in-situ and remote sensing platforms, which may not offer sufficient spatial and temporal resolution. Additionally, substantial precipitation frequently veils the ocean surface around ETCs, limiting the capacity of space-borne instruments to observe the surface processes within maturing ETCs. Though designed as a tropics-focused mission, the Cyclone Global Navigation Satellite System (CYGNSS) can observe ocean surface wind speed and heat fluxes within a notable quantity of low-latitude extratropical fronts and cyclones. These observations can assist in understanding how surface processes may play a role in cyclogenesis and evolution. This paper illustrates CYGNSS’s capability to observe extratropical cyclones manifesting in various ocean basins throughout the globe and shows that the observations provide a robust sample of ETCs winds and surface fluxes, as compared with a reanalysis dataset.

Description: Temperature and humidity profiles in the lowest 3 km of the atmosphere provide crucial information in determining the precipitation type which aids forecasters in relaying winter-weather risks. In response to the challenges associated with forecasting mixed-phase environments, this study employs uncrewed aerial vehicles (UAVs) to explore the efficacy of high-resolution temporal and vertical measurements in winter-weather environments. On 19 February 2019, boundary-layer measurements of an Oklahoma winter storm were collected by a UAV and radiosondes. UAV observations show a pronounced surface-based subfreezing layer that corresponds with observed ice pellets at the surface. This is in contrast to the High Resolution Rapid Refresh (HRRR) model analyses, which show a subfreezing layer near the surface that is 3°C warmer than both the UAV and radiosonde observations. Using a spectral-bin-microphysics algorithm designed to provide hydrometeor-phase diagnosis throughout the vertical column, it was found that UAV measurements can improve discrimination between hydrometer types in environments near 0°C. A numerical-modeling study of the same winter-weather event illustrates the potential benefit of vertically sampling a mixed-phase environment at multiple mesonet sites and highlights future scientific and operational questions to be addressed by the UAV community.

Description: Fog has a significant effect on aviation and road transport networks around the world. The International Airport in Abu Dhabi, United Arab Emirates, experiences dense fog during winter months that affect operations at the airport. We describe the fog climatology at the airport using 36 years of aviation routine weather reports (METAR), an important long-term data source, and report on the number of fog days per year, the seasonal cycle, the diurnal cycle, and the duration of fog events. Fog days per year vary from 8 to 51, with a mean of ~23.91 days (standard deviation of 9.83). Events are most frequent from September until March, with December and January being the most active months. November, unexpectedly, has a low number of fog days, which appears to be due to a decrease in aerosol loading in the atmosphere. The most fog days experienced in one month is 13 (March 2004). Fog occurs any time from 1900 to 1100 local time, and the frequency increases as night progresses, peaking around sunrise. Fog events most frequently last 1 h or less. Events of 9 h or more were recorded in January and December, with the longest event lasting 16 h. Events are strongly dependent on the land–sea breeze and seldom form when the wind is blowing from the Arabian Gulf. The thickness of the nocturnal inversion layer increases up to about 500 m AGL on fog days as compared with 273 m AGL on clear-sky days. This study is the first to use the 36-yr dataset to characterize fog climatology at Abu Dhabi Airport.

Description: Various methods have been developed to characterize cloud type, otherwise referred to as cloud regime. These include manual sky observations, combining radiative and cloud vertical properties observed from satellite, surface-based remote sensing, and digital processing of sky imagers. While each methodology has inherent advantages and disadvantages, none of these cloud typing methods actually include measurements of surface shortwave or longwave radiative fluxes. Here, a methodology that relies upon detailed, surface-based radiation and cloud measurements and derived data products to train a random forest machine learning cloud classification model is introduced. Measurements from five years of data from the ARM Southern Great Plains site were compiled to train and independently evaluate the model classification performance. A cloud type accuracy of approximately 80% using the random forest classifier reveals the model is well suited to predict climatological cloud properties. Furthermore, an analysis of the cloud type misclassifications is performed. While physical cloud types may be misreported, the shortwave radiative signatures are similar between misclassified cloud types. From this, we assert the cloud regime model has the capacity to successfully differentiate clouds with comparable cloud-radiative interactions. Therefore, we conclude the model can provide useful cloud property information for fundamental cloud studies, inform renewable energy studies, a tool for numerical model evaluation and parameterization improvement, among many other applications.

Description: This study presents the application of K-means clustering to satellite-based solar irradiation in different regions of Vietnam. The solar irradiation products derived from the Himawari-8 satellite, named AMATERASS by the solar radiation consortium under the Japan Science and Technology Agency (JST), are validated with observations recorded at 5 stations in the period from October 2017 to September 2018 before their use for clustering. High correlations among them enable the use of satellite-based daily global horizontal irradiation (GHI) for spatial variability analysis and regionalization. With respect to the climate regime in Vietnam, the defined 6-cluster groups demonstrate better agreement with the conventionally classified 7 climatic zones rather than the 4 climatic zones of the Köppen classification. The spatial distribution and seasonal variation in the regionalized solar irradiation reflect interchangeable influences of large-scale atmospheric circulation in terms of the East Asian winter monsoon and the South Asian summer monsoon as well as the effect of local topography. Higher daily averaged solar radiation and its weaker seasonal variation were found in two clusters in the southern region where the South Asian summer monsoon dominates in the rainy season. Pronounced seasonal variability in solar irradiation in four clusters in the northern region is associated with the influence of the East Asian monsoon, resulting in its clear reduction during the winter months.

Description: Daily weather regimes are defined around the Kerguelen Islands (Southern Ocean) based on daily 500 hPa geopotential height anomalies derived from the ERA5 ensemble reanalysis over the period 1979-2018. Ten regimes are retained as significant. Their occurrences are highly consistent across reanalysis ensemble members. Regimes show weak seasonality and non-significant long-term trends in their occurrences. Their sequences are usually short (1-3 days), with extreme persistence values above 10 days. Seasonal regime frequency is mostly driven by the phase of the Southern Annular Mode over Antarctica, mid-latitude dynamics over the Southern Ocean like the Pacific South American mode, and to a lesser extent, tropical variability, with significant but weaker relationships with El Niño Southern Oscillation. At the local scale over the Kerguelen Islands, regimes have a strong influence on measured atmospheric and oceanic variables, including minimum and maximum air temperature, mostly driven by horizontal advections, sea water temperature recorded 5 m below the surface, wind speed and sea level pressure. Relationships are weaker for precipitation amounts. Regimes also modify regional contrasts between observational sites in Kerguelen, highlighting strong exposure contrasts. The regimes allow improving our understanding of weather and climate variability and interactions in this region; they will be used in future work to assess past and projected long-term circulation changes in the southern mid-latitudes.

Description: A robust and computationally efficient object tracking algorithm is developed by incorporating various tracking techniques. Physical properties of the objects, such as brightness temperature or reflectivity, are not considered. Therefore, the algorithm is adaptable for tracking convection-like features in simulated data and remotely sensed two-dimensional images. In this algorithm, a first guess of the motion, estimated using the Fourier phase shift, is used to predict the candidates for matching. A disparity score is computed for each target–candidate pair. The disparity also incorporates overlapping criteria in the case of large objects. Then the Hungarian method is applied to identify the best pairs by minimizing the global disparity. The high-disparity pairs are unmatched, and their target and candidate are declared expired and newly initiated objects, respectively. They are tested for merger and split on the basis of their size and overlap with the other objects. The sensitivity of track duration is shown for different disparity and size thresholds. The paper highlights the algorithm’s ability to study convective life cycles using radar and simulated data over Darwin, Australia. The algorithm skillfully tracks individual convective cells (a few pixels in size) and large convective systems. The duration of tracks and cell size are found to be lognormally distributed over Darwin. The evolution of size and precipitation types of isolated convective cells is presented in the Lagrangian perspective. This algorithm is part of a vision for a modular platform [viz., TINT is not TITAN (TINT) and Tracking and Object-Based Analysis of Clouds (tobac)] that will evolve into a sustainable choice to analyze atmospheric features.

Description: Extreme precipitation events are challenging to local and regional stakeholders across the United States. The Missouri River Basin (MoRB), covering an area over 1.29 million km2, is prone to extreme precipitation events. These events are exacerbated by the complex terrain in the west and the numerous weather and climate features which impact the region on a seasonal/annual basis (low-level jets, mesoscale convective systems, extreme cold air intrusions, etc.). Without an in-depth analysis of extreme precipitation in the MoRB, the evolving nature of extreme precipitation is not known. This warrants an analysis of extreme precipitation, especially relating to sub-annual variations when extreme precipitation is more impactful. To this end, data from 131 United States Historical Climatology Network (USHCN) stations were used to determine the nature of extreme precipitation from 1950 – 2019. Annual 99th percentile and annual station maximum precipitation events occur more frequently in the eastern MoRB than in the western MoRB, in line with the annual precipitation climatology. Results show that 99th percentile events and annual station maximum precipitation events are becoming more frequent across the MoRB. Through analysis of 3-month extreme precipitation trends, areas in the eastern and southern MoRB are shown to have an increasing event frequency and intensity. Frequency shifts in the 99th percentile events, however, have occurred across the entire region. The increasing frequency of extreme events in the western MoRB represent a significant change for the hydroclimate of the region. Overall, the results from this work show that MORB extreme precipitation has increased in frequency and intensity during the 1950 – 2019 period.

Description: We present a hurricane risk assessment model that simulates N. Atlantic tropical cyclone (TC) tracks and intensity, conditioned on the early season large-scale climate state. The model, C3-HITS (for Cluster-based Climate Conditioned Hurricane Intensity and Track Simulator), extends a previous version of HITS (Nakamura et al., 2015). HITS is a nonparametric, spatial semi-Markov, stochastic model that generates TC tracks by conditionally simulating segments of randomly varying lengths from the TC tracks contained in NOAA’s Best Track Data version 2 data set. The distance to neighboring tracks, track direction, TC wind speed, and age are used as conditioning variables. C3-HITS adds conditioning on two early season, large-scale climate covariates to condition the track simulation: the NINO3.4 index, representing the eastern equatorial Pacific sea surface temperature (SST) departure from climatology, and MDR, representing tropical N. Atlantic SST departure from climatology in the N. Atlantic TC Main Development Region. A track clustering procedure (Nakamura et al., 2009) is used to identify track families and a Poisson regression model is used to model the probabilistic number of storms formed in each cluster, conditional on the two climate covariates. The HITS algorithm is then applied to evolve these tracks forward in time. The output of this two-step, climate conditioned simulator is compared with an unconditional HITS application to illustrate its prognostic efficacy in simulating tracks during the subsequent season. As in the HITS model, each track retains information on velocity and other attributes that can be used for predictive coastal risk modeling for the upcoming TC season.

Description: This paper is the first attempt to document a simple convection-tracking method based on the IMERG precipitation product to generate an IMERG-based Convection Tracking (IMERG-CT) dataset. Up to now, precipitation datasets have been Eulerian accumulations. Now with IMERG-CT, we can estimate total rainfall based on Lagrangian accumulations, which is a very important step in diagnosing cloud-precipitation process following the evolution of air masses. Convection-tracking algorithms have traditionally been developed on the basis of brightness temperature (Tb) from satellite infrared (IR) retrievals. However, vigorous rainfall can be produced by warm-topped systems in a moist environment; this situation cannot be captured by traditional IR-based tracking but is observed in IMERG-CT. Therefore, an advantage of IMERG-CT is its ability to include the previously missing information of shallow clouds that grow into convective storms, which provides us more-complete life cycle records of convective storms than traditional IR-based tracking does. This study also demonstrates the utility of IMERG-CT through investigating various properties of convective systems in terms of the evolution before and after peak precipitation rate and amount. For example, composite analysis reveals a link between evolution of precipitation and convective development: the signature of stratiform anvils remaining after the storm has produced the maximum rainfall, as average Tb stays almost constant for 5 h after the peak of precipitation. Our study highlights the importance of joint analysis of cloud and precipitation data in time sequence, which helps to elucidate the underlying dynamic processes producing tropical rainfall and its resultant effects on the atmospheric thermodynamics.

Description: During the DYNAMO (Dynamics of the MJO) field campaign, radiosonde launches were regularly conducted from three small islands/atolls (Malé, Gan, and Diego Garcia) as part of a large-scale sounding network. Comparison of island upsondes to nearby and near-contemporaneous dropsondes over the ocean provides evidence for the magnitude and scope of the islands’ influence on the surrounding atmosphere, and on the island upsonde profiles. The island’s impact on the upsonde data is most prominent in the lowest 200 m. Noting that the vertical gradients of temperature, moisture, and winds over the ocean are generally constant in the lowest 0.5 km of dropsondes, a simple procedure was constructed to adjust the upsonde profiles in the lowest few hundred meters to resemble the atmospheric structures over the open ocean. This procedure was applied to the soundings from the three islands mentioned above for the October to December 2011 period of DYNAMO. As a result of this procedure, the adjusted diurnal cycle amplitude of surface temperature is reduced five-fold, resembling that over the ocean, and low-level wind speeds are increased in ~90% of the island soundings. Examination of the impact of these sounding adjustments shows that dynamical and budget fields are primarily affected by adjustments to the wind field, while convective parameters are sensitive to the adjustments in thermodynamic fields. Although the impact of the adjustments is generally small (order of a few percent), intraseasonal wind regime changes result in some systematic variations in divergence and vertical motion over the sounding arrays.

