ALBERT

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: Winds at the Salt Lake City International Airport (SLC) during the April–October period from 1948 to 2003 have been observed to shift to the north (up-valley direction) between late morning and afternoon on over 70% of the days without precipitation. Lake-breeze fronts that develop as a result of the differential heating between the air over the nearby Great Salt Lake and that over the lake’s surroundings are observed at SLC only a few times each month. Fewer lake-breeze fronts are observed during late July–early September than before or after that period. Interannual fluctuations in the areal extent of the shallow Great Salt Lake contribute to year-to-year variations in the number of lake-breeze frontal passages at SLC. Data collected during the Vertical Transport and Mixing Experiment (VTMX) of October 2000 are used to examine the structure and evolution of a lake-breeze front that moved through the Salt Lake Valley on 17 October. The onset of upslope and up-valley winds occurred within the valley prior to the passage of the lake-breeze front. The lake-breeze front moved at roughly 3 m s−1 up the valley and was characterized near the surface by an abrupt increase in wind speed and dewpoint temperature over a distance of 3–4 km. Rapid vertical mixing of aerosols at the top of the 600–800-m-deep boundary layer was evident as the front passed.

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: Drought is a typical disaster in the main soybean production area of northeast China. The spatiotemporal variations of drought related to soybean production based on a crop water deficit index (CWDI) and sensitivity to meteorological variables were investigated in northeast China using daily meteorological data from 87 weather stations from 1981 to 2010. Statistical analysis revealed that precipitation could not meet the water demands of soybeans during the seedling–branching, filling, and maturing stages, and excessive drought occurred more often in northeast China. The Mann–Kendall test indicated that the soybean CWDI significantly increased during the filling stage. Kriging spatial analysis showed that the most drought-prone area was located in the west of northeast China. Explanations for the spatiotemporal variations of the drought for soybean production were explored in terms of meteorological variables. Statistical analysis showed that the crop evapotranspiration, air temperature, wind speed, and number of sunshine hours were significantly higher and the precipitation and relative humidity were significantly lower in the drought-prone area than in the dry area less prone to droughts. An explored method of sensitive analysis quantitatively revealed that precipitation and humidity negatively affected the CWDI, whereas temperature, wind speed, and number of sunshine hours positively affected the CWDI. The CWDI was most sensitive to precipitation. These results not only provide valuable information for soybean planning and management but also produce important background and physical evidence for the influence of climate on the drought related to soybean production in northeast China.

Description: Many data-merging studies of the Tropical Rainfall Measuring Mission (TRMM) satellite involve the integration of high-resolution Visible and Infrared Scanner (VIRS) signals (~2 km) with low-resolution Precipitation Radar (PR) footprint (~5 km) to obtain comprehensive information from observations. Based on the merged dataset, “warm rain” is generally identified as having averaging 10.8-μm brightness temperatures (TB10.8) exceeding 273 K and the existence of surface rainfall. However, this integration may lead to the misidentification of warm rain because the beam-filling problem (nonuniform TB10.8 in PR pixels) is not fully considered through the method using high-resolution TB10.8 to match low-resolution rainfall. To assess the bias that is associated with identifying warm rain, a new dataset that includes all VIRS signals within the PR resolution is established, and the characteristics of this warm rain in the summers of 1998–2012 are analyzed. The results show that clear-sky pixels and “cold” pixels probably exist in some apparent warm-rain cases (60.5% and 11.2% of the time, respectively). According to this finding, warm-rain pixels are divided into pixels with and without clear sky. Statistical analysis shows that the existence of clear-sky pixels has a huge influence on the characteristics of the warm-rain pixels. The implications of this study are that many of the warm-rain cases are in fact not warm rain. When studying warm rain, the situation whereby the edges of pixels are clear sky should be fully considered. Also, when computing the weighted average brightness temperature and other characteristics of warm-rain pixels, parts that are clear-sky or cold pixels should be expelled to mitigate beam-filling problems.

Description: Accurate evaluations of incoming longwave radiation (Lin) parameterization have practical implications for glacier and river runoff changes in high-mountain regions of the Tibetan Plateau (TP). To identify potential means of accurately predicting spatiotemporal variations in Lin, 13 clear-sky parameterizations combined with 10 cloud corrections for all-sky atmospheric emissivity were evaluated at five sites in high-mountain regions of the TP through temporal and spatial parameter transfer tests. Most locally calibrated parameterizations for clear-sky and all-sky conditions performed well when applied to the calibration site. The best parameterization at five sites is Dilley and O’Brien’s A model combined with Sicart et al.’s A for cloud-correction-incorporated relative humidity. The performance of parameter transferability in time is better than that in space for the same all-sky parameterizations. The performance of parameter transferability in space presents spatial discrepancies. In addition, all all-sky parameterizations show a decrease in performance with increasing altitude regardless of whether the parameters of all-sky parameterizations were recalibrated by local conditions or transferred from other study sites. This may be attributable to the difference between screen-level air temperature and the effective atmospheric boundary layer temperature and to different cloud-base heights. Nevertheless, such worse performance at higher altitudes is likely to change because of terrain, underlying surfaces, and wind systems, among other factors. The study also describes possible spatial characteristics of Lin and its driving factors by reviewing the few studies about Lin for the mountain regions of the TP.

Description: Analysis of drop size distributions (DSD) measured by collocated Meteorological Particle Spectrometer (MPS) and a third-generation, low-profile, 2D-video disdrometer (2DVD) are presented. Two events from two different regions (Greeley, Colorado, and Huntsville, Alabama) are analyzed. While the MPS, with its 50-μm resolution, enabled measurements of small drops, typically for drop diameters below about 1.1 mm, the 2DVD provided accurate measurements for drop diameters above 0.7 mm. Drop concentrations in the 0.7–1.1-mm overlap region were found to be in excellent agreement between the two instruments. Examination of the combined spectra clearly reveals a drizzle mode and a precipitation mode. The combined spectra were analyzed in terms of the DSD parameters, namely, the normalized intercept parameter NW, the mass-weighted mean diameter Dm, and the standard deviation of mass spectrum σM. The inclusion of small drops significantly affected the NW and the ratio σM/Dm toward higher values relative to using the 2DVD-based spectra alone. For each of the two events, polarimetric radar data were used to characterize the variation of radar-measured reflectivity Zh and differential reflectivity Zdr with Dm from the combined spectra. In the Greeley event, this variation at S band was well captured for small values of Dm (

Description: A simple numerical experiment was performed to investigate the result published in many papers that measurements indicate that aggregates may be well represented as oblate spheroids with mean aspect ratio (semiminor axis to semimajor axis length) of 0.6. The aspect ratio measurements are derived from two-dimensional projections of complex three-dimensional aggregates. Here, aggregates were modeled as ellipsoids with semiprincipal axes of length a, b, and c, which include oblate spheroids (a = b) as a class, and the projected aspect ratios of large numbers of two-dimensional projections of them were sampled. When sampling oblate spheroids with aspect ratio 0.6 over random orientations, the mean projected aspect ratio is 0.746. A mean projected aspect ratio of 0.6 is obtained for an oblate spheroid with aspect ratio of 0.33. When sampling randomly oriented ellipsoids with semiminor axes (b, c) varying from 0.10 to 1.00 in steps of 0.01, representing many complex shapes, the mean projected aspect ratio is 0.595, close to the measured mean projected aspect ratio of aggregates of 0.6. These experiments demonstrate that the conclusion one may safely draw from the projected aspect ratio measurements is that the mean aspect ratio of aggregates is lower than 0.6. Moreover, the projected aspect ratio distributions from measurements suggest a mixture of aggregate shapes, rather than only oblate spheroids as is often assumed.