Description: Drylands cover about half of the land surface in China and are highly sensitive to climate change. Understanding climate change and its impact drivers on dryland is essential for supporting dryland planning and sustainable development. Based on meteorological observations (period: 1960-2019), the aridity changes in drylands of China were evaluated using aridity index (AI), and the impact of various climatic factors (i.e., precipitation, P; sunshine duration, SSD; relative humidity, RH; maximum temperature, Tmax; minimum temperature, Tmin; wind speed, WS) on the aridity changes was decomposed and quantified. Results of trend analysis based on Sen’s slope estimator and Mann–Kendall test indicated that the aridity trends were very weak averaged over whole drylands in China during 1960-2019, but exhibited a significant wetting trend in hyper-arid and arid regions of drylands. AI was most sensitive to changes in water factors (i.e., P and RH), followed by SSD, Tmax and WS, but the sensitivity of AI to Tmin was very small and negligible. Interestingly, the dominant climatic driver to AI change varied in the four dryland subtypes. The significantly increased P dominated the increase in AI in the hyper-arid and arid regions. While the significantly reduced WS and the significantly increased Tmax contributed more to AI changes than the P in the semi-arid and dry subhumid regions of drylands. Previous studies emphasized the impact of precipitation and temperature on the global or regional dry-wet changes, however, the findings of this study suggested that beyond precipitation and temperature, the impact of wind speed on aridity changes of drylands in China should be given equal attention.

Description: Extreme heat is one of the most pressing climate risks in the United States, and is exacerbated by a warming climate and aging population. Much work in heat-health has focused only on temperature-based metrics, which do not fully measure the physiological impact of heat stress on the human body. The United States Climate Reference Network (USCRN) consists of 139 sites across the US, and includes meteorological parameters fully encompassing human tolerance to heat, including relative humidity, wind, and solar radiation. Hourly and 5-minute observations from USCRN are used to develop heat exposure products, including heat index (HI), apparent temperature (AT), and wet bulb globe temperature (WBGT). Validation of this product is conducted with nearby airport and mesonet stations, with reanalysis data used to fill in data gaps.Using these derived heat products, two separate analyses are conducted. The first is based on standardized anomalies, which place current heat state in the context of a long-term climate record. In the second study, heat events are classified by time spent at various levels of severity of conditions. There is no consensus as to what defines a heat event, so a comparison of absolute thresholds (i.e., ≥ 30.0°C, 35.0°C, 40.0°C) and relative thresholds (≥ 90th, 95th, 98th percentile) will be examined. The efficacy of the product set will be studied using an extreme heat case study in the southeast US. While no heat exposure metric is deemed superior, each has their own advantages and caveats, especially in the context of public communication.

Description: A newly developed precipitation phase metric is used to detect the impact of urbanization on the nature of precipitation at Toronto, Ontario, Canada, by contrasting the relative amounts of rain and snow. 162 years of observed precipitation data were analyzed to classify the nature of winter season precipitation for the Canadian city of Toronto. In addition shorter records were examined for nearby climate stations in less urbanized areas in and near Toronto. For Toronto, all winters from 1849 to 2010 as well as three climate normal periods (1961-1990, 1971-2000, 1981-2010) were thus categorized for the Toronto climate record. The results show that Toronto winters have become increasingly “rainy” across these time-periods in a statistically significant fashion, consistent with a warming climate. Toronto was compared to the other less urban sites to tease out the impacts of the urban heat island from larger scale warming. This yielded an estimate of 19 to 27% of the Toronto shift in precipitation type (snow to rain) that can be attributed to urbanization for coincident time periods. Other regions characterized by similar climates and urbanization with temperatures near the freezing point are likely to experience similar climatic changes expressed as a change in the phase of winter season precipitation.

Description: Characteristics of temperature and specific humidity inversions (TIs and SHIs, respectively), and their relationships in three of the latest global reanalyses, the European Centre for Medium-Range Weather Forecasts Interim Reanalysis (ERA-I), the Japanese 55-year Reanalysis (JRA-55) and the ERA-5, are assessed against in-situ radiosonde (RS) measurements from two expeditions over the Arctic Ocean. All reanalyses tend to detect much lower TI and SHI occurrences, together with much less common multiple TIs and SHIs per profile than RS data in summer 2008, winter 2015 and spring 2015. The reanalyses generally well depict the relationships among TI characteristics seen in RS data, except for the TIs below 400 m in summer, as well as above 1000 m in summer and winter. The depth is worst simulated by the reanalyses among the SHI characteristics, which may result from its sensitivity to the uncertainties in specific humidity in the reanalyses. The strongest TI per profile in RS data exhibits more robust dependency on surface conditions than the strongest SHI per profile, and the former is better presented by the reanalyses than the latter. Furthermore, all reanalyses have difficulties simulating the relationships between TIs and SHIs, together with the correlations between the simultaneous inversions. The accuracy and vertical resolution in the reanalyses are both important to properly capture occurrence and characteristics of the Arctic inversions. Generally, ERA-5 performs better than ERA-I and JRA-55 in depicting the relationships among the TIs. However, the representation of SHIs is more challenging than TIs in all reanalyses over the Arctic Ocean.

Description: The Hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) model is a state-of-the-science atmospheric dispersion model that is developed and maintained at the National Oceanic Atmospheric Administration’s (NOAA) Air Resources Laboratory (ARL). In the early 2000s, HYSPLIT served as the starting point for development of the Stochastic Time-Inverted Lagrangian Transport (STILT) model that emphasizes backward-in-time dispersion simulations to determine source regions of receptors. STILT continued its separate development and gained a wide user base. Since STILT was built on a now outdated version of HYSPLIT and lacks long-term institutional support to maintain the model, incorporating STILT features into HYSPLIT allows these features to stay up to date. This paper describes the STILT features incorporated into HYSPLIT, which include: a new vertical interpolation algorithm for WRF derived meteorological input files, a detailed algorithm for estimating boundary layer height, a new turbulence parameterization, a vertical Lagrangian timescale that varies in time and space, a complex dispersion algorithm, and two new convection schemes. An evaluation of these new features was performed using tracer release data from the Cross Appalachian Tracer Experiment and the Across North America Tracer Experiment. Results show the dispersion module from STILT, which takes up to double the amount of time to run, is less dispersive in the vertical and in better agreement with observations than the existing HYSPLIT option. The other new modeling features from STILT were not consistently statistically different than existing HYSPLIT options. Forward-time simulations from the new model were also compared against backward-time equivalents and found to be statistically comparable to one another.

Description: Understanding tropical cyclone wind speed decay during the post-landfall stage is critical for inland hazard preparation. This paper examines the spatial variation of wind speed decay of tropical cyclones over the continental United States. We find that tropical cyclones making landfall over the Gulf Coast decay faster within the first 24 hours after landfall than those making landfall over the Atlantic East Coast. The variation of the decay rate over the Gulf Coast remains larger than that over the Atlantic East Coast for tropical cyclones that had made landfall more than 24 hours prior. Besides an average weaker tropical cyclone landfall intensity, the near-parallel trajectory and the proximity of storms to the coastline also help to explain the slower post-landfall wind speed decay for Atlantic East Coast landfalling tropical cyclones. Tropical cyclones crossing the Florida peninsula only slowly weaken after landfall, with an average of less than 20% post-landfall wind speed drop while transiting the state. The existence of these spatial variations also brings into question the utility of a uniform wind decay model. While weak intensity decay over the Florida peninsula is well estimated by the uniform wind decay model, the error from the uniform wind decay model increases with tropical cyclones making direct landfall more parallel to the Atlantic East Coast. The underestimation of inland wind speed by the uniform wind decay model found over the western Gulf Coast brings attention to the role of land-air interactions in the decay of inland tropical cyclones.

Description: In order to better understand cloud-precipitation relationships, we extend the concept of cloud regimes (CRs) developed from two-dimensional joint histograms of cloud optical thickness and cloud top pressure from the Moderate Resolution Imaging Spectroradiometer (MODIS), to include precipitation information. Taking advantage of the high-resolution Integrated Multi-satellitE Retrievals for GPM (IMERG) precipitation dataset, we derive cloud-precipitation “hybrid” regimes by implementing a k-means clustering algorithm with advanced initialization and objective measures to determine the optimal number of clusters. By expressing the variability of precipitation rates within 1-degree grid cells as histograms and varying the relative weight of cloud and precipitation information in the clustering algorithm, we obtain several editions of hybrid cloud-precipitation regimes (CPRs), and examine their characteristics.In the deep tropics, when precipitation is weighted weakly, the cloud part centroids of the hybrid regimes resemble their counterparts of cloud-only regimes, but combined clustering tightens the cloud-precipitation relationship by decreasing each regime’s precipitation variability. As precipitation weight progressively increases, the shape of the cloud part centroids becomes blunter, while the precipitation part sharpens. When cloud and precipitation are weighted equally, the CPRs representing high clouds with intermediate to heavy precipitation exhibit distinct enough features in the precipitation parts of the centroids to allow us to project them onto the 30-min IMERG domain. Such a projection overcomes the temporal sparseness of MODIS cloud observations associated with substantial rainfall, suggesting great application potential for convection-focused studies where characterization of the diurnal cycle is essential.

Description: The surface roughness aerodynamic parameters z0 (roughness length) and d (zero-plane displacement height) are vital to the accuracy of the Monin–Obukhov similarity theory. Deriving improved urban canopy parameterization (UCP) schemes within the conventional framework remains mathematically challenging. The current study explores the potential of a machine-learning (ML) algorithm, a random forest (RF), as a complement to the traditional UCP schemes. Using large-eddy simulation and ensemble sampling, in combination with nonlinear least squares regression of the logarithmic-layer wind profiles, a dataset of approximately 4.5 × 103 samples is established for the aerodynamic parameters and the morphometric statistics, enabling the training of the ML model. While the prediction for d is not as good as the UCP after Kanda et al., the performance for z0 is notable. The RF algorithm also categorizes z0 and d with an exceptional performance score: the overall bell-shaped distributions are well predicted, and the ±0.5σ category (i.e., the 38% percentile) is competently captured (37.8% for z0 and 36.5% for d). Among the morphometric features, the mean and maximum building heights (Have and Hmax, respectively) are found to be of predominant influence on the prediction of z0 and d. A perhaps counterintuitive result is the considerably less striking importance of the building-height variability. Possible reasons are discussed. The feature importance scores could be useful for identifying the contributing factors to the surface aerodynamic characteristics. The results may shed some light on the development of ML-based UCP for mesoscale modeling.

Description: Recently strong sound wave was proposed to enhance precipitation. The theoretical basis of this proposal has not been effectively studied either experimentally or theoretically. Based on the microscopic parameters of atmospheric cloud physics, this paper solved the complex nonlinear differential equation to show the movement characteristics of cloud droplets under the action of sound waves. The motion process of individual cloud droplet in a cloud layer in the acoustic field is discussed as well as the relative motion between two cloud droplets. The effects of different particle sizes and sound field characteristics on particle motion and collision are studied to analyze the dynamic effects of thunder-level sound waves on cloud droplets. The amplitude of velocity variation has positive correlation with Sound Pressure Level (SPL) and negative correlation with the frequency of the surrounding sound field. Under the action of low-frequency sound waves with sufficient intensity, individual cloud droplets could be forced to oscillate significantly. The droplet smaller than 40μm can be easily driven by sound waves of 50 Hz and 123.4 dB. The calculation of the collision process of two droplets reveals that the disorder of motion for polydisperse droplets is intensified, resulting in the broadening of the collision time range and spatial range. When the acoustic frequency is less than 100Hz (@ 123.4dB) or the Sound Pressure Level (SPL) is greater than 117.4dB (@ 50Hz), the sound wave can affect the collision of cloud droplets significantly. This study provides theoretical perspective of acoustic effect to the microphysics of atmospheric clouds.

Description: Fine-scale characteristics of summer precipitation over Cang Mountain, a long and narrow mountain with a quasi-north-south orientation in Southwest China, are studied based on station and radar data. Three kinds of rainfall processes are classified according to the initial stations of regional rainfall events (RREs) by utilizing minute-scale rain gauge data. RREs initiating in the western part of Cang Mountain exhibit eastward evolution and tend to reach their maximum rainfall intensity on the mountaintop. The results indicate differences in the precipitation evolution characteristics between short-duration (1–3 h) and long-duration (at least 6 h) events. Short-duration events begin farther from the mountaintop and then propagate eastward, while long-duration events remain longer around the mountaintop. RREs that initiate from the eastern part of Cang Mountain display westward propagation and frequently reach their maximum rainfall intensity over the eastern slope of the mountain. Among them, short-duration events tend to propagate farther west of Cang Mountain at high speeds, but the westward evolution of long-duration events is mainly confined to the eastern part of Cang Mountain. For mountaintop-originated RREs, precipitation quickly reaches its maximum intensity after it starts and then continues for a long time around the mountaintop during the period from late afternoon to early morning. These findings provide references for the fine-scale prediction of precipitation evolution in small-scale mountainous areas.