Description: Surface air temperature is a basic meteorological variable to monitor the environment and assess climate change. Four remote sensing methods—the temperature–vegetation index (TVX), the univariate linear regression method, the multivariate linear regression method, and the advection-energy balance for surface air temperature (ADEBAT)—have been developed to acquire surface air temperature on a regional scale. To evaluate their utilities, they were applied to estimate the surface air temperature in northwestern China and were compared with each other through regressive analyses, t tests, estimation errors, and analyses on estimations of different underlying surfaces. Results can be summarized into three aspects: 1) The regressive analyses and t tests indicate that the multivariate linear regression method and the ADEBAT provide better accuracy than the other two methods. 2) Frequency histograms on estimation errors show that the multivariate linear regression method produces the minimum error range, and the univariate linear regression method produces the maximum error range. Errors of the multivariate linear regression method exhibit a nearly normal distribution and that of the ADEBAT exhibit a bimodal distribution, whereas the other two methods display negative skewness distributions. 3) Estimates on different underlying surfaces show that the TVX and the univariate linear regression method are significantly limited in regions with sparse vegetation cover. The multivariate linear regression method has estimation errors within 1°C and without high levels of errors, and the ADEBAT also produces high estimation errors on bare ground.

Description: The existence of strong easterly winds down the western slope of the south-central Andes in Chile, locally known as Puelche winds, has been known by the meteorological community since at least the mid-twentieth century. However, this is the first time that a climatological characterization of them is presented. The analysis is based on 36 yr of daily CFSR–NCEP reanalyzed data, validated by surface weather observations. Puelche winds are present all year round. The main synoptic-scale forcing of Puelche winds in south-central Chile is the passage of cold anticyclonic systems across the Andes Mountains. As these systems progress into the South American continent, a zonal surface circulation crossing from Argentina (upslope) to Chile (downslope) develops. Unlike terral and raco, other foehnlike winds at subtropical latitudes in Chile, the Puelche winds are forced by both meridional and zonal pressure gradients. Presumably, the smaller altitude of the Andes Mountains south of 35°S allows the air crossing from east to west in response to the presence of the migratory high pressure system over Argentina. As in other places where foehnlike winds develop, the warming extends far from places where the Puelche is actually observed, that is, to the west of the Andes into the surface at the coastal and the central depression areas. This “foehn clearance” is the result of cloudless sky and drier atmosphere that would allow an increase in the solar radiation reaching the surface and a subsequent warming of the near-surface air. The foehn clearance also drives an enhanced nighttime cooling, especially on the days after the onset of the Puelche event.

Description: The intermittency of solar power production is dependent on the evolution and advection of the nearby cloud field. A key problem related to solar energy integration is the improvement of 1-h-ahead forecasts to reduce the impact of intermittency on power systems operations. Many solar forecasts explicitly or implicitly assume Taylor’s hypothesis. While such advection-only forecasts can be presumed to be valid across sufficiently short time scales, it is not clear how rapidly the skill of such a forecast decays with increased lead time. As the goal is to improve the quality of 1-h-ahead forecasts, this work focuses on quantifying the skill of cloud-track wind-based cumulus-dominated cloud field forecasts with respect to lead time. No explicit connection is drawn to the quality of solar forecasts because of the importance of separating two potential sources of error: cloud field forecasting and radiative transfer estimation. It is found that the cumulus field forecast skill begins to asymptotically approach a minimum at lead times of beyond 30 min, suggesting that advection-only forecasts in a cumulus-dominated environment should not be relied upon for 1-h-ahead point forecasts used by radiative transfer methods to estimate solar power production. A first attempt at forming a probabilistic forecast that can quantify this increasing uncertainty when using advection-only methods is presented.

Description: Cities are home to the majority of humanity. Therefore, understanding the mechanisms that control urban climates has substantial societal importance to a variety of sectors, including public health and energy management. In this study, data from an urban sensor network (25 stations) and moderate-resolution remote sensing were used to explore how spatial variation in near-surface air temperature Ta, vapor pressure deficit (VPD), and land surface temperature (LST) depend on local variations in urban land use, both diurnally and seasonally, in the Boston, Massachusetts, metropolitan area. Positive correlations were observed between the amount of local impervious surface area (ISA) and both Ta and VPD. Heat-island effects peaked during the growing-season nighttime, when mean Ta and VPD increased by up to 0.02°C and 0.008 kPa, respectively, per unit ISA. Air temperature and VPD were strongly coupled, but their relationship exhibited significant diurnal hysteresis during the growing season, with changes in VPD generally preceding changes in Ta. Over 79% of the urban–rural difference in VPD was explained by differences in near-surface atmospheric water content, which the authors attribute to reduced evapotranspiration from lower canopy cover in Boston’s urban core. Changes in daytime heat-island intensity were mediated by seasonal feedbacks between vegetation transpiration and VPD forcing. Differences between LST and Ta showed weaker coupling in highly urbanized areas than in rural areas, with summertime surface-urban-heat-island intensity (based on LST) being up to 14°C higher than corresponding urban–rural differences in Ta.

Description: Analyses of the Tropical Rainfall Measuring Mission (TRMM) satellite rainfall estimates reveal a substantial disagreement between its active [Precipitation Radar (PR)] and passive [TRMM Microwave Imager (TMI)] sensors over certain regions. This study focuses on understanding the role of the synoptic state of atmosphere in these discrepancies over land regions where passive microwave (PMW) retrievals are limited to scattering signals. As such the variability in the relationship between the ice-induced scattering signal and the surface rainfall is examined. Using the Amazon River and central Africa regions as a test bed, it is found that the systematic difference seen between PR and TMI rainfall estimates is well correlated with both the precipitating system structure and the level of its organization. Relying on a clustering technique to group raining scenes into three broad but distinct organizational categories, it is found that, relative to the PR, deep-organized systems are typically overestimated by TMI while the shallower ones are underestimated. Results suggest that the storm organization level can explain up to 50% of the regional systematic difference between the two sensors. Because of its potential for retrieval improvement, the ability to forecast the level of systems organization is tested. The state of the atmosphere is found to favor certain storm types when constrained by CAPE, wind shear, dewpoint depression, and vertical humidity distribution. Among other findings, the observations reveal that the ratio between boundary layer and midtropospheric moisture correlates well with the organization level of convection. If adjusted by the observed PR-to-TMI ratio under a given environment, the differences between PMW and PR rainfall estimates are diminished, at maximum, by 30% in RMSE and by 40% in the mean.

Description: A Bayesian optimal estimation methodology is applied to retrieve the time-varying ice particle mass–dimensional (M–D) relationships (i.e., M = amDbm) and the associated uncertainties using the in situ data that were collected by the NASA WB-57 during the Midlatitude Airborne Cirrus Properties Experiment (MACPEX) in March and April 2011. The authors utilize the coincident measurements of bulk ice water content and projected cross-sectional area to constrain M–D relationships and estimate the uncertainties. It is demonstrated that the additional information provided by the particle area with respect to size could contribute considerable improvements to the algorithm performance. Extreme variability of M–D properties is found among cases as well as within individual cases, indicating the nondiscrete nature of ice crystal habits within cloud volumes and further suggesting the risk of assuming a constant M–D relationship in different conditions. Relative uncertainties of am are approximately from 50% to 80%, and relative uncertainties of bm range from 6% to 9.5%, which would cause approximately 2.5-dB uncertainty in forward-modeled radar reflectivity or a factor-of-2 uncertainty in ice water content.

Description: As NWP and climate models continue to evolve toward finer grid spacing, efforts have been undertaken to better represent urban effects. For this study, the single-layer urban canopy model (SLUCM) of the High-Resolution Land Data Assimilation System (HRLDAS) and WRF Model was used to investigate the sensitivity of near-surface air temperatures and energy fluxes to SLUCM parameters in uncoupled (land) and coupled (land–atmosphere) predictions. Output from HRLDAS and WRF was compared with observations from the Oklahoma Mesonet and Joint Urban 2003 experiment. Variations in roof albedo (0.04–0.4) produced 40–135 W m−2 changes in net radiation and sensible heat fluxes. Sensible and ground heat fluxes varied by 40–100 W m−2 with changes in roof thermal conductivity (0.05–1.4). The urban fraction was found to be the only SLUCM parameter to significantly impact latent heat fluxes. Near-surface air temperatures, particularly during the daytime, did not show significant variations with SLUCM parameters (remaining within the 0.5-K range). Differences in urban air temperatures due to the change in boundary layer scheme were greater than the temperature changes due to SLUCM parameter variations. The sensitivity of near-surface air temperatures to SLUCM parameters depended on the method used to calculate the skin temperature of the impervious surface. For all simulations, predicted 2-m urban air temperatures were consistently higher than observations, with deviations approaching 8 K during the day and below 3 K at night. These large errors affected the model’s skill in reproducing the diurnal cycle of UHI intensity.

Description: Retrievals of liquid cloud properties from remote sensing observations by necessity assume sufficient information is contained in the measurements, and in the prior knowledge of the cloudy state, to uniquely determine a solution. Bayesian algorithms produce a retrieval that consists of the joint probability distribution function (PDF) of cloud properties given the measurements and prior knowledge. The Bayesian posterior PDF provides the maximum likelihood estimate, the information content in specific measurements, the effect of observation and forward model uncertainties, and quantitative error estimates. It also provides a test of whether, and in which contexts, a set of observations is able to provide a unique solution. In this work, a Bayesian Markov chain Monte Carlo (MCMC) algorithm is used to sample the joint posterior PDF for retrieved cloud properties in shallow liquid clouds over the remote Southern Ocean. Combined active and passive observations from spaceborne W-band cloud radar and visible and near-infrared reflectance are used to retrieve the parameters of a gamma particle size distribution (PSD) for cloud droplets and drizzle. Combined active and passive measurements are able to distinguish between clouds with and without precipitation; however, unique retrieval of PSD properties requires specification of a scene-appropriate prior estimate. While much of the uncertainty in an unconstrained retrieval can be mitigated by use of information from 94-GHz passive brightness temperature measurements, simply increasing measurement accuracy does not render a unique solution. The results demonstrate the robustness of a Bayesian retrieval methodology and highlight the importance of an appropriately scene-consistent prior constraint in underdetermined remote sensing retrievals.

Description: Freezing precipitation, including freezing rain, freezing drizzle, and ice pellets, presents substantial hazards to transportation, energy, and infrastructure. Most quantitative climate-length (〉30 years) datasets for freezing precipitation are obtained from in situ instrumentation, such as National Weather Service (NWS) automated observing systems or the NWS Cooperative Observer Program, that can be spatially and temporally inhomogeneous. This work investigates whether the 32-km North American Regional Reanalysis (NARR) is a viable dataset for developing a comprehensive and high-resolution spatially gridded dataset for freezing precipitation and its associated meteorological environment, with a focus on the south-central United States. NARR includes categorical precipitation type as a variable; to permit a translatable method across other gridded multidimensional reanalyses, however, a multialgorithm approach is also used to extract environmental conditions, event counts, and liquid water equivalent (LWE) for freezing precipitation, defined as total accumulated freezing rain and ice pellets. The resulting datasets are evaluated spatially and temporally against hourly and daily event counts and LWE compiled from 13 first-order NWS stations, the National Centers for Environmental Information Storm Data product, and “meteorological phenomena identification near the ground” (mPING) observations. Very good statistical agreement is evident for many of the station sites, and NARR is able, in most cases, to reproduce years with heavy-ice events and the spatial extent of such events. Climatological freezing precipitation tends to be underestimated in the western subdomain and overestimated in the south. It is concluded that the derived datasets could be a useful tool for climatological research and hazard analysis, with some caveats.

Description: This paper explores simulated changes to the cool-season (November–March) storm-surge and coastal-flooding events at the Battery in New York City, New York (NYC), during most of the twenty-first century using several climate models and a previously developed multilinear regression model. The surface wind and pressure forcing for the surge predictions are obtained from an ensemble of 6 coupled global climate models (GCM) and 30 members from the Community Earth System Model. Using the “RCP8.5” emission scenario, both the single-model and multimodel ensemble means yielded insignificant (significance level p 〉 0.05) simulated changes to the median surge event (〉0.61 m above astronomical tide) between a historical period (1979–2004) and the mid-to-late twenty-first century (2054–79). There is also little change in the return interval for the moderate-to-high surge events. By the mid-to-late twenty-first century, there is a poleward shift of the mean surface cyclone track in many of the models and most GCMs demonstrate an intensification of the average cyclone. There is little effect on the future surge events at the Battery because most of these storm changes are not in a region that favors more or higher-amplitude surges at NYC. Rather, projected sea level rise dominates the future simulated change in the number of flooding events by the mid-to-late twenty-first century. For example, the projections show about 23 times as many coastal-flooding events (tide + surge ≥ 2.44 m above mean lower low water; 1983–2001) in 2079 when compared with 1979, and the return intervals for some major coastal floods (e.g., the December 1992 northeaster) decrease by a factor of 3–4.

Description: Cloud ice microphysical properties measured or estimated from in situ aircraft observations are compared with global climate models and satellite active remote sensor retrievals. Two large datasets, with direct measurements of the ice water content (IWC) and encompassing data from polar to tropical regions, are combined to yield a large database of in situ measurements. The intention of this study is to identify strengths and weaknesses of the various methods used to derive ice cloud microphysical properties. The in situ data are measured with total water hygrometers, condensed water probes, and particle spectrometers. Data from polar, midlatitude, and tropical locations are included. The satellite data are retrieved from CloudSat/CALIPSO [the CloudSat Ice Cloud Property Product (2C-ICE) and 2C-SNOW-PROFILE] and Global Precipitation Measurement (GPM) Level2A. Although the 2C-ICE retrieval is for IWC, a method to use the IWC to get snowfall rates S is developed. The GPM retrievals are for snowfall rate only. Model results are derived using the Community Atmosphere Model (CAM5) and the Met Office Unified Model [Global Atmosphere 7 (GA7)]. The retrievals and model results are related to the in situ observations using temperature and are partitioned by geographical region. Specific variables compared between the in situ observations, models, and retrievals are the IWC and S. Satellite-retrieved IWCs are reasonably close in value to the in situ observations, whereas the models’ values are relatively low by comparison. Differences between the in situ IWCs and those from the other methods are compounded when S is considered, leading to model snowfall rates that are considerably lower than those derived from the in situ data. Anomalous trends with temperature are noted in some instances.