Description: Glaciogenic cloud seeding has long been practiced as a way to increase water availability in arid regions, such as the interior western United States. Many seeding programs in this region target cold-season orographic clouds with ground-based silver iodide generators. Here, the “seedability” (defined as the fraction of time that conditions are suitable for ground-based seeding) is evaluated in this region from 10 years of hourly output from a regional climate model with a horizontal resolution of 4 km. Seedability criteria are based on temperature, presence of supercooled liquid water, and Froude number, which is computed here as a continuous field relative to the local terrain. The model’s supercooled liquid water compares reasonably well to microwave radiometer observations. Seedability peaks at 20%–30% for many mountain ranges in the cold season, with the best locations just upwind of crests, over the highest terrain in Colorado and Wyoming, as well as over ranges in the northwest interior. Mountains farther south are less frequently seedable, because of warmer conditions, but when they are, cloud supercooled liquid water content tends to be relatively high. This analysis is extended into a future climate, anticipated for later this century, with a mean temperature 2.0 K warmer than the historical climate. Seedability generally will be lower in this future warmer climate, especially in the most seedable areas, but, when seedable, clouds tend to contain slightly more supercooled liquid water.

Description: Multiple configurations of the Geophysical Fluid Dynamics Laboratory vortex tracker are tested to determine a setup that produces the best representation of a model forecast tropical cyclone center fix for the purpose of providing track guidance with the highest degree of accuracy and availability. Details of the tracking algorithms are provided, including descriptions of both the Barnes analysis used for center-fixing most variables and a separate scheme used for center-fixing wind circulation. The tracker is tested by running multiple configurations on all storms from the 2015-2017 hurricane seasons in the Atlantic and eastern Pacific Basins using forecasts from two operational National Weather Service models, the Global Forecast System (GFS) and the Hurricane Weather Research and Forecast (HWRF) model. A configuration that tracks only 850 mb geopotential height has the smallest forecast track errors of any configuration based on an individual parameter. However, a configuration composed of the mean of eleven parameters outperforms any of the configurations that are based on individual parameters. Configurations composed of subsets of the eleven parameters and including both mass and momentum variables provide results comparable to or better than the full 11-parameter configuration. In particular, a subset configuration with thickness variables excluded generally outperforms the 11-parameter mean, while one composed of variables from only the 850 mb and near-surface layers performs nearly as well as the 11-parameter mean. Tracker configurations composed of multiple variables are more reliable in providing guidance through the end of a forecast period than are tracker configurations based on individual parameters.

Description: The outdoor events of the 2022 Winter Olympics and Paralympics will be held in the mountain areas of Beijing–Zhangjiakou, North China, where there is a complete reliance on artificial snow production owing to the dry and cold weather conditions. To assess how favorable the meteorological conditions are to snowmaking at the mountain venues, we reconstructed the daily wet-bulb temperature by adopting the thin-plate smoothing spline function method, and then we assessed the potential number of snowmaking days at eight weather stations (928–2098 m MSL) from October to the next April (i.e., the ski season) during the period 1978–2017. Results showed that artificial snow production would have been possible on 121 (±14) to 171 (±12) days on average at the stations with the increases of altitude, and the number of days decreased at rates of 4.3–5.1 days decade−1 across four decades of the study period. The cause of the decrease was the warming trend, which affected the number of days in low-altitude sites simultaneously, but the reduction was delayed with increased elevation. At monthly scale, the number of snowmaking days was robust in wintertime but reduced in other months of the ski season, particularly in March in more recent subperiods at high-altitude stations, which was determined by the increase in high values of daily mean wet-bulb temperature. Further improvements in assessing snowmaking conditions require detailed microclimatic studies to reduce the uncertainties caused by meteorological conditions as well as combination with model-based methods to determine potential future changes.

Description: Pesticide applications during surface inversions can lead to spray drift causing severe damage up to several kilometers off-target. Current regulations in Australia prohibit spray application of certain agricultural chemicals when hazardous surface inversions exist. This severely limits spray opportunities. Surface inversions can be classified as weakly or strongly stable. In the weakly stable case, moderate to strong turbulent mixing is not supportive of long-distance concentrated drift. In the very stable case, weak turbulent mixing can support the transport of high concentrations of fine material over long distances. A review of the literature and our analyses indicate that if the turbulence, as measured by the standard deviation of the vertical wind speed σw, is greater than about 0.2 m s−1 then turbulence-driven mixing and dispersion is moderate to strong and conversely if σw is less than about 0.2 m s−1 then turbulence-driven mixing and dispersion is weaker (an order of magnitude). The concept of maximum downward heat flux as a natural division between the regimes is applied within Monin–Obukhov stability theory, and it is shown that the observed mean σw of 0.2 m s−1 aligns with the ridge line of maximum heat flux in stable conditions. The level of turbulence in the weakly stable regime is comparable to the turbulence typically observed in near-neutral conditions that are recommended under current guidelines as suitable for spraying and is therefore seen as an acceptable prerequisite to avoid nondispersive spraying conditions.

Description: Drought is a common natural disaster that greatly affects the crop yield and water supply in China. However, the spatiotemporal characteristics of drought in China are not well understood. This paper explores the spatial and temporal distributions of droughts in China over the past 40 years using multiscale standardized precipitation evapotranspiration index (SPEI) values calculated by monthly precipitation and temperature data from 612 meteorological stations in China from 1980 to 2019 and combines the space–time cube (STC), Mann–Kendall test, emerging spatiotemporal hot-spot analysis, spatiotemporal clustering, and local outliers for the analysis. The results were as follows: 1) the drought frequency and STC show that there is a significant difference in the spatiotemporal distribution of drought in China, with the most severe drought in Northwest China, followed by the western part of Southwest China and the northern part of North China. 2) The emerging spatiotemporal hot-spot analysis of SPEI6 over the past 40 years reveals two cold spots in subregion 4, indicating that future droughts in the region will be more severe. 3) A local outlier analysis of the multiscale SPEI yields a low–low outlier in western North China, indicating relatively more severe year-round drought in this area than in other areas. The low–high outlier in central China indicates that this region was not dry in the past and that drought will become more severe in this region in the future.

Description: It is argued that the occurrence of cold events decreases under the background of global warming. However, from the mid-1990s to the early 2010s, northern China experienced a period of increasing occurrence of low temperature extremes (LTE). Factors responsible for this increase of LTE are investigated in this analysis. The results show that the interdecadal variation of the winter mean temperature over mid- and high-latitude Eurasia acts as an important thermal background. It is characterized by two dominant modes, the “consistent cooling” pattern and the “warm high-latitude Eurasia and cold midlatitude Eurasia” pattern, from the mid-1990s to the early 2010s. The two patterns jointly provide a cooling background for the increase of LTE in northern China. Meanwhile, though the interdecadal variation of the Arctic Oscillation (AO), Ural blocking (UB), and Siberian high (SH) are all highly correlated with the occurrence of LTE in northern China, the AO is found to play a dominant role. On one hand, the AO directly affects the occurrence of LTE because of its dynamic structure; on the other hand, it takes an indirect effect by affecting the intensity of UB and SH. Further analyses show that the winter temperature in mid- and high-latitude Eurasia and the AO are independent factors that influence the increase of LTE in northern China from the mid-1990s to the early 2010s.

Description: In this study, the total days, mean duration, and intensity of extreme hot events over southern China during the 1971–2020 warm seasons are analyzed, based on daily maximum and minimum temperatures, by comparing the newly proposed independent hot day (IHD), independent warm night (IWN), and compound extreme (CMPD, i.e., the continuous occurrences of hot days and hot nights) to the traditionally defined hot day and warm night. Relationships between the hot extremes and urbanization are explored with 1-km resolution population density data. Results show obvious differences in the spatial distributions between IHD, IWN, and CMPD over southern China. Positive correlations of 0.43, 0.41 and 0.37 are found between the population density and the total days, mean duration and mean intensity of CMPD, respectively, which are qualitatively similar to those using the traditional hot days and warm nights. In contrast, negative correlations between the IHD and IWN indices and the population density are found, as those indices are more apparent over rural areas. Moreover, total days, mean duration and mean intensity of CMPD increase significantly with trends of about 103% decade-1, 21% decade-1 and 38% decade-1, respectively, during the rapid urbanization period from mid-1990s to 2020, which are about 4.9, 2.1 and 2.4 times of their counterparts from 1970 to mid-1990s, while less significant and smaller differences between the two eras are found in IHD and IWN. These results will provide a new scientific basis for evaluating climate models of hot extremes in southern China and have important implications for the other urbanized regions as well.

Description: Recent climatic studies for the dominantly rain-fed agricultural U.S. Corn Belt (CB) suggest an influence of land use/land cover (LULC) spatial differences on convective development, set within the larger-scale (synoptic) atmospheric conditions of pressure, winds, and vertical motion. However, the potential role of soil moisture (SM) in the LULC association with atmospheric humidity, horizontal wind and convective precipitation (CVP) has received more limited attention, mostly as modeling studies or empirical analyses for regions non-analogous to the CB. Accordingly, we determine the categorical associations between SM and the near-surface atmospheric humidity (q), with 850-hPa horizontal wind (V850) at four representative CB locations for the nine warm-seasons of 2011-2019. Recurring configurations of joint SM-q-V850 conducive to CVP are then identified and stratified into three phenologically distinct sub-seasons (early, middle, late).We show that the stations show some statistical similarity in their SM-CVP relationships. Corn Belt CVP occurs preferentially with high humidity and southerly winds sometimes comprising a low-level jet (LLJ), particularly on early-season days having low SM and late-season days having high SM. Additionally, mid-season CVP days having weaker V850 (i.e., non-LLJ) tend to be associated with medium SM values and high humidity. Conversely, late-season CVP days are frequently characterized by high values of both SM and humidity. These empirical results are likely explained by the inferred sensible and latent heat fluxes varying according to SM content and LULC type. They provide a basis for future mesoscale modeling studies of Corn Belt SM and CVP interactions to test the hypothesized physical processes.

Description: Extratropical cyclones are the primary driver of sensible weather conditions across the mid-latitudes of North America, often generating various types of precipitation, gusty non-convective winds, and severe convective storms throughout portions of the annual cycle. Given ongoing modifications of the zonal atmospheric thermal gradient due to anthropogenic forcing, analyzing the historical characteristics of these systems presents an important research question. Using the North American Regional Reanalysis, boreal cool-season (October–April) extratropical cyclones for the period 1979–2019 were identified, tracked, and classified based on their genesis location. Additionally, bomb cyclones—extratropical cyclones that recorded a latitude normalized pressure fall of 24 hPa in 24-hr—were identified and stratified for additional analysis. Cyclone lifespan across the domain exhibits a log-linear relationship, with 99% of all cyclones tracked lasting less than 8 days. On average, ≈ 270 cyclones were tracked across the analysis domain per year, with an average of ≈ 18 year−1 being classified as bomb cyclones. The average number of cyclones in the analysis domain has decreased in the last 20 years from 290 year−1 during the period 1979–1999 to 250 year−1 during the period 2000–2019. Spatially, decreasing trends in the frequency of cyclone track counts were noted across a majority of the analysis domain, with the most significant decreases found in Canada’s Northwest Territories, Colorado, and east of the Graah mountain range. No significant interannual or spatial trends were noted with bomb cyclone frequency.

Description: Based on daily meteorological observation data in South China (SC) from 1967 to 2018, the spatiotemporal characteristics of the precipitation in SC over the past 52 years were studied. Only 8% of the stations showed a significant increase in annual rainfall, and there was no significant negative trend at any weather stations at a confidence level of 90%. Monthly rainfall showed the most significant decreasing and increasing trends in April and November, respectively. During the entire flooding season from April to September, the monthly rainfall at the weather stations in the coastal areas showed almost no significant change. The annual rainfall gradually decreased toward the inland area with the central and coastal areas of Guangdong Province as the high-value rainfall center. By using the empirical orthogonal function decomposition method, it was found that the two main monthly rainfall modes had strong annual signals. The first modal spatial distribution was basically consistent with the average annual rainfall distribution. Based on the environmental background analysis, it was found that during the flooding season, the main water vapor to SC was transported by the East Asian summer monsoon and the Indian summer monsoon. In late autumn and winter, the prevailing wind from northeastern China could not bring much water vapor to SC and led to little precipitation in these two seasons. The spatial distribution of precipitation in SC during summer was more consistent with the moisture flux divergence distribution of the bottom layer from 925 hPa to 1000 hPa, rather than the layer from 700 hPa to 1000 hPa.

Description: We analyze how winter thaw events (TE; T〉0°C) are changing on the summit of Mount Washington, New Hampshire using three metrics: the number of TE, number of thaw hours, and number of thaw degree-hours for temperature and dewpoint for winters from 1935-36 to 2019-20. The impact of temperature-only-TE and dewpoint-TE on snow depth are compared to quantify the different impacts of sensible-only and sensible-and-latent heating, respectively. Results reveal that temperature- and dewpoint-TE for all metrics increased at a statistically significant rate (p

Description: Our study examines the horizontal variation of the nocturnal surface air temperature by analyzing measurements from four contrasting networks of stations with generally modest topography. The horizontal extent of the networks ranges from 1 to 23 km. For each network, we investigate the general relationship of the horizontal variation of temperature to the wind speed, wind direction, near-surface stratification, and turbulence. As an example, the horizontal variation of temperature generally increases with increasing stratification and decreases with increasing wind speed. However, quantitative details vary significantly between the networks. Needed changes of the observational strategy are discussed.

Description: United States tornado records form the basis for a variety of meteorological, climatological and disaster-risk analyses, but how reliable are they in light of changing standards for rating, as with the 2007 transition of Fujita (F) to Enhanced Fujita (EF) damage scales? To what extent are recorded tornado metrics subject to such influences that may be nonmeteorological in nature? While addressing these questions with utmost thoroughness is too large of a task for any one study, and may not be possible given the many variables and uncertainties involved, some variables that are recorded in large samples are ripe for new examination. We assess basic tornado-path characteristics—damage rating, length, width, and occurrence time, as well as some combined and derived measures—for a 24-yr period of constant path-width recording standard that also coincides with National Weather Service modernization and the WSR-88D deployment era. The middle of that period (in both time and approximate tornado counts) crosses the official switch from F to EF. At least minor shifts in all assessed path variables are associated directly with that change, contrary to the intent of EF implementation. Major and essentially stepwise expansion of tornadic path widths occurred immediately upon EF usage, and widths have expanded still further within the EF era. We also document lesser increases in path lengths, and in tornadoes rated at least EF1 compared to EF0. These apparently secular changes in the tornado data can impact research dependent on bulk tornado-path characteristics and damage-assessment results.

Description: Sub-daily precipitation extremes are high-impact events that can result in flash floods, sewer system overload, or landslides. Several studies have reported an intensification of projected short-duration extreme rainfall in a warmer future climate. Traditionally, regional climate models (RCMs) are run at a coarse resolution using deep-convection parameterization for these extreme events. As computational resources are continuously ramping up, these models are run at convection-permitting resolution, thereby partly resolving the small-scale precipitation events explicitly. To date, a comprehensive evaluation of convection-permitting models is still missing. We propose an evaluation strategy for simulated sub-daily rainfall extremes that summarizes the overall RCM performance. More specifically, the following metrics are addressed: the seasonal/diurnal cycle, temperature and humidity dependency, temporal scaling and spatio-temporal clustering. The aim of this paper is: (i) to provide a statistical modeling framework for some of the metrics, based on extreme value analysis, (ii) to apply the evaluation metrics to a micro-ensemble of convection-permitting RCM simulations over Belgium, against high-frequency observations, and (iii) to investigate the added value of convection-permitting scales with respect to coarser 12-km resolution. We find that convection-permitting models improved precipitation extremes on shorter time scales (i.e, hourly or two-hourly), but not on 6h-24h time scales. Some metrics such as the diurnal cycle or the Clausius-Clapeyron rate are improved by convection-permitting models, whereas the seasonal cycle appears robust across spatial scales. On the other hand, the spatial dependence is poorly represented at both convection-permitting scales and coarser scales. Our framework provides perspectives for improving high-resolution atmospheric numerical modeling and datasets for hydrological applications.

Description: This paper showed the frequency of local-scale heavy winter snowfall in Hokkaido, Japan, its historical change, and its response to global warming using self-organizing map (SOM) of synoptic-scale sea-level pressure anomaly. Heavy snowfall days were here defined as days when the snowfall exceeded 10 mm in water equivalent. It was shown that the SOMs can be grouped into three categories for heavy snowfall days: 1) a passage of extratropical cyclones to the south of Hokkaido, 2) a pressure pattern between the Siberian high and the Aleutian low, and 3) a low-pressure anomaly just to the east of Hokkaido. Groups 1 and 2 were associated with heavy snowfall in Hiroo (located in southeastern Hokkaido) and in Iwamizawa (western Hokkaido), respectively, and heavy snowfall in Sapporo (western Hokkaido) was related to Group 3. The large-ensemble historical simulation reproduced the observed increasing trend in Group 2 and future projection revealed that Group 2 was related to a negative phase of the Western Pacific pattern and the frequency of this group would increase in the future. Heavy snowfall days associated with SOM Group 2 would also increase due to the increase in water vapor and preferable weather patterns in global warming climate, in contrast to the decrease of heavy snowfall days in other sites associated with SOM Group 1.

Description: The studies related to the coherent structures in the atmosphere, using Doppler wind lidar observations, so far relied on the manual detection and classification of the structures in the lidar images, making this process time-consuming. We developed an automated classification based on texture analysis parameters and the quadratic discriminant analysis algorithm for the detection of medium-to-large fluctuations and coherent structures recorded by single Doppler wind lidar quasi-horizontal scans. The algorithm classified a training dataset of 150 cases into four types of patterns, namely streaks (narrow stripes), rolls (wide stripes), thermals (enclosed areas) and “others” (impossible to classify), with 91% accuracy. Subsequently, we applied the trained algorithm to a dataset of 4577 lidar scans recorded in Paris, atop a 75 m tower for a 2-month period (September-October 2014). The current study assesses the quality of the classification by examining the physical properties of the classified cases. The results show a realistic classification of the data: with rolls and thermals cases mostly classified concurrently with a well-developed atmospheric boundary layer and the streaks cases associated with nocturnal low-level jets (nllj) events. Furthermore, rolls and streaks cases were mostly observed under moderate or high wind conditions. The detailed analysis of a four-day period reveals the transition between the types. The analysis of the space spectra in the direction transverse to the mean wind, during these four days, revealed streaks spacing of 200 to 400 m, and rolls sizes, as observed in the lower level of the mixed layer, of approximately 1 km.

Description: Storm surge is a weather hazard that can generate dangerous flooding and is not fully understood in terms of timing and atmospheric forcing. Using observations along the Northeast United States, surge is sorted based on duration and intensity to reveal distinct time-evolving behavior. Long-duration surge events slowly recede, while strong, short-duration events often involve negative surge in quick succession after the maximum. Using Lagrangian track information, the tropical and extratropical cyclones and atmospheric blocks that generate the surge events are identified. There is a linear correlation between surge duration and surge maximum, and the relationship is stronger for surge caused by extratropical cyclones as compared to those events caused by tropical cyclones. For the extremes based on duration, the shortest-duration strong surge events are caused by tropical cyclones, while the longest-duration events are most often caused by extratropical cyclones. At least half of long-duration surge events involve anomalously strong atmospheric blocking poleward of the cyclone, while strong, short-duration events are most often caused by cyclones in the absence of blocking. The dynamical influence of the blocks leads to slow-moving cyclones that take meandering paths. In contrast, for strong, short-duration events, cyclones travel faster and take a more meridional path. These unique dynamical scenarios provide better insight for interpreting the threat of surge in medium-range forecasts.

Description: Canada experiences a relatively large number of tornadoes, which can cause a significant amount of damage and fatalities. In the present study, a preferred prediction model for the spatially varying tornado occurrence rate is developed for Canada. The development takes into account the most commonly used spatial stochastic models and the underreporting due to low population density. It incorporates the annual average cloud-to-ground lightning flash (ACGLF) density and annual average thunderstorm days (ATD) as covariates in the prediction model. The model parameters estimation is carried out by using both the maximum likelihood method and the Bayesian inference.The analysis results indicate that the negative binomial model is preferable to the zero-inflated Poisson model and the Poison model. The results show that the tornado occurrence in Canada is associated with large overdispersion. Also, the statistical analysis indicates that the prediction model for the tornado occurrence rate developed based on Bayesian inference is relatively insensitive to the assumed “non-informative” prior distributions. A prediction model is suggested for the spatially varying tornado occurrence rate based on the negative binomial model with the ACGLF density and ATD as covariates.

Description: Surface latent and sensible heat fluxes are important for extratropical cyclone evolution and intensification. Because extratropical cyclone genesis often occurs at low-latitude, CYGNSS surface latent and sensible heat flux retrievals are composited to provide a mean picture of their spatial distribution in low-latitude oceanic extratropical cyclones. CYGNSS heat fluxes are not affected by heavy precipitation and offer observations of storms with frequent revisit times. Consistent with prior results obtained for cyclones in the Gulf Stream region, the fluxes are strongest in the wake of the cold fronts, and weakest to negative in the warm sector in advance of the cold fronts. As cyclone strength increases, or mean precipitable water decreases, the maximum in surface heat fluxes increases while the minimum decreases. This impacts the changes in fluxes during cyclone intensification: the post-cold frontal surface heat flux maximum increases due to the increase in near surface winds. During cyclone dissipation, the fluxes in this sector decrease, due to the decrease in winds and in temperature and humidity contrast. The warm sector minimum decreases throughout the entire cyclone lifetime and is mostly driven by sea-air temperature and humidity contrast changes. However, during cyclone dissipation, the surface heat fluxes increase along the cold front in a narrow band to the east, independently from changes in the cyclone characteristics. This suggests that, during cyclone dissipation, energy transfers from the ocean to the atmosphere are linked to frontal in addition to synoptic-scale processes.

Description: Marine stratocumulus clouds are intimately coupled to the turbulence in the boundary layer and drizzle is known to be ubiquitous within them. Six years of data collected at the Atmospheric Radiation Measurement (ARM)’s Eastern North Atlantic site are utilized to characterize turbulence in the marine boundary layer and air motions below stratocumulus clouds. Profiles of variance of vertical velocity binned by wind direction (wdir) yielded that the boundary layer measurements are affected by the island when the wdir is between 90° and 310° (measured clockwise from North where air is coming from). Data collected during the marine conditions (wdir310) showed that the variance of vertical velocity was higher during the winter months than during the summer months due to higher cloudiness, wind speeds, and surface fluxes. During marine conditions the variance of vertical velocity and cloud fraction exhibited a distinct diurnal cycle with higher values during the nighttime than during the daytime. Detailed analysis of 32 cases of drizzling marine stratocumulus clouds showed that for a similar amount of radiative cooling at the cloud top, within the sub-cloud layer 1) drizzle increasingly falls within downdrafts with increasing rain rates, 2) the strength of the downdrafts increases with increasing rain rates, and 3) the correlation between vertical air motion and rain rate is highest in the middle of the sub-cloud layer. The results presented herein have implications for climatological and model evaluation studies conducted at the ENA site, along with efforts of accurately representing drizzle-turbulence interactions in a range of atmospheric models.

Description: Droughts are widespread disasters worldwide and are concurrently influenced by multiple large-scale climate signals. This is particularly true over Japan, where drought has strong heterogeneity due to multiple factors such as monsoon, topography, and ocean circulations. Regional heterogeneity poses challenges for drought prediction and management. To overcome this difficulty, this study provides a comprehensive analysis of teleconnection between climate signals and homogeneous drought zones over Japan. First, droughts are characterized by simulated soil moisture from land surface model during 1958-2012. The Mclust toolkit, distinct empirical orthogonal function, and wavelet coherence analysis are used, respectively, to investigate the homogeneous drought zone, principal component of each homogeneous zone, and teleconnection between climate signals and drought. Results indicate that nine homogeneous drought zones with different characteristics are defined and quantified. Among these nine zones, zone-1 is dominated by extreme drought events. Zone-2 and zone-6 are typical representatives of spring droughts, while zone-7 is wet for most of the period. The Hokkaido region is divided into wetter zone-4 and drier zone-9. Zone-3, zone-5 and zone-8 are distinguished by the topography. The analyses also reveal almost nine zones have a high level of homogeneity, with more than 60% explained variance. Also, these nine zones are dominated by different large-scale climate signals: the Arctic Oscillation has the strongest impact on zone-1, zone-7, and zone-8; the influence of the North Atlantic Oscillation on zone-3, zone-4, and zone-6 is significant; zone-2 and zone-9 are both dominated by the Pacific Decadal Oscillation; El Niño-Southern Oscillation dominates zone-5. The results will be valuable for drought management and drought prevention.

Description: Accurate simulation of planetary boundary layer height (PBLH) is key to greenhouse gas emission estimation, air quality prediction, and weather forecasting. This paper describes an extensive performance assessment of several Weather Research and Forecasting (WRF) Model configurations in which novel observations from ceilometers, surface stations, and a flux tower were used to study their ability to reproduce the PBLH and the impact that the urban heat island (UHI) has on the modeled PBLHs in the greater Washington, D.C., area. In addition, CO2 measurements at two urban towers were compared with tracer transport simulations. The ensemble of models used four PBL parameterizations, two sources of initial and boundary conditions, and one configuration including the building energy parameterization urban canopy model. Results have shown low biases over the whole domain and period for wind speed, wind direction, and temperature, with no drastic differences between meteorological drivers. We find that PBLH errors are mostly positively correlated with sensible heat flux errors and that modeled positive UHI intensities are associated with deeper modeled PBLs over the urban areas. In addition, we find that modeled PBLHs are typically biased low during nighttime for most of the configurations with the exception of those using the MYNN parameterization, and these biases directly translate to tracer biases. Overall, the configurations using the MYNN scheme performed the best, reproducing the PBLH and CO2 molar fractions reasonably well during all hours and thus opening the door to future nighttime inverse modeling.