Description: Standard meteorological model performance evaluation (sMPE) can be insufficient in determining “fitness” for air quality modeling. An sMPE compares predictions of meteorological variables with community-based thresholds. Conceptually, these thresholds measure the model’s capability to represent mesoscale features that cause variability in air pollution. A method that instead examines features could provide a better estimate of fitness. This work compares measures of fitness from sMPE analysis with a feature-based MPE (fMPE). Meteorological simulations for Bogotá, Colombia, using the Weather Research and Forecasting (WRF) Model provide an ideal case study that highlights the importance of fMPE. Bogotá is particularly interesting because the complex topography presents challenges for WRF in sMPE. A cluster analysis identified four dominant meteorological features associated with air quality driven by wind patterns. The model predictions are able to pass several sMPE thresholds but show poor performance for wind direction. The base simulation can be improved with alternative surface characterization datasets for terrain, soil classification, and land use. Despite doubling the number of days with acceptable specific humidity, overall acceptability was never more than 10%. By comparison, an fMPE showed that predictions were able to reproduce the air-quality-relevant features on 38.4% of the days. The fMPE is based on features derived from an observational cluster analysis that have clear relationships with air quality, which suggests that reproducing those features will indicate better air quality model performance. An fMPE may be particularly useful for high-resolution modeling (1 km or less) when finescale variability can cause poor sMPE performance even when the general pattern that drives air pollution is well reproduced.

Description: The authors investigate whether radar remote sensing of a certain class of ice clouds allows for characterization of the precipitation rates and aggregation processes. The NASA DC-8 collected the measurements in tropical anvils during July and August 2007 as part of the Tropical Composition, Cloud and Climate Coupling (TC4) experiment. Measured hydrometeor size distributions are used to estimate precipitation rates (P) and to solve the hydrodynamical collection equation. These distributions are also used to estimate radar reflectivity factors (Z) and Doppler velocities (Vd) at W, Ka, and Ku bands. Optimal estimation techniques are then used to estimate the uncertainty in retrieving P and aggregation rates (A) from combinations of Z and Vd. It is found that diagnosing information about A requires significant averaging and that a dual-frequency combination of W and Ka bands seems to provide the most information for the ice clouds sampled during TC4. Furthermore, the addition of Vd with expected uncertainty contributes little to the microphysical retrieval of either P or A. It is also shown that accounting for uncertainty in ice microphysical bulk density dominates the retrieval uncertainty in both P and A causing, for instance, the instantaneous uncertainty in retrieved P to increase from ~30% to ~200%.

Description: Cloud-top verification is inherently difficult because of large uncertainties in the estimates of observed cloud-top height. Misplacement of cloud top associated with transmittance through optically thin cirrus is one of the most common problems. Forward radiative models permit a direct comparison of predicted and observed radiance, but uncertainties in the vertical position of clouds remain. In this work, synthetic brightness temperatures are compared with forecast cloud-top heights so as to investigate potential errors and develop filters to remove optically thin ice clouds. Results from a statistical analysis reveal that up to 50% of the clouds with brightness temperatures as high as 280 K are actually optically thin cirrus. The filters successfully removed most of the thin ice clouds, allowing for the diagnosis of very specific errors. The results indicate a strong negative bias in midtropospheric cloud cover in the model, as well as a lack of land-based convective cumuliform clouds. The model also predicted an area of persistent stratus over the North Atlantic Ocean that was not apparent in the observations. In contrast, high cloud tops associated with deep convection were well simulated, as were mesoscale areas of enhanced trade cumulus coverage in the Sargasso Sea.

Description: Retrievals of ice-cloud properties from cloud-radar observations are challenging because the retrieval methods are typically underdetermined. Here, the authors investigate whether additional information can be obtained from higher-order moments and the slopes of the radar Doppler spectrum such as skewness and kurtosis as well as the slopes of the Doppler peak. To estimate quantitatively the additional information content, a generalized Bayesian retrieval framework that is based on optimal estimation is developed. Real and synthetic cloud-radar observations of the Indirect and Semi-Direct Aerosol Campaign (ISDAC) dataset obtained around Barrow, Alaska, are used in this study. The state vector consists of the microphysical (particle-size distribution, mass–size relation, and cross section–area relation) and kinematic (vertical wind and turbulence) quantities required to forward model the moments and slopes of the radar Doppler spectrum. It is found that, for a single radar frequency, more information can be retrieved when including higher-order moments and slopes than when using only reflectivity and mean Doppler velocity but two radar frequencies. When using all moments and slopes with two or even three frequencies, the uncertainties of all state variables, including the mass–size relation, can be considerably reduced with respect to the prior knowledge.

Description: Precipitation events in which rainfall is generated primarily below the freezing level via warm-rain processes have traditionally presented a significant challenge for radar and satellite quantitative precipitation estimation (QPE) algorithms. It is possible to improve QPE in warm-rain events if they are correctly identified/classified as warm rain prior to precipitation estimation. Additionally, it is anticipated that classification schemes incorporating polarimetric radar data will be able to leverage precipitation microphysical information to better identify warm-rain precipitation events. This study lays the groundwork for the development of a polarimetric warm-rain classification algorithm by documenting the typical three-dimensional polarimetric characteristics associated with midlatitude warm-rain precipitation events. These characteristics are then compared with those observed in non-warm-rain events. Nearly all warm-rain precipitation events were characterized by lower median values of Z, ZDR, and KDP relative to the non-warm-rain convective cases. Furthermore, droplet coalescence was determined to be the dominant microphysical process in the majority of warm-rain events, while in non-warm-rain stratiform events, evaporation and breakup appeared to be the dominant (warm) microphysical processes. Most warm-rain events were also associated with sharp decreases in reflectivity, with height above the freezing level coincident with low echo-top heights and freezing-level ZDR values near 0, indicating limited ice- and mixed-phase precipitation growth processes. These results support the feasibility of a future polarimetric warm-rain identification algorithm.