Description: Tropical cyclones (TCs) rank among the most costly natural disasters in the United States, and accurate forecasts of track and intensity are critical for emergency response. Intensity guidance has improved steadily but slowly, as processes which drive intensity change are not fully understood. Because most TCs develop far from land-based observing networks, geostationary satellite imagery is critical to monitor these storms. However, these complex data can be challenging to analyze in real time, and off-the-shelf machine learning algorithms have limited applicability on this front due to their “black box” structure. This study presents analytic tools that quantify convective structure patterns in infrared satellite imagery for over-ocean TCs, yielding lower-dimensional but rich representations that support analysis and visualization of how these patterns evolve during rapid intensity change. The proposed ORB feature suite targets the global Organization, Radial structure, and Bulk morphology of TCs. By combining ORB and empirical orthogonal functions, we arrive at an interpretable and rich representation of convective structure patterns that serve as inputs to machine learning methods. This study uses the logistic lasso, a penalized generalized linear model, to relate predictors to rapid intensity change. Using ORB alone, binary classifiers identifying the presence (versus absence) of such intensity change events can achieve accuracy comparable to classifiers using environmental predictors alone, with a combined predictor set improving classification accuracy in some settings. More complex nonlinear machine learning methods did not perform better than the linear logistic lasso model for current data.

Description: Realistically representing the land–atmosphere interactions during persistent cold-air pools (PCAPs) is critical in simulating the strength of PCAPs, where uncertainties in simulating the PCAP strength will impact the ability to model the poor air quality. To quantify the model performance for land–atmosphere exchange, measurements of surface turbulent and radiative energy fluxes during two PCAPs, one weak and one strong, in Utah were compared with simulations from the Weather Research and Forecasting (WRF) Model. The results show that the WRF Model simulated the surface energy fluxes well in the weak PCAP case and that the performance degraded in the strong PCAP case. The significantly overestimated surface sensible heat flux H and latent heat flux (LE) in the strong PCAP were related, in part, to the overestimated net radiation and soil moisture and unsuitable turbulence parameterizations. The simulation using the Mellor–Yamada–Nakanishi–Niino planetary boundary layer scheme produced the least bias in both net radiation and surface turbulent fluxes for the strong PCAP case, which is expected because of the local higher-order (2.5) turbulence closure scheme. The surface exchange coefficient (CH), a crucial variable used to calculate H, was overall overestimated by the WRF Model. The underestimation of the nondimensional vertical temperature gradient in the Monin–Obukhov stability function was responsible for the overestimated CH, where the stability functions deviate significantly from expected values from observations for the stable atmospheric boundary layer. Our study highlights the need to improve the flux–profile parameterizations under stable conditions over complex terrain by including impacts due to mountainous terrain, such as surface radiative flux divergence and the diurnal mountain wind system.

Description: This study analyzes the microphysics and precipitation pattern of Hurricanes Harvey (2017) and Florence (2018) in both the eyewall and outer rainband regions. From the retrievals by a satellite red–green–blue scheme, the outer rainbands show a strong convective structure while the inner eyewall has less convective vigor (i.e., weaker upper-level reflectivities and electrification), which may be related to stronger vertical wind shear that hinders fast vertical motions. The WSR-88D column-vertical profiles further confirm that the outer rainband clouds have strong vertical motion and large ice-phase hydrometeor formation aloft, which correlates well with 3D Lightning Mapping Array source counts in height and time. From the results from this study, it is determined that the inner eyewall region is dominated by warm rain, whereas the external rainband region contains intense mixed-phase precipitation. External rainbands are defined here as those that reside outside of the main hurricane circulation, associated with surface tropical storm wind speeds. The synergy of satellite and radar dual-polarization parameters is instrumental in distinguishing between the key microphysical features of intense convective rainbands and the warm-rain-dominated eyewall regions within the hurricanes. Substantial amounts of ice aloft and intense updrafts in the external rainbands are indicative of heavy surface precipitation, which can have important implications for severe weather warnings and quantitative precipitation forecasts. The novel part of this study is to combine ground-based radar measurement with satellite observations to study hurricane microphysical structure from surface to cloud top so as to fill in the gaps between the two observational techniques.

Description: The objective of this study is to propose and evaluate a set of modifications to enhance a machine-learning-based method for forecasting day-ahead solar irradiation. To assess the proposed modifications, they were implemented in an initial forecast method, and their effectiveness was analyzed using two years of data on a national scale in Japan. In addition, the accuracy of the modified method was compared with one of the forecast methods for solar irradiation used by the Japan Meteorological Agency (JMA), namely, the mesoscale model (MSM). Such forecasts were made publicly available only recently, which makes this study one of the first ones to compare them with machine-learning-based forecasts. The annual root-mean-square error (RMSE) of local forecasts of the JMA-MSM varied from 0.1 to 0.14 kW h m−2; the regional equivalent varied from 0.062 to 0.091 kW h m−2. In comparison with these results, the modified model achieved an average RMSE reduction of 7.5% on the local scale and 16% on the regional scale. The modified model also had a skill score that was 23% higher than that of the JMA model. Furthermore, the performance of the JMA model had strong spatial and seasonal dependencies, which were reduced in the machine-learning-based forecasts. The results show that the proposed modifications are effective in reducing large forecasts errors, but they cannot compensate for situations in which the input data used to make the forecasts are highly inaccurate.

Description: The city of Jeddah, Saudi Arabia, is characterized by a hot and arid desert climate. On occasion, however, extreme precipitation events have led to flooding that caused extensive damage to human life and infrastructure. This study investigates the effect of incorporating an urban canopy model and urban land cover when simulating severe weather events over Jeddah using the Weather Research and Forecasting (WRF) Model at a convective-permitting scale (1.5-km resolution). Two experiments were conducted for 10 heavy rainfall events associated with the dominant large-scale patterns favoring convection over Jeddah: (i) an “urban” experiment that included the urban canopy model and modern-day land cover and (ii) a “desert” experiment that replaced the city area with its presettlement, natural land cover. The results suggest that urbanization plays an important role in modifying rainfall around city area. The urban experiment enhances the amount of rainfall by 26% on average over the Jeddah city area relative to the desert experiment in these extreme events. The changes in model-simulated precipitation are primarily tied to a nocturnal heat-island effect that modifies the planetary boundary layer and atmospheric instability of the convective events.

Description: Heavy rainfall and strong wind are the two main sources of disasters that are caused by tropical cyclones (TCs), and typhoons with different characteristics may induce different agricultural losses. Traditionally, the classification of typhoon intensity has not considered the amount of rainfall. Here, we propose a novel approach to calculate the typhoon type index (TTI). A positive TTI represents a “wind type” typhoon, where the overall damage in a certain area from TCs is dominated by strong wind. On the other hand, a negative TTI represents a “rain type” typhoon, where the overall damage in a certain area from TCs is dominated by heavy rainfall. From the TTI, the vulnerability of crop losses from different types of typhoons can be compared and explored. For example, Typhoon Kalmaegi (2008) was classified as a rain-type typhoon (TTI = −1.22). The most affected crops were oriental melons and leafy vegetables. On the contrary, Typhoon Soudelor (2015) was classified as a significant wind-type typhoon in most of Taiwan (TTI = 1.83), and the damaged crops were mainly bananas, bamboo shoots, pomelos, and other crops that are easily blown off by strong winds. Through the method that is proposed in this study, we can understand the characteristics of each typhoon that deviate from the general situation and explore the damages that are mainly caused by strong winds or heavy rainfall at different locations. This approach can provide very useful information that is important for the disaster analysis of different agricultural products.

Description: The demand of city planners for quantitative information on the impact of climate change on the urban environment is increasing. However, such information is usually extracted from decadelong climate projections generated with global or regional climate models (RCMs). Because of their coarse resolution and unsuitable physical parameterization, however, their model output is not adequate to be used at city scale. A full dynamical downscaling to city level, on the other hand, is computationally too expensive for climatological time scales. A statistical–dynamical computationally inexpensive method is therefore proposed that approximates well the behavior of the full dynamical downscaling approach. The approach downscales RCM simulations using the combination of an RCM at high resolution (H-RES) and a land surface model (LSM). The method involves the setup of a database of urban signatures by running an H-RES RCM with and without urban parameterization for a relatively short period. Using an analog approach, these signatures are first selectively added to the long-term RCM data, which are then used as forcing for an LSM using an urban parameterization in a stand-alone mode. A comparison with a full dynamical downscaling approach is presented for the city of Brussels, Belgium, for 30 summers with the combined ALADIN–AROME model (ALARO-0) coupled to the Surface Externalisée model (SURFEX) as H-RES RCM and SURFEX as LSM. The average bias of the nocturnal urban heat island during heat waves is vanishingly small, and the RMSE is strongly reduced. Not only is the statistical–dynamical approach able to correct the heat-wave number and intensities, it can also improve intervariable correlations and multivariate and temporally correlated indices, such as Humidex.

Description: Surface temperature is one of the key parameters for estimating regional evapotranspiration (ET) based on the Surface Energy Balance System (SEBS) model using remote sensing data. However, continuous daily remote sensing surface temperature data are often not available due to the weather and environmental conditions. This paper proposed a scheme to obtain reliable ET that estimating ET using WRF-simulated surface skin temperature (TSK) and then modifying the deviation using the normalized difference vegetation index (NDVI). This study aims to explore whether the model data can be a viable option when the continuous-time-series remote sensing surface temperature is missing for estimating ET. Comparison results show that the correlation between WRF TSK and the measured temperature of the 2-cm soil ( T s) is better than MODIS land surface temperature (LST) in the study area, while the correlation between MODIS LST and the measured surface radiation temperature (IRT) is better than WRF TSK. The MODIS LST is significantly higher than T s, and the WRF TSK is closer to T s than MODIS LST. However, the ET calculated using WRF TSK was not good, exhibiting relatively high ET in the whole area and a poor correlation with the measurements, whereby R2, RMSE, and the percent bias (PBIAS) were equal to 0.1256, 5.2783 mm, and −202.17%, respectively. According to the principle of land surface process simulation in WRF, this paper proposes using NDVI to modify ET calculated using TSK. The comparison between the modified ET and the measurements exhibited a relatively good correlation, with R2 = 0.7532, RMSE = 1.0993 mm, and PBIAS = −17.9%. Therefore, the model surface temperature data can be used to estimate continuous-time-series regional ET when NDVI is used to modify the deviation, which indicates the surface temperature data simulated by the WRF Model can become the optional data for estimating ET and compensate for the shortcoming of poor time continuity of remote sensing data, further expanding the application prospects of meteorological model data in the remote sensing field.

Description: Satellite retrievals strive to exploit the information contained in thousands of channels provided by hyperspectral sensors and show promise in providing a gain in computational efficiency over current radiance assimilation methods by transferring computationally expensive radiative transfer calculations to retrieval providers. This paper describes the implementation of a new approach based on the transformation proposed in 2008 by Migliorini et al., which reduces the impact of the a priori information in the retrievals and generates transformed retrievals (TRs) whose assimilation does not require knowledge of the hyperspectral instruments characteristics. Significantly, the results confirm both the viability of Migliorini’s approach and the possibility of assimilating data from different hyperspectral satellite sensors regardless of the instrument characteristics. The Weather Research and Forecasting (WRF) Model’s Data Assimilation (WRFDA) 3-h cycling system was tested over the central North Pacific Ocean, and the results show that the assimilation of TRs has a greater impact in the characterization of the water vapor distribution than on the temperature field. These results are consistent with the knowledge that temperature field is well constrained by the initial and boundary conditions of the Global Forecast System (GFS), whereas the water vapor distribution is less well constrained in the GFS. While some preliminary results on the comparison between the assimilation with and without TRs in the forecasting system are presented in this paper, additional work remains to explore the impact of the new assimilation approach on forecasts and will be provided in a follow-up publication.

Description: Drought forecasts could effectively reduce the risk of drought. Data-driven models are suitable forecast tools because of their minimal information requirements. The motivation for this study is that because most data-driven models, such as autoregressive integrated moving average (ARIMA) models, can capture linear relationships but cannot capture nonlinear relationships they are insufficient for long-term prediction. The hybrid ARIMA–support vector regression (SVR) model proposed in this paper is based on the advantages of a linear model and a nonlinear model. The multiscale standard precipitation indices (SPI: SPI1, SPI3, SPI6, and SPI12) were forecast and compared using the ARIMA model and the hybrid ARIMA–SVR model. The performance of all models was compared using measures of persistence, such as the coefficient of determination, root-mean-square error, mean absolute error, Nash–Sutcliffe coefficient, and kriging interpolation method in the ArcGIS software. The results show that the prediction accuracies of the multiscale SPI of the combined ARIMA–SVR model and the single ARIMA model were related to the time scale of the index, and they gradually increase with an increase in time scale. The predicted value decreases with increase in lead time. Comparing the measured data with the predicted data from the model shows that the combined ARIMA–SVR model had higher prediction accuracy than the single ARIMA model and that the predicted results 1–2 months ahead show reasonably good agreement with the actual data.

Description: The reproducibility of surface wind and tracer transport simulations from high-resolution weather and transport models was studied over complex terrain in wintertime in Japan. The horizontal grid spacing was varied (5-, 3-, and 1-km grids), and radioactive cesium (Cs-137) from the Fukushima nuclear power plant was used as a tracer. Fukushima has complex terrain, such as mountains and valleys. The model results were validated by observations collected from the national networks of the automated meteorological data acquisition system and the hourly air pollution sampling system. The reproducibility depended on the model resolution, topographic complexity, and synoptic weather conditions. Higher model resolution led to higher reproducibility of surface winds, especially in mountainous areas when the Siberian winter monsoon was disturbed. In contrast, the model improvement was negligible or nonexistent over plain/coastal areas when the synoptic field was steady. The statistical scores of the tracer transport simulations often deteriorated as a result of small errors in the plume locations. However, the higher-resolution models advantageously performed better transport simulations in the mountainous areas because of the lower numerical diffusion and higher reproducibility of the mass flux. The reproducibility of the tracer distribution in the valley of the Fukushima mountainous region was dramatically improved with increasing model resolution. In the range of mesoscale model resolutions (commonly 1–10 km), it was concluded that a higher-resolution model is definitely recommended for tracer transport simulations over mountainous terrain.

Description: In a 2018 paper by Bukovčić et al., polarimetric bivariate power-law relations for estimating snowfall rate S and ice water content (IWC), [Formula: see text] and [Formula: see text], were developed utilizing 2D video disdrometer snow measurements in Oklahoma. Herein, these disdrometer-based relations are generalized for the range of particle aspect ratios from 0.5 to 0.8 and the width of the canting angle distribution from 0° to 40° and are validated via analytical/theoretical derivations and simulations. In addition, a novel S( KDP, Zdr) polarimetric relation utilizing the ratio between specific differential phase KDP and differential reflectivity Zdr, [Formula: see text], is derived. Both KDP and [Formula: see text] are proportionally affected by the ice particles’ aspect ratio and width of the canting angle distribution; therefore, the variables’ ratio tends to be almost invariant to the changes in these parameters. The S( KDP, Z) and S( KDP, Zdr) relations are applied to the polarimetric S-band WSR-88D data obtained from three geographical locations in Virginia, Oklahoma, and Colorado, and their performance is compared with estimations from the standard S( Z) relations and ground snow measurements. The polarimetric estimates of snow accumulations from the three cases exhibit smaller bias in comparison with the S( Z), indicating good potential for more reliable radar snow measurements.

Description: The sensitivity of microwave brightness temperatures (TBs) to hydrometeors at frequencies between 89 and 190 GHz is investigated by comparing Fengyun-3C (FY-3C) Microwave Humidity Sounder-2 (MWHS-2) measurements with radar reflectivity profiles and retrieved products from the Global Precipitation Measurement mission’s Dual-Frequency Precipitation Radar (DPR). Scattering-induced TB depressions (ΔTBs), calculated by subtracting simulated cloud-free TBs from bias-corrected observed TBs for each channel, are compared with DPR-retrieved hydrometeor water path (HWP) and vertically integrated radar reflectivity ZINT. We also account for the number of hydrometeors actually visible in each MWHS-2 channel by weighting HWP with the channel’s cloud-free gas transmission profile and the observation slant path. We denote these transmission-weighted, slant-path-integrated quantities with a superscript asterisk (e.g., HWP*). The so-derived linear sensitivity of ΔTB with respect to HWP* increases with frequency roughly to the power of 1.78. A retrieved HWP* of 1 kg m−2 at 89 GHz on average corresponds to a decrease in observed TB, relative to a cloud-free background, of 11 K. At 183 GHz, the decrease is about 34–53 K. We perform a similar analysis using the vertically integrated, transmission-weighted slant-path radar reflectivity Z*INT and find that ΔTB also decreases approximately linearly with (Z*INT)0.58. The exponent of 0.58 corresponds to the one we find in the purely DPR-retrieval-based ZINT–HWP relation. The observed sensitivities of ΔTB with respect to Z*INT and HWP* allow for the validation of hydrometeor scattering models.

Description: High-resolution maps of the urban heat island (UHI) and building energy consumption are relevant for urban planning in the context of climate change mitigation and adaptation. A statistical–dynamical downscaling for these parameters is proposed in the present study. It combines a statistical local weather type approach with dynamical simulations using the mesoscale atmospheric model Meso-NH coupled to the urban canopy model Town Energy Balance. The downscaling is subject to uncertainties related to the weather type approach (statistical uncertainty) and to the numerical models (dynamical uncertainty). These uncertainties are quantified for two French cities (Toulouse and Dijon) for which long-term dense high-quality observations are available. The seasonal average nocturnal UHI intensity is simulated with less than 0.2 K bias for Dijon, but it is overestimated by up to 0.8 K for Toulouse. The sensitivity of the UHI intensity to weather type is, on average, captured by Meso-NH. The statistical uncertainty is as large as the dynamical uncertainty if only one day is simulated for each weather type. It can be considerably reduced if 3–6 days are taken instead. The UHI reduces the building energy consumption by 10% in the center of Toulouse; it should therefore be taken into account in the production of building energy consumption maps.

Description: The Land–Atmosphere Feedback Experiment (LAFE) was a field campaign to investigate influences of different land surface types on the atmospheric boundary layer (ABL). The primary goals of LAFE were to better understand ABL development and structure and to improve turbulence parameterizations in numerical weather prediction models. Three 10-m micrometeorological towers were installed over different land surface types (i.e., early growth soybean, native grassland, and mature soybean) along a 1.7-km southwest–northeast-oriented line. All towers measured standard meteorological variables in addition to heat, moisture, and momentum fluxes. In this study, we used these measurements to evaluate the validity of applying Monin–Obukhov similarity theory (MOST) to represent surface–atmosphere exchange over different land surface types. We investigated relationships between stability length ζ and the dimensionless wind shear ϕm, temperature gradient ϕh, and moisture gradient ϕq as well as relationships between bulk Richardson number Rib, friction coefficient Cu, heat-transfer coefficient Ct, and moisture-transfer coefficient Cr. We evaluated the new similarity functions developed using independent datasets obtained during the Verification of the Origins of Rotation in Tornadoes Experiment-Southeast (VORTEX-SE). We found that using the Rib functions rather than the more traditional ζ functions to compute wind, temperature, and moisture yielded better agreement with the VORTEX-SE observations. These findings underscore limitations in MOST and motivate the need to consider modifying the functional forms of the similarity equations that form the basis for surface-layer parameterizations in numerical weather prediction models.

Description: An important impact of climate change on agriculture and the sustainability of ecosystems is the increase of extended warm spells during winter. We apply crossing theory to the central England temperature time series of winter daily maximum temperatures to quantify how increased occurrence of higher temperatures translates into more frequent, longer-lasting, and more intense winter warm spells. We find since the late 1800s an overall two- to threefold increase in the frequency and duration of winter warm spells. A winter warm spell of 5 days in duration with daytime maxima above 13°C has a return period that was often over 5 years but now is consistently below 4 years. Weeklong warm intervals that return on average every 5 years now consistently exceed ~13°C. The observed changes in the temporal pattern of environmental variability will affect the phenology of ecological processes and the structure and functioning of ecosystems.

Description: High-density surface networks have become available in recent years in a number of regions throughout the world, but their utility in high-resolution dynamic downscaling has not been examined. As an attempt to fill such a gap, a suite of high-resolution (4 km) dynamical downscaling simulations is developed in this study with the Weather Research and Forecasting (WRF) Model and observation nudging over Liaoning in northeastern China. Three experiments, including no nudging (CTL), analysis nudging (AN), and combined analysis nudging and observation nudging with surface observations (AON), are conducted to downscale the CFSv2 reanalysis with the WRF Model for the year 2015. The three 1-yr regional climate simulations were compared with the independent surface observations. The results show that observational nudging can improve the simulation of surface variables, including temperature, wind speed, humidity, and pressure, more than nudging large-scale driving data with AN alone. The two nudging simulations can improve the cold bias for the temperature of the WRF Model. For precipitation, both the simulations with AN and observation nudging can capture the pattern of precipitation; however, with the introduction of small-scale information at the surface, AON cannot further improve the simulation of precipitation.

Description: This paper presents an evaluation of the precipitation patterns and seedability of orographic clouds in Wyoming using SNOTEL precipitation data and a high-resolution multiyear model simulation over an 8-yr period. A key part of assessing the potential for cloud seeding is to understand the natural precipitation patterns and how often atmospheric conditions and clouds meet cloud-seeding criteria. The analysis shows that high-resolution model simulations are useful tools for studying patterns of orographic precipitation and establishing the seedability of clouds by providing information that is either missed by or not available from current observational networks. This study indicates that the ground-based seeding potential in some mountain ranges in Wyoming is limited by flow blocking and/or prevailing winds that were not normal to the barrier to produce upslope flow. Airborne seeding generally had the most potential for all of the mountain ranges that were studied.

Description: Lightning is a key driver of wildfire activity in Alaska. Quantifying its historical variability and trends has been challenging because of changes in the observational network, but understanding historical and possible future changes in lightning activity is important for fire management planning. Dynamically downscaled reanalysis and global climate model (GCM) data were used to statistically assess lightning data in geographic zones used operationally by fire managers across Alaska. Convective precipitation was found to be a key predictor of weekly lightning activity through multiple regression analysis, along with additional atmospheric stability, moisture, and temperature predictor variables. Model-derived estimates of historical June–July lightning since 1979 showed increasing but lower-magnitude trends than the observed record, derived from the highly heterogeneous lightning sensor network, over the same period throughout interior Alaska. Two downscaled GCM projections estimate a doubling of lightning activity over the same June–July season and geographic region by the end of the twenty-first century. Such a substantial increase in lightning activity may have significant impacts on future wildfire activity in Alaska because of increased opportunities for ignitions, although the final outcome also depends on fire weather conditions and fuels.

Description: This study evaluated the Weather Research and Forecasting (WRF) model sensitivity to different planetary boundary layer (PBL) schemes (the YSU and MYJ schemes) and urban schemes including the bulk scheme (BULK), single-layer urban canopy model (UCM), multi-layer building environment parameterization (BEP) model, and multi-layer building energy model (BEM). Daily reinitialization simulations were conducted over Dallas-Fort Worth during a dry summer month (July 2011) and a wet summer month (July 2015) with weaker (stronger) daytime (nocturnal) UHI in 2011 than 2015. All urban schemes overestimated the urban daytime 2m temperature in both summers, but BEP and BEM still reproduced the daytime urban cool island in dry summer. All urban schemes reproduced the nocturnal urban heat island, with BEP producing the weakest one due to its unrealistic urban cooling. BULK and UCM overestimated the urban canopy wind speed, while BEP and BEM underestimated it. The urban schemes showed prominent impact on daytime PBL profiles. UCM+MYJ showed a superior performance than other configurations. The relatively large (small) aspect ratio between building height and road width in UCM (BEM) was responsible for the overprediction (underprediction) of urban canopy temperature. The relatively low (high) building height in UCM (BEM) was responsible for the overprediction (underprediction) of urban canopy wind speed. Improving urban schemes and providing realistic urban parameters were critical for improving urban canopy simulation.

Description: Catastrophic impacts associated with tropical cyclone (TC) activity mean that the accurate and timely provision of TC outlooks are important to people, places and numerous sectors in Australia and beyond. In this study, we apply a Poisson regression statistical framework to predict TC counts in the Australian region (AR; 5°S-40°S, 90°E-160°E) and its four sub-regions. We test ten unique covariate models, each using different representations of the influence of El Niño-Southern Oscillation (ENSO), Indian Ocean Dipole (IOD) and Southern Annular Mode (SAM), and use an automated covariate selection algorithm to select the optimum combination of predictors. The performance of pre-season TC count outlooks generated between April-October for the AR TC season (November-April) and in-season TC count outlooks generated between November-January for the remaining AR TC season are tested. Results demonstrate skilful TC count outlooks can be generated in April (i.e. 7 months prior to the start of the AR TC season), with Pearson correlation coefficient values between r= 0.59-0.78 and covariates explaining between 35-60% of the variance in TC counts. The dependence of models on indices representing Indian Ocean sea surface temperature (SST) highlights the importance of the Indian Ocean for TC occurrence in this region. Importantly, generating rolling monthly pre-season and in-season outlooks for the AR TC season enables the continuous refinement of expected TC counts in a given season.