Description: Nepal has been the location of a series of fatal aircraft accidents, raising serious concerns about civil aviation security and the safety of passengers. However, significant studies on weather patterns associated with the airports and air routes of the Himalayan complex terrain and their implications for aviation activities are yet to be carried out. The present study numerically reconstructs the prevailing weather conditions and puts forward some possible causes behind the most recent fatal aircraft accident in the foothills of the western Nepal Himalaya at 0730 UTC (1315 LST) 16 February 2014. The weather patterns have been numerically simulated at 1-km2 horizontal grid resolution using the Weather Research and Forecasting (WRF) modeling system. The reconstructed weather situation shows the existence of a low-level cloud ceiling, supercooled cloud water and hail, trapped mountain waves, supercritical descent of a strong tail wind, and the development of turbulence at the altitude of the flight path followed by the aircraft. The aircraft might have gone through a series of weather hazards including visibility obstruction, moderate turbulence, abnormal loss in altitude, and icing. It is concluded that the weather situation over the region was adverse enough to affect small aircraft and therefore that it might have played an important role leading to the fatal accident. The development of hazardous weather over the region may be attributed to a previously unanticipated large-scale easterly gravity current over the middle hills of the Nepal Himalaya. The gravity current originated from the central high Himalayan mountainous region located northeast of the Kathmandu valley and traveled more than 200 km, reaching the foothills of the western Nepal Himalaya.

Description: The Cyclone Global Navigation Satellite System (CYGNSS) constellation is designed to provide observations of surface wind speed in and near the inner core of tropical cyclones with high temporal resolution throughout the storm’s life cycle. A method is developed for estimating tropical cyclone integrated kinetic energy (IKE) using CYGNSS observations. IKE is calculated for each geographically based quadrant out to an estimate of the 34-kt (1 kt = 0.51 m s−1) wind radius. The CYGNSS-IKE estimator is tested and its performance is characterized using simulated CYGNSS observations with realistic measurement errors. CYGNSS-IKE performance improves for stronger, more organized storms and with increasing number of observations over the extent of the 34-kt radius. Known sampling information can be used for quality control. While CYGNSS-IKE is calculated for individual geographic quadrants, using a total-IKE—a sum over all quadrants—improves performance. CYGNSS-IKE should be of interest to operational and research meteorologists, insurance companies, and others interested in the destructive potential of tropical cyclones developing in data-sparse regions, which will now be covered by CYGNSS. The CYGNSS-IKE product will be available for the 2017 Atlantic Ocean hurricane season.

Description: During the late afternoon of 18 June 2015, ozone concentrations in advance of a strong lake-breeze front arising from the Great Salt Lake in northern Utah were ~20 ppb lower than those in its wake. The lake-breeze progression and ozone concentrations in the valley were monitored by an enhanced observation network that included automated weather stations, a nearby Terminal Doppler Weather Radar, state air quality measurement sites, and mobile platforms, including a news helicopter. Southerly flow opposing the lake breeze increased convergent frontogenesis and delayed the onset of its passage through the Salt Lake valley. Ozone concentrations were exceptionally high aloft at the lake-breeze frontal boundary. The progression of this lake breeze was simulated using the Weather Research and Forecasting Model at 1-km horizontal grid spacing over northern Utah. The model was initialized using hourly analyses from the High Resolution Rapid Refresh model. Errors in the underlying surface initialization were improved by adjusting the areal extent and surface temperature of the lake to observed lake conditions. An urban canopy parameterization is also included. The opposing southerly flow was weaker in the simulation than that observed such that the simulated lake-breeze front occurred too early. Continuous passive tracers initialized within and ahead of the lake breeze highlight the dispersion and transport of pollutants arising from the lake-breeze front. Tracers within the lake breeze are confined near the surface while tracers in advance of the front are lofted over it.

Description: The Moderate Resolution Imaging Spectroradiometer (MODIS) aerosol optical depth (AOD) retrievals from the Terra and Aqua satellites currently provide the largest satellite aerosol dataset for investigating relationships to meteorological phenomena, such as aerosol impact on electrification in deep convection. The usefulness of polar-orbiting satellite aerosol retrievals in lightning inference is examined by correlating MODIS AOD retrievals with lightning observations of the thunderstorms in the summers during 2002–14 over northern Alabama. Lightning flashes during the 1400–1700 local standard time peak period show weak but positive correlations with the MODIS AOD retrievals 2–4 h earlier. The correlation becomes stronger in particular meteorological conditions, including weak vertical wind shear and prevailing northerly winds over northern Alabama. Results show that the MODIS AOD retrievals are less useful in predicting enhanced lightning flash rate for lightning-producing storms than the forecasts of other meteorological variables that are more closely linked to the intensification of convective storms. However, when relatively weaker convective available potential energy (CAPE) is forecast, the probability of enhanced lightning flash rate increases in a more polluted environment, making the knowledge of aerosols more useful in lightning inference in such CAPE regimes. The aerosol enhancement of lightning, if present, may be associated with enhanced convergence in the boundary layer and secondary convection.

Description: The successive stages of nocturnal atmospheric structure inside a small isolated basin are investigated when a katabatically driven flow on an adjacent tilted plain advects cold air over the basin rim. Data came from Arizona’s Meteor Crater during intensive observing period 4 of the Second Meteor Crater Experiment (METCRAX II) when a mesoscale flow above the plain was superimposed on the katabatic flow leading to a flow acceleration and then deceleration over the course of the night. Following an overflow-initiation phase, the basin atmosphere over the upwind inner sidewall progressed through three stages as the katabatic flow accelerated: 1) a cold-air-intrusion phase in which the overflowing cold air accelerated down the upwind inner sidewall, 2) a bifurcation phase in which the katabatic stable layer lifted over the rim included both a nonnegatively buoyant upper layer that flowed horizontally over the basin and a negatively buoyant lower layer (the cold-air intrusion) that continued on the slope below to create a hydraulic jump at the foot of the sidewall, and 3) a final warm-air-intrusion phase in which shear instability in the upper overflowing layer produced a lee wave that brought warm air from the elevated residual layer downward into the basin. Strong winds during the third phase penetrated to the basin floor, stirring the preexisting, intensely stable, cold pool. Later in the night a wind direction change aloft decelerated the katabatic wind and the atmosphere progressed back through the bifurcation and cold-air-intrusion phases. A conceptual diagram illustrates the first four evolutionary phases.

Description: The coupled ocean–atmosphere–wave–sediment transport model and the Weather Research and Forecasting (WRF) atmospheric model were used to simulate extreme rainfall events from 10 to 25 June 2010 in eastern Northeast Brazil (ENEB). The simulations aimed at investigating the improvements from using a coupled ocean–atmospheric model of meteorological systems as the ocean–atmosphere interactions intensified during the period when flood events occurred in ENEB. In June 2010, the sea surface temperature (SST) was warmer than 28.5°C in the western tropical South Atlantic Ocean with anomalies above 1°C, which are characteristics of a warm pool. The sensible and latent heat fluxes acted to moisten the lower troposphere and affected the height of the trade winds inversion layer (TWIL). The meteorological system that occurred at the low–midlevels during the period favored the weakening and even the breakdown of the TWIL. These atmospheric disturbances were associated with convergence, cyclonic vorticity, and upward water vapor motion to the midtroposphere levels. When the disturbances reached the coast of ENEB, they favored convection and intense rainfall over the region. Both coupled and uncoupled modeling experiments were performed with the same physical parameterizations and validated with in situ atmospheric and oceanic measurements. The results highlight that the predictions of extreme rainfall events were greatly improved with the coupled model.

Description: Sea-effect precipitation (SEP) over the Shandong Peninsula is a unique climatological phenomenon in mainland China, and it exerts a considerable impact on the southern shore of the Bohai Sea. From observed data from 123 stations for the period 1962–2012, the characteristics of cold-season (November–February) SEP in this area were analyzed. Results showed that SEP occurred throughout the late autumn and winter. In all, 1173 SEP days were identified during the 51 years, of which snow days accounted for 73.7% and rain and snow–rain days accounted for 16.1% and 10.1%, respectively. December had the largest number of SEP snow days, followed by January and November. November was the most productive month in terms of SEP rain and snow–rain days. Intense SEP snowfall mainly affected the inland hill area of the peninsula, whereas light SEP snowfall reached farther inland. SEP rainfall shared a similar pattern with snowfall. The SEP frequency showed a significant interannual variability and a nonsignificant upward trend over the period analyzed. SEP was most likely to occur when the temperature difference between sea surface and 850 hPa over the Bohai Sea was above 10°C, indicating a dominant influence of low-level cold-air advection over the sea on the generation and development of the weather phenomenon. A significant negative correlation was also found between the area of sea ice in the Bohai Sea and intense SEP snowfall, indicating that sea ice extent had an important effect on SEP variability over the peninsula. In the case of extremely intense SEP events, a deeper East Asian trough at the 500-hPa level developed over the southwest of the study area and temperature and geopotential height contours were orthogonal to each other, indicating strong geostrophic cold-air advection over the Bohai Sea and the Shandong Peninsula. The extremely intense SEP events were also characterized by anomalous low temperature and high relative humidity in the lower troposphere, which contributed to greater gravitational instability in the study area.

Description: The higher temperature in cities relative to their rural surroundings, known as the urban heat island (UHI), is one of the most well documented and severe anthropogenic modifications of the environment. Heat islands are hazardous to residents and the sustainability of cities during summertime and heat waves; on the other hand, they provide considerable benefits in wintertime. Yet, the evolution of UHIs during cold waves has not yet been explored. In this study, ground-based observations from 12 U.S. cities and high-resolution weather simulations show that UHIs not only warm urban areas in the winter but also further intensify during cold waves by up to 1.32° ± 0.78°C (mean ± standard deviation) at night relative to precedent and subsequent periods. Anthropogenic heat released from building heating is found to contribute more than 30% of the UHI intensification. UHIs thus serve as shelters against extreme-cold events and provide benefits that include mitigating cold hazard and reducing heating demand. More important, simulations indicate that standard UHI mitigation measures such as green or cool roofs reduce these cold-wave benefits to different extents. Cities, particularly in cool and cold temperate climates, should hence revisit their policies to favor (existing) mitigation approaches that are effective only during hot periods.

Description: Persistent high pressure conditions over the Mediterranean Basin favor the occurrence of sea breezes that can lead to ozone transport through complex recirculation patterns. These features were investigated during an ozone episode with hourly concentrations exceeding 200 μg m−3 that occurred on July 2015 in Naples (Italy), one of the largest and densest conurbations in the Mediterranean region. Aircraft measurements were taken at heights from 150 to 1500 m AGL and compared and integrated with high-resolution meteorological and air quality model simulations to investigate local circulation and pollutants dynamics. The integration of airborne measurements, surface observations, and modeling established a framework to assess the photochemical phenomena in the area. Sea breezes and local emissions triggered ozone production at inland areas, causing high concentrations between the coast and the Apennine chain. Ozone was then injected into the upper boundary layer and transported toward the sea by the wind rotation occurring above 500 m AGL, causing a complex vertical layering of concentrations, with maxima between 500 and 800 m AGL. Vertical growth of the ozone concentration profile was also caused by the decrease of the boundary layer depth occurring when the breeze front reached the inland area carrying NOx-rich air from the densely populated coast and favoring titration near the surface. Although the whole airshed was a net ozone producer, local surface concentrations were determined by a complex interaction of atmospheric flow and chemistry at different scales, supporting the need for coordinated efforts to control smog precursors over wide areas.

Description: Climate research in Portugal is often constrained by the lack of homogeneous, temporally and spatially consistent, and long-term climatic series. To overcome this limitation, the authors developed new high-resolution gridded datasets (~1 km) of daily mean, minimum, and maximum air temperatures over Portugal (1950–2015, 66 yr), based on gridded daily temperatures (E-OBS) at ~25-km spatial resolution. A two-step approach was followed, under the assumption that daily temperature variability in Portugal is mainly controlled by atmospheric large-scale forcing, while local processes are mostly expressed as strong spatial gradients. First, monthly baseline (1971–2000) patterns were estimated at 1-km grid resolution by applying multivariate linear regressions (exploratory variables: elevation, latitude, and distance to coastline). A kriging of residuals from baseline normals of 36 weather stations was applied for bias corrections. Second, bilinearly interpolated daily temperature anomalies were then added to the daily baseline patterns to obtain the final datasets. The method performance was evaluated using fivefold cross-validations. The datasets were also validated using daily temperatures from 23 stations not incorporated in E-OBS. A climatological analysis based on these datasets was carried out, highlighting spatial heterogeneities, seasonality, long-term trends, interannual variability, and extremes. The spatial and temporal variability is generally coherent with previous studies at coarser resolutions. An overall warming trend is apparent for all variables and indices, but showing different strengths and spatial variability. These datasets show important advantages over preexisting data, including more detailed and accurate information on trends and interannual variability of precipitation extremes, and can thus be applied to several areas of research in Portugal, such as hydrology, ecology, agriculture, and forestry.

Description: Marine boundary layer clouds are modified by processes at different spatial and temporal scales. To isolate the processes governing aerosol–cloud–precipitation interactions, multiday synoptic variability of the environment must be accounted for. Information on the location of low clouds relative to the ridge–trough pattern gives insight into how cloud properties vary as a function of environmental subsidence and stability. The technique of self-organizing maps (SOMs) is employed to objectively classify the 500-hPa geopotential height patterns for 33 years of reanalysis fields (ERA-Interim) into pretrough, trough, posttrough, ridge, and zonal-flow categories. The SOM technique is applied to a region of prevalent marine low cloudiness over the eastern North Atlantic Ocean that is centered on the Azores island chain, the location of a long-term U.S. Department of Energy observation site. The Azores consistently lie in an area of substantial variability in synoptic configuration, thermodynamic environment, and cloud properties. The SOM method was run in two ways to emphasize multiday and seasonal variability separately. Over and near the Azores, there is an east-to-west sloshing back and forth of the western edge of marine low clouds associated with different synoptic states. The different synoptic states also exhibit substantial north–south variability in the position of high clouds. For any given month of the year, there is large year-to-year variability in the occurrence of different synoptic states. Hence, estimating the climatological behavior of clouds from short-term field campaigns has large uncertainties. This SOM approach is a robust method that is broadly applicable to characterizing synoptic regimes for any location.