Description: Evaluation of downscaled meteorological information is crucial to identifying model behaviors that may propagate to end applications such as the simulation of local air quality. This study conducted and assessed yearlong simulations of hourly meteorological conditions over the Terrace–Kitimat Valley of northwestern British Columbia, Canada, at 1-km horizontal gridding for six PBL schemes in the Weather and Forecasting (WRF) Model, version 4.0. In terms of key surface meteorological variables that affect air quality, simulations over land demonstrated better skill for specific humidity and wind direction than for air temperature and wind speed. Spatial differences in modeled atmospheric properties and vertical profiles, especially for moisture content, were used to diagnose the relative capacity of each PBL scheme to represent pollutant dispersion and dilution. Stable conditions at night increased suppression of boundary layer mixing by the nonlocal Yonsei University (YSU) scheme when compared with suppression by the local eddy-diffusion component of the Asymmetric Convective Model, version 2 (ACM2), scheme, resulting in decreased wind speed and ambient temperature but moister air with the YSU scheme. The weakening of mixing by the Mellor–Yamada–Nakanishi–Niino (MYNN3) scheme with inland distance suggested that higher-order, nonlocal transport is sensitive to increasing topographic steepness toward the northern part of the valley. Disparities in mixing strengths among PBL schemes were greater in the summer when conditions were generally less stable with moist, warm air blowing inland than in winter when the valley channels cold, stable air from the interior. Increased convection in daytime led to greater entrainment of air from aloft and a thicker PBL with the YSU scheme than with the ACM2 scheme in summer while increasing countergradient transport in the MYNN3 scheme that reduces dilution.

Description: A quantile-based bias-correction method is applied to a seven-member dynamic ensemble of global wave climate simulations with the aim of reducing the significant wave height HS, mean wave period Tm, and mean wave direction (MWD) biases, in comparison with the ERA5 reanalysis. The corresponding projected changes toward the end of the twenty-first century are assessed. Seven CMIP5 EC-EARTH runs (single forcing) were used to force seven wave model (WAM) realizations (single model), following the RCP8.5 scenario (single scenario). The biases for the 1979–2005 reference period (present climate) are corrected using the empirical Gumbel quantile mapping and empirical quantile mapping methods. The same bias-correction parameters are applied to the HS, Tm (and wave energy flux Pw), and MWD future climate projections for the 2081–2100 period. The bias-corrected projected changes show increases in the annual mean HS (14%), Tm (6.5%), and Pw (30%) in the Southern Hemisphere and decreases in the Northern Hemisphere (mainly in the North Atlantic Ocean) that are more pronounced during local winter. For the upper quantiles, the bias-corrected projected changes are more striking during local summer, up to 120%, for Pw. After bias correction, the magnitude of the HS, Tm, and Pw original projected changes has generally increased. These results, albeit consistent with recent studies, show the relevance of a quantile-based bias-correction method in the estimation of the future projected changes in swave climate that is able to deal with the misrepresentation of extreme phenomena, especially along the tropical and subtropical latitudes.

Description: A polarimetric radar–based method for retrieving atmospheric ice particle shapes is applied to snowfall measurements by a scanning Ka-band radar deployed at Oliktok Point, Alaska (70.495°N, 149.883°W). The mean aspect ratio, which is defined by the hydrometeor minor-to-major dimension ratio for a spheroidal particle model, is retrieved as a particle shape parameter. The radar variables used for aspect ratio profile retrievals include reflectivity, differential reflectivity, and the copolar correlation coefficient. The retrievals indicate that hydrometeors with mean aspect ratios below 0.2–0.3 are usually present in regions with air temperatures warmer than approximately from −17° to −15°C, corresponding to a regime that has been shown to be favorable for growth of pristine ice crystals of planar habits. Radar reflectivities corresponding to the lowest mean aspect ratios are generally between −10 and 10 dBZ. For colder temperatures, mean aspect ratios are typically in a range between 0.3 and 0.8. There is a tendency for hydrometeor aspect ratios to increase as particles transition from altitudes in the temperature range from −17° to −15°C toward the ground. This increase is believed to result from aggregation and riming processes that cause particles to become more spherical and is associated with areas demonstrating differential reflectivity decreases with increasing reflectivity. Aspect ratio retrievals at the lowest altitudes are consistent with in situ measurements obtained using a surface-based multiangle snowflake camera. Pronounced gradients in particle aspect ratio profiles are observed at altitudes at which there is a change in the dominant hydrometeor species, as inferred by spectral measurements from a vertically pointing Doppler radar.

Description: The operational LIght Detection And Ranging (LIDAR) data from the Hong Kong International Airport (HKIA) is assimilated in a six nest, high resolution Weather Research and Forecasting (WRF) model. The existing radar data assimilation schemes in the WRF Data Assimilation (WRF-DA) package have been adapted to accommodate the high temporal frequency and spatial resolution of the LIDAR observations. The weather data is then used to produce Lagrangian coherent structures (LCS) to detect atmospheric hazards for flights. The coherent structures obtained from the various data sets are contrasted against flight data measured onboard aircrafts. It is found that both WRF and WRF-DA produce coherent structures more distinguishable than those obtained from two-dimensional retrieval, which may improve the detection of true windshear hazards.

Description: Because downscaling tools are needed to support climate change mitigation and adaptation practices, the guarantee of their credibility is of vital importance. To evaluate downscaling results, one needs to select a set of effective and nonoverlapping indices that reflect key system attributes. However, this subject is still insufficiently researched. With this study, we propose a diagnostic framework that evaluates the credibility of precipitation downscaling using five different attributes: spatial, temporal, trend, extreme, and climate event. A daily variant of the bias-corrected spatial downscaling approach is used to downscale daily precipitation from the GFDL-ESM2G climate model at 148 stations in the Yangtze River basin in China. Results prove that this framework is effective in systematically evaluating the performance of downscaling across the Yangtze River basin in the context of climate change and exacerbating climate extremes. Moreover, results also indicate that the downscaling approach adopted in this study yields good performance in correcting spatiotemporal bias, preserving trends, approximating extremes, and characterizing climate events across the Yangtze River basin. The proposed framework can be beneficial to planners and engineers facing issues relevant to climate change assessment.

Description: The multilayer urban canopy models (UCMs) building effect parameterization (BEP) and BEP + building energy model (BEM; a building energy model integrated in BEP) are added to the Yonsei University (YSU) planetary boundary layer (PBL) parameterization in the Weather Research and Forecasting (WRF) Model. The additions allow for the first analysis of the detailed effects of buildings on the urban boundary layer in a nonlocal closure scheme. The modified YSU PBL parameterization is compared with the other 1.5-order local PBL parameterizations that predict turbulent kinetic energy (TKE), Mellor–Yamada–Janjić and Bougeault–Lacarerre, using both ideal and real cases. The ideal-case evaluation confirms that BEP and BEP+BEM produce the expected results in the YSU PBL parameterization because the simulations are qualitatively similar to the TKE-based PBL parameterizations in which the multilayer UCMs have long existed. The modified YSU PBL parameterization is further evaluated for a real case. Similar to the ideal case, there are larger differences among the different UCMs (simple bulk scheme, BEP, and BEP+BEM) than across the PBL parameterizations when the UCM is held fixed. Based on evaluation against urban near-surface wind and temperature observations for this case, the BEP and BEP+BEM simulations are superior to the simple bulk scheme for each PBL parameterization.

Description: A multiscale modeling study of a real case has been conducted to explore the capability of the large-eddy simulation version of the Weather Research and Forecasting Model (WRF-LES) over Xiaohaituo Mountain (a game zone for the Beijing, China, 2022 Winter Olympic Games). In comparing WRF-LES results with observations collected during the Mountain Terrain Atmospheric Observations and Modeling (MOUNTAOM) field campaign, it is found that at 37-m resolution with LES settings, the model can reasonably capture both large-scale events and microscale atmospheric circulation characteristics. Employing the Shuttle Radar Topography Mission 1 arc s dataset (SRTM1; ~30 m) high-resolution topographic dataset instead of the traditional USGS_30s (~900 m) dataset effectively improves the model capability for reproducing fluctuations and turbulent features of surface winds. Five sensitivity experiments are conducted to investigate the impact of different PBL treatments, including YSU/Shin and Hong (SH) PBL schemes and LES with 1.5-order turbulence kinetic energy closure model (1.5TKE), Smagorinsky (SMAG), and nonlinear backscatter and anisotropy (NBA) subgrid-scale (SGS) stress models. In this case, at gray-zone scales, differences between YSU and SH are negligible. LES outperform two PBL schemes that generate smaller turbulence kinetic energy and increase the model errors for mean wind speed, energy spectra, and probability density functions of velocity. Another key finding is that wind field features in the boundary layer over complex terrain are more sensitive to the choice of SGS models than above the boundary layer. With the increase of model resolution, the effects of the SGS model become more significant, especially for the statistical characteristics of turbulence. Among these three SGS models, NBA has the best performance. Overall, this study demonstrates that WRF-LES is a promising tool for simulating real weather flows over complex terrain.

Description: This paper focuses on the analysis of the time series behavior of the air quality in the 50 U.S. states by looking at the statistical properties of particulate matter (PM10 and PM2.5) datasets. We use long daily time series of outdoor air quality indices to examine issues such as the degree of persistence as well as the existence of time trends in data. For this purpose, we use a long-memory fractionally integrated framework. The results show significant negative time trend coefficients in a number of states and evidence of long memory in the majority of the cases. In general, we observe heterogeneous results across counties though we notice higher degrees of persistence in the states on the west with respect to those on the east, where there is a general decreasing trend. It is hoped that the findings in the paper will continue to assist in quantitative evidence-based air quality regulation and policies.

Description: In this paper, we propose a downscaling method that statistically describes a local-scale climate from large-scale circulations using the case of a Korean basin during boreal winter. Specifically, since the East Asian winter monsoon (EAWM) affects the climate of the Korean Peninsula, we make a weather generator model describing the response of the basin climate to the monsoon strength. Moreover, it operates on the basis of a tercile probabilistic prediction of the EAWM strength to generate diverse scenarios of daily weather sequence during the season, which can be utilized in evaluation of the climate impact. We evaluate the prediction skills of operational hindcasts for several existing EAWM indices by applying a multinomial logistic regression method to choose the most suitable index for the downscaling. In the weather generator model, the precipitation model part is designed to be fully parametric. Its parameter values are allowed to vary according to the monsoon strength so that they can represent the climate variability of precipitation. In the temperature model part, the daily temporal variations of the temperature over the Korean basin are decomposed into several oscillations with different frequencies. Since the slowly varying oscillations significantly respond to the monsoon strength, the proposed downscaling scheme is based on the statistical simulation of oscillations according to the monsoon strength. The proposed downscaling scheme is evaluated in terms of the reproducibility of the climate characteristics for a given EAWM strength and the informativeness for predicting monthly climate characteristics.

Description: Ice storms are important winter weather events that can have substantial environmental, economic, and social impacts. Mapping and assessment of damage after these events could be improved by making ice accretion measurements at a greater number of sites than is currently available. There is a need for low-cost collectors that can be distributed broadly in volunteer observation networks; however, use of low-cost collectors necessitates understanding of how collector characteristics and configurations influence measurements of ice accretion. A study was conducted at the Hubbard Brook Experimental Forest in New Hampshire that involved spraying water over passive ice collectors during freezing conditions to simulate ice storms of different intensity. The collectors consisted of plates composed of four different materials and installed horizontally; two different types of wires strung horizontally; and rods of three different materials, with three different diameters, and installed at three different inclinations. Results showed that planar ice thickness on plates was 2.5–3 times as great as the radial ice thickness on rods or wires, which is consistent with expectations based on theory and empirical evidence from previous studies. Rods mounted on an angle rather than horizontally reduced the formation of icicles and enabled more consistent measurements. Results such as these provide much needed information for comparing ice accretion data. Understanding of relationships among collector configurations could be refined further by collecting data from natural ice storms under a broader range of weather conditions.

Description: Surface albedo, the fraction of incoming solar radiation reflected hemispherically by the surface, is an essential climate variable (ECV) directly related to the energy budget of Earth. The presence and properties of snow cover alter surface albedo significantly, with variability in temporal scales reaching from seasonal to diurnal. The diurnal variation of snow albedo is typically parameterized with the solar zenith angle, but it cannot take into account asymmetry with respect to midday. Using the solar azimuth angle instead is suggested, since especially in the melting season the snow albedo varies highly asymmetrically during the day. To derive a general time- and latitude-independent formula, the azimuth angle values are normalized. Baseline Surface Radiation Network data are used to derive an empirical formula for the diurnal variation of snow black-sky surface albedo. The overall accuracy is on the order of 0.02, and the relative accuracy is about 3%.