Description: A historical rainfall index, relevant to the grazing industries of Queensland, Australia, is described. We refer to our index as the Queensland grazing lands rainfall index (QGLRI), which is a long-term (1890/91–present) time series of austral summer (November–March) rainfall, spatially averaged over a region we define as the Queensland grazing lands region. We argue that our QGLRI better represents historical summer rainfall variability faced by the majority of the grazing industry in Queensland than does area-averaged statewide rainfall. The geographical boundaries of our region were chosen to 1) better represent the spatial patterns of land use, settlement, and livestock densities and 2) coincide with spatial patterns of airmass dominance. The selected region covers 59% of Queensland’s mainland area but carries more than 80% of the state’s livestock. The region’s boundaries also closely match the mean summer location of the boundaries of the “tropical maritime Pacific” air mass. The selected 5-month season (November–March) was chosen based on summer rainfall dominance, seasonal climatic effects restricting pasture and animal growth, and pasture management implications such as burning and the risk of overgrazing. We find that this season also corresponds to the timing of tropical maritime airmass dominance. The remaining regions of Queensland, far-northern and far-western Queensland, also correspond to well-defined dominant air masses, with properties that are markedly different from those of the tropical maritime Pacific air mass. We demonstrate that the rainfall regime in far-northern Queensland makes a strong contribution to statewide totals, resulting in statewide summer rainfall having lower variability than our QGLRI.

Description: The surface skin and air temperatures reported by the Atmospheric Infrared Sounder/Advanced Microwave Sounding Unit-A (AIRS/AMSU-A), the Modern-Era Retrospective Analysis for Research and Applications (MERRA), and MERRA-2 at Summit, Greenland, are compared with near-surface air temperatures measured at National Oceanic and Atmospheric Administration (NOAA) and Greenland Climate Network (GC-Net) weather stations. The AIRS/AMSU-A surface skin temperature (TS) is best correlated with the NOAA 2-m air temperature (T2M) but tends to be colder than the station measurements. The difference may be the result of the frequent near-surface temperature inversions in the region. The AIRS/AMSU-A surface air temperature (SAT) is also correlated with the NOAA T2M but has a warm bias during the cold season and a larger standard error than the surface temperature. The extrapolation of the temperature profile to calculate the AIRS SAT may not be valid for the strongest inversions. The GC-Net temperature sensors are not held at fixed heights throughout the year; however, they are typically closer to the surface than the NOAA station sensors. Comparing the lapse rates at the two stations shows that it is larger closer to the surface. The difference between the AIRS/AMSU-A SAT and TS is sensitive to near-surface inversions and tends to measure stronger inversions than both stations. The AIRS/AMSU-A may be sampling a thicker layer than either station. The MERRA-2 surface and near-surface temperatures show improvements over MERRA but little sensitivity to near-surface temperature inversions.

Description: Lake-effect storms (LES) produce substantial snowfall in the vicinity of the downwind shores of the Great Lakes. These storms may take many forms; one type of LES event, lake to lake (L2L), occurs when LES clouds/snowbands develop over an upstream lake (e.g., Lake Huron), extend across an intervening landmass, and continue over a downstream lake (e.g., Lake Ontario). The current study examined LES snowfall in the vicinity of Lake Ontario and the atmospheric conditions during Lake Huron-to-Lake Ontario L2L days as compared with LES days on which an L2L connection was not present [i.e., only Lake Ontario (OLO)] for the cold seasons (October–March) from 2003/04 through 2013/14. Analyses of snowfall demonstrate that, on average, significantly greater LES snowfall totals occur downstream of Lake Ontario on L2L days than on OLO days. The difference in mean snowfall between L2L and OLO days approaches 200% in some areas near the Tug Hill Plateau and central New York State. Analyses of atmospheric conditions found more-favorable LES environments on L2L days relative to OLO days that included greater instability over the upwind lake, more near-surface moisture available, faster wind speeds, and larger surface heat fluxes over the upstream lake. Last, despite significant snowfalls on L2L days, their average contribution to the annual accumulated LES snowfall in the vicinity of Lake Ontario was found to be small (i.e., 25%–30%) because of the relatively infrequent occurrence of L2L days.

Description: The properties of clouds derived using a suite of remote sensors on board the Australian research vessel (R/V) Investigator during the 5-week Clouds, Aerosols, Precipitation, Radiation, and Atmospheric Composition over the Southern Ocean (CAPRICORN) voyage south of Australia during March and April 2016 are examined and compared to similar measurements collected by CloudSat and CALIPSO (CC) and from data collected at Graciosa Island, Azores (GRW). In addition, we use depolarization lidar data to examine the thermodynamic phase partitioning as a function of temperature and compare those statistics to similar information reported from the CALIPSO lidar in low-Earth orbit. We find that cloud cover during CAPRICORN was 76%, dominated by clouds based in the marine boundary layer. This was lower than comparable measurements collected by CC during these months, although the CC dataset observed significantly more high clouds. In the surface-based data, approximately 2/3 (1/2) of all low-level layers observed had a reflectivity below −20 dBZ in the CAPRICORN data (GRW) with 30% (20%) of the layers observed only by the lidar. The phase partitioning in layers based in the lower 4 km of the atmosphere was similar in the two surface-based datasets, indicating a greater occurrence of the ice phase in subfreezing low clouds than what is reported from analysis of CALIPSO data.

Description: Dual-polarization radar rainfall estimation relationships have been extensively tested in continental and subtropical coastal rain regimes, with little testing over tropical oceans where the majority of rain on Earth occurs. A 1.5-yr Indo-Pacific warm pool disdrometer dataset was used to quantify the impacts of tropical oceanic drop-size distribution (DSD) variability on dual-polarization radar variables and their resulting utility for rainfall estimation. Variables that were analyzed include differential reflectivity Zdr; specific differential phase Kdp; reflectivity Zh; and specific attenuation Ah. When compared with continental or coastal convection, tropical oceanic Zdr and Kdp values were more often of low magnitude ( 0.3° km−1 thresholds. Because of these thresholds and the lack of hail, R(Kdp) was never used. At all wavelengths, R(z) was still needed 43% of the time during light rain (R 〈 5 mm h−1, Zdr 〈 0.25 dB), composing 7% of the total rain volume. As wavelength decreased, R(Kdp, ζdr) was used more often, R(z, ζdr) was used less often, and the blended algorithm became increasingly more accurate than R(z).

Description: In this study, rainfall data are prepared at a 0.01° scale using 16-yr spaceborne radar data over the area of 36.13°S–36.13°N as provided by the Tropical Rainfall Measuring Mission (TRMM) Precipitation Radar (PR). A spatial resolution that is finer than the field of view is obtained by assuming rainfall uniformity within an instantaneous footprint centered on the PR footprint geolocation. These ultra-high-resolution data reveal local rainfall concentrations over slope areas. A new estimate of the maximum rainfall at Cherrapunji, India, was observed on the valley side, approximately 5 km east of the gauge station, and is approximately 50% higher than the value indicated by the 0.1°-scale data. A case study of Yakushima Island, Japan, indicates that several percent of the sampling error arising from the spatial mismatch may be contained in conventional 0.05°-scale datasets generated without footprint areal information. The differences attributable to the enhancement in the resolution are significant in complex terrain such as the Himalayas. The differences in rainfall averaged for the 0.1° and 0.01° scales exceed 10 mm day−1 over specific slope areas. In the case of New Guinea, the mean rainfall on a mountain ridge can be 30 times smaller than that on an adjacent slope at a distance of 0.25°; this is not well represented by other high-resolution datasets based on gauges and infrared radiometers. The substantial nonuniformity of rainfall climatology highlights the need for a better understanding of kilometer-scale geographic constraints on rainfall and retrieval approaches.