Description: Cloud phase retrievals from the Atmospheric Infrared Sounder (AIRS) are evaluated against combined CloudSat–CALIPSO (CCL) observations using four years of data (2007–10) over the Arctic Ocean. AIRS cloud phase is evaluated over sea ice and open ocean separately using collocated CCL and AIRS fields of view (FOVs). In addition, AIRS and CCL cloud phase occurrences are evaluated seasonally, zonally, and with respect to total column water vapor (TCWV) and the temperature difference between 1000 and 300 hPa (ΔT1000−300). Last, collocated MODIS cloud information is implemented in a 1-month case study to assess the relationship between AIRS and CCL phase decisions, cloud cover, and cloud phase throughout the AIRS FOV. Depending on the surface type, AIRS classification skill for single-layer ice and liquid-phase clouds is over the ranges of 85%–95% and 22%–32%, respectively. Most unknown and liquid AIRS phase classifications correspond to mixed-phase clouds. AIRS ice-phase relative occurrence is biased low relative to CCL. However, the liquid-phase relative occurrence is similar between the two instruments. When compared with the CCL climatology, AIRS accurately represents the seasonal cycle of liquid and ice cloud phase across the Arctic as well as the relationship between cloud phase and TCWV and ΔT1000−300 regime in some cases. The more heterogeneous the MODIS cloud macrophysical properties within an AIRS FOV are, the more likely it is that the AIRS FOV is classified as unknown phase.

Description: Modeling and observational studies stemming from the 2013–14 Ontario Winter Lake-effect Systems (OWLeS) field campaign have yielded much insight into the structure and development of long-lake-axis-parallel (LLAP) lake-effect systems over Lake Ontario. This study uses airborne single- and dual-Doppler radar data obtained during two University of Wyoming King Air flights, as well as a high-resolution numerical model simulation, to examine and contrast two distinctly different LLAP band structures observed within a highly persistent lake-effect system on 7–9 January 2014. On 7 January, a very cold air mass accompanied by strong westerly winds and weak capping aloft resulted in a deep, intense LLAP band that produced heavy snowfall well inland. In contrast, weaker winds, weaker surface heat fluxes, and stronger capping aloft resulted in a weaker LLAP band on 9 January. This band was blocked along the downwind shore and produced only light snowfall closer to the shoreline. Although the two structures examined here represent opposite ends of a spectrum of LLAP bands, both cases reveal a well-organized mesoscale secondary circulation composed of two counter-rotating horizontal vortices positioned on either side of a narrow updraft within the band. In both cases, this circulation traces back to a shallow, baroclinic land-breeze front originating along a bulge in the lake’s southern shoreline. As the band extends downstream and the low-level baroclinity weakens, buoyancy increases within the band—driven in part by cloud latent heating—leading to band intensification and a deeper, stronger, and more symmetric secondary circulation over the lake.

Description: Almost all daily rainfall time series contain gaps in the instrumental record. Various methods can be used to fill in missing data using observations at neighboring sites (predictor stations). In this study, five computationally simple gap-filling approaches—normal ratio (NR), linear regression (LR), inverse distance weighting (ID), quantile mapping (QM), and single best estimator (BE)—are evaluated to 1) determine the optimal method for gap filling daily rainfall in Hawaii, 2) quantify the error associated with filling gaps of various size, and 3) determine the value of gap filling prior to spatial interpolation. Results show that the correlation between a target station and a predictor station is more important than proximity of the stations in determining the quality of a rainfall prediction. In addition, the inclusion of rain/no-rain correction on the basis of either correlation between stations or proximity between stations significantly reduces the amount of spurious rainfall added to a filled dataset. For large gaps, relative median errors ranged from 12.5% to 16.5% and no statistical differences were identified between methods. For submonthly gaps, the NR method consistently produced the lowest mean error for 1- (2.1%), 15- (16.6%), and 30-day (27.4%) gaps when the difference between filled and observed monthly totals was considered. Results indicate that gap filling prior to spatial interpolation improves the overall quality of the gridded estimates, because higher correlations and lower performance errors were found when 20% of the daily dataset is filled as opposed to leaving these data unfilled prior to spatial interpolation.

Description: Snow cover plays a significant role in the weather and climate system by affecting the energy and mass transfer between the surface and the atmosphere. It has also far-reaching effects on ecosystems of the snow-covered areas. Therefore, global snow cover observations in a timely manner are needed. Satellite-based instruments can be utilized to produce snow cover information suitable for these needs. Highly variable surface and snow cover features suggest that operational snow extent algorithms may benefit from at least partly empirical approach, based on carefully analyzed training data. Here a new two-phase snow cover algorithm utilizing data from the Advanced Very High Resolution Radiometer (AVHRR) onboard the Metop satellites of the European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT) is introduced and evaluated. This algorithm is used to produce the Metop/AVHRR H32 snow extent product for the Satellite Application Facility on Support to Operational Hydrology and Water Management (H SAF). The algorithm aims at direct detection of snow-covered and snow-free pixels without preceding cloud-masking. Pixels which can not be classified reliably to snow or snow-free, due to clouds or other reasons, are set as unclassified. This reduces the coverage but increases the accuracy of the algorithm. More than four years of snow depth and state of the ground observations from weather stations were used to validate the product. Validation results show that the algorithm produces high-quality snow coverage data which may be suitable, e.g., for numerical weather prediction, hydrological modelling and other applications.

Description: We used empirical-statistical downscaling in a pseudo-reality context, where both large-scale predictors and small-scale predictands were based on climate model results. The large-scale conditions were taken from a global climate model and the small-scale conditions from the dynamical downscaling of the same global model with a convection permitting regional climate model covering South Norway. This hybrid downscaling approach, a “perfect model”-type experiment, provided 120-years of data under the CMIP5 high emission scenario. Ample calibration samples made rigorous testing possible, enabling us to evaluate the effect of empirical-statistical model configurations and predictor choices, and to assess the stationarity of the statistical models by investigating their sensitivity to different calibration intervals. The skill of the statistical models was evaluated in terms of their ability to reproduce the inter-annual correlation and long-term trends in seasonal 2-meter temperature (T2m), wet-day frequency (fw), and wet-day mean precipitation (μ). We found that different 30-year calibration intervals often resulted in differing statistical models, depending on the specific choice of years. The hybrid downscaling approach allowed us to emulate seasonal mean regional climate model output with a high spatial resolution (0.05° latitude and 0.1° longitude grid) for up to 100 GCM runs while circumventing the issue of short calibration time, and provides a robust set of empirically downscaled GCM runs.

Description: “Low-level wind shear” is a known aviation safety hazard and refers to a sustained change in head wind encountered by an aircraft during takeoff or landing. Because of their small spatiotemporal scales and high variability, automatic alerting of wind shears at airports around the world is almost exclusively detection based (using remote sensing equipment). Numerical modeling studies so far mainly cover individual cases and lack systematic validation. This paper presents the first statistical evaluation of numerical weather prediction (NWP) model performance in predicting low-level wind shear at a major international airport over a 2-yr continuous period. The 200-m-resolution Aviation Model (AVM) of the Hong Kong Observatory is used to generate runway-specific wind shear forecasts at 1-min output intervals for the Hong Kong International Airport (HKIA), known for its susceptibility to wind shear occurrence. The AVM forecasts are then validated against over 800 actual reports of wind shear by aircraft pilots over the two major arrival runway corridors, 07LA and 25RA, at HKIA between 2014 and 2015 using a verification scheme with the same level of spatiotemporal stringency as operational alerting systems at HKIA. With “relative operating characteristic” analysis, positive skill is consistently observed across both runway corridors throughout the study period and across all considered forecast lead times out to 6 h ahead. This study serves to establish and document the current capability of fine-resolution NWP in predicting the phenomenon of low-level wind shear for aviation weather applications.

Description: Recent advances in the skill of seasonal forecasts in the extratropics during winter mean they could offer improvements to seasonal hydrological forecasts. However, the signal-to-noise paradox, whereby the variability in the ensemble mean signal is lower than would be expected given its correlation skill, prevents their use to force hydrological models directly. We describe a postprocessing method to adjust for this problem, increasing the size of the predicted signal in the large-scale circulation. This reduces the ratio of predictable components in the North Atlantic Oscillation (NAO) from 3 to 1. We then derive a large ensemble of daily sequences of spatially gridded rainfall that are consistent with the seasonal mean NAO prediction by selecting historical observations conditioned on the adjusted NAO forecasts. Over northern and southwestern Europe, where the NAO is strongly correlated with winter mean rainfall, the variability of the predicted signal in the adjusted rainfall forecasts is consistent with the correlation skill (they have a ratio of predictable components of ~1) and are as skillful as the unadjusted forecasts. The adjusted forecasts show larger predicted deviations from climatology and can be used to better assess the risk of extreme seasonal mean precipitation as well as to force hydrological models.

Description: High-resolution simulations are conducted with the Weather Research and Forecasting Model to evaluate the sensitivity of wake effects and power production from two wind farm parameterizations [the commonly used Fitch scheme and the more recently developed Explicit Wake Parameterization (EWP)] to the resolution at which the model is applied. The simulations are conducted for a 9-month period for a domain encompassing much of the U.S. Midwest. The two horizontal resolutions considered are 4 km × 4 km and 2 km × 2 km grid cells, and the two vertical discretizations employ either 41 or 57 vertical layers (with the latter having double the number in the lowest 1 km). Higher wind speeds are observed close to the wind turbine hub height when a larger number of vertical layers are employed (12 in the lowest 200 m vs 6), which contributes to higher power production from both wind farm schemes. Differences in gross capacity factors for wind turbine power production from the two wind farm parameterizations and with resolution are most strongly manifest under stable conditions (i.e., at night). The spatial extent of wind farm wakes when defined as the area affected by velocity deficits near to wind turbine hub heights in excess of 2% of the simulation without wind turbines is considerably larger in simulations with the Fitch scheme. This spatial extent is generally reduced by increasing the horizontal resolution and/or increasing the number of vertical levels. These results have important applications to projections of expected annual energy production from new wind turbine arrays constructed in the wind shadow from existing wind farms.

Description: An overview of climatological and meteorological conditions and their seasonal variability in the Denali summit region is presented, based on the NCEP–NCAR reanalysis 1 dataset for the 1948–2018 period. At the Denali grid cell, a warming trend of +0.02°C significant at the 95% level is found—equivalent to a temperature increase of 1.4°C over the time period. The number of very cold days (

Description: Deep convection and the related occurrence of hail, intense precipitation, and wind gusts represent a hazard to a range of energy infrastructure including wind turbine blades. Wind turbine blade leading-edge erosion (LEE) is caused by the impact of falling hydrometeors onto rotating wind turbine blades. It is a major source of wind turbine maintenance costs and energy losses from wind farms. In the U.S. southern Great Plains (SGP), where there is widespread wind energy development, deep convection and hail events are common, increasing the potential for precipitation-driven LEE. A 25-day Weather Research and Forecasting (WRF) Model simulation conducted at convection-permitting resolution and using a detailed microphysics scheme is carried out for the SGP to evaluate the effectiveness in modeling the wind and precipitation conditions relevant to LEE potential. WRF output for these properties is evaluated using radar observations of precipitation (including hail) and reflectivity, in situ wind speed measurements, and wind power generation. This research demonstrates some skill for the primary drivers of LEE. Wind speeds, rainfall rates, and precipitation totals show good agreement with observations. The occurrence of precipitation during power-producing wind speeds is also shown to exhibit fidelity. Hail events frequently occur during periods when wind turbines are rotating and are especially important to LEE in the SGP. The presence of hail is modeled with a mean proportion correct of 0.77 and an odds ratio of 4.55. Further research is needed to demonstrate sufficient model performance to be actionable for the wind energy industry, and there is evidence for positive model bias in cloud reflectivity.

Description: The University of Pécs and NCAR Bin (UPNB) microphysical scheme was implemented into the mesoscale Weather Research and Forecast (WRF) Model that was used to study the impact of silver iodide (AgI) seeding on precipitation formation in winter orographic clouds. Four different experimental units were chosen from the Wyoming Weather Modification Pilot Project to simulate the seeding effect. The results of the numerical experiments show the following: (i) Comparisons with the soundings, snow gauges, and microwave radiometer data indicate that the three-dimensional simulations with detailed microphysics reasonably represent both the dynamics and the microphysics of real clouds. (ii) The dispersion of the AgI particles from the simulated ground-based seeding was effective because of turbulent mixing. (iii) In the investigated cases (surface temperature is less than 0°C), surface precipitation and precipitation efficiency show low susceptibility to the concentrations of cloud condensation nuclei and natural ice nucleating particles. (iv) If the available liquid water content promotes the enhancement of the number of snowflakes by diffusional growth, the surface precipitation can be increased by more than 5%. A novel parameter relevant to orographic clouds, horizontally integrated liquid water path (LWP), was evaluated to find the relation between seeding efficiency and liquid water content. The impact of seeding is negligible if the horizontal LWP is less than 0.1 mm and is apparent if the horizontal LWP is larger than 1 mm, as based on the cases investigated in this study.

Description: A new thermal metric is examined that is based on the ratio of day-to-day warm and cold surface temperature transitions. Urban and rural sites in Canada are examined using this new metric for the minimum temperature, maximum temperature, and mean temperature of the day. A distinctive signature emerges for “peri-urban” landscapes—landscapes at the urban–rural interface—and thus may provide a useful and relatively easy way to detect such environments using the current and historical climate records. A climatological basis for the presence of these distinct thermal signatures in peri-urban landscapes is proposed.