Description: Surfzone sensible heat flux (HS,SZ) obtained through direct eddy-covariance estimates was measured at four different sandy beach sites along Monterey Bay, California. The HS,SZ source region is estimated from a footprint probability distribution function (pdf) model and is only considered when at least 70% of the footprint pdf occupies the surfzone. The measured HS,SZ is 2 times the modeled interfacial sensible heat (HS,int) using COARE3.5. A formulation for estimating sensible heat flux from spray droplets (HS,spray) generated during depth-limited wave breaking is developed. The sea-spray generation function for droplet radii ranging over 0.1 〈 ro 〈 1000 μm is based on self-similar spectra of spray droplets measured from the surfzone forced by the average depth-limited breaking wave dissipation across the surfzone. However, it is shown that the size of the spume droplets that contribute to HS,spray is limited owing to the relatively short residence time in air as the droplets fall to the sea surface during wave breaking. The addition of the surfzone-modeled HS,spray to the COARE3.5 HS,int gives values similar to the observed surfzone HS,SZ, highlighting the importance of depth-limited wave-breaking processes to sensible heat flux. Measured HS,SZ values are an order of magnitude larger than simultaneous open ocean observations.

Description: This study investigates to what extent weather types (WTs) computed over tropical North Africa and the tropical North Atlantic Ocean (40°W–40°E, 0°–30°N) are relevant for documenting intraseasonal and interannual temperature variability in tropical North Africa (west of 37°E, 2°–27°N). Nine WTs are extracted by using clustering analysis of the daily anomalies of sea level pressure and low-level 925-hPa winds from two reanalyses (NCEP–DOE and ERA-Interim) from 1979 to 2016. The analyses are carried out separately for February–March and for April–June, when temperatures reach their annual peak across most of the region. The WT patterns mix the effects of different multiscale phenomena, including the extratropical Rossby waves that travel on the northern edge of the domain (and are partly related to the North Atlantic Oscillation), the Madden–Julian oscillation, and Kelvin waves in the subequatorial zone. For each WT, warm (cold) minimum (TN) and maximum (TX) daily temperature anomalies tend to be systematically located east of cyclonic (anticyclonic) low-level circulation anomalies associated with the WT patterns. By modulating the greenhouse effect, the water vapor anomalies exert a major influence, leading to warm (cold) TX and TN anomalies associated with moister (drier) air, through advection from the tropical Atlantic or equatorial latitudes (the Sahara or northern latitudes) toward tropical North Africa. WTs are also useful for monitoring interannual variability of TX/TN anomalies mostly north of 10°N in February–March, even if they greatly underestimate the long-term warming trend. Most WTs significantly raise or lower the probability of regional-scale heat peaks, defined as the crossing of the 90th percentile of daily TX or TN.

Description: The use of nudging in the Weather Research and Forecasting (WRF) Model to constrain regional climate downscaling simulations is gaining in popularity because it can reduce error and improve consistency with the driving data. While some attention has been paid to whether nudging is beneficial for downscaling, very little research has been performed to determine best practices. In fact, many published papers use the default nudging configuration (which was designed for numerical weather prediction), follow practices used by colleagues, or adapt methods developed for other regional climate models. Here, a suite of 45 three-year simulations is conducted with WRF over the continental United States to systematically and comprehensively examine a variety of nudging strategies. The simulations here use a longer test period than did previously published works to better evaluate the robustness of each strategy through all four seasons, through multiple years, and across nine regions of the United States. The analysis focuses on the evaluation of 2-m temperature and precipitation, which are two of the most commonly required downscaled output fields for air quality, health, and ecosystems applications. Several specific recommendations are provided to effectively use nudging in WRF for regional climate applications. In particular, spectral nudging is preferred over analysis nudging. Spectral nudging performs best in WRF when it is used toward wind above the planetary boundary layer (through the stratosphere) and temperature and moisture only within the free troposphere. Furthermore, the nudging toward moisture is very sensitive to the nudging coefficient, and the default nudging coefficient in WRF is too high to be used effectively for moisture.

Description: Convective surface winds in the contiguous United States are classified as severe at 50 kt (58 mi h−1, or 26 m s−1), whether measured or estimated. In 2006, NCDC (now NCEI) Storm Data, from which analyzed data are directly derived, began explicit categorization of such reports as measured gusts (MGs) or estimated gusts (EGs). Because of the documented tendency of human observers to overestimate winds, the quality and reliability of EGs (especially in comparison with MGs) has been challenged, mostly for nonconvective winds and controlled-testing situations, but only speculatively for bulk convective data. For the 10-yr period of 2006–15, 150 423 filtered convective-wind gust magnitudes are compared and analyzed, including 15 183 MGs and 135 240 EGs, both nationally and by state. Nonmeteorological artifacts include marked geographic discontinuities and pronounced “spikes” of an order of magnitude in which EG values (in both miles per hour and knots) end in the digits 0 or 5. Sources such as NWS employees, storm chasers, and the general public overestimate EGs, whereas trained spotters are relatively accurate. Analysis of the ratio of EG to MG and their sources also reveals an apparent warning-verification-influence bias in the climatological distribution of wind gusts imparted by EG reliance in the Southeast. Results from prior wind-tunnel testing of human subjects are applied to 1) illustrate the difference between measured and perceived winds for the database and 2) show the impact on the severe-wind dataset if EGs were bias-corrected for the human overestimation factor.

Description: The properties of clouds derived from measurements collected using a suite of remote sensors on board the Australian R/V Investigator during a 5-week voyage into the Southern Ocean during March and April 2016 are examined. Based on the findings presented in a companion paper (Part I), we focus our attention on a subset of marine boundary layer (MBL) clouds that form a substantial portion of the cloud-coverage fraction. We find that the MBL clouds that dominate the coverage fraction tend to occur in decoupled boundary layers near the base of marine inversions. The thermodynamic conditions under which these clouds are found are reminiscent of marine stratocumulus studied extensively in the subtropical eastern ocean basins except that here they are often supercooled with a rare presence of the ice phase, quite tenuous in terms of their physical properties, rarely drizzling, and tend to occur in migratory high pressure systems in cold-air advection. We develop a simple cloud property retrieval algorithm that uses as input the lidar-attenuated backscatter, the W-band radar reflectivity, and the 31-GHz brightness temperature. We find that the stratocumulus clouds examined have water paths in the 15–25 g m−2 range, effective radii near 8 μm, and number concentrations in the 20 cm−3 range in the Southern Ocean with optical depths in the range of 3–4. We speculate that addressing the high bias in absorbed shortwave radiation in climate models will require understanding the processes that form and maintain these marine stratocumulus clouds in southern mid- and high latitudes.

Description: At 0247 UTC 13 February 2013, a South Korean commercial aircraft encountered moderate-level clear-air turbulence at ~24 000 ft (~7.3 km) over the Yellow Sea (121.25°E, 38.55°N) en route from Incheon, South Korea, to Tianjin, China. Two crew members were severely injured by this event. To investigate the possible mechanisms of this event, a high-resolution numerical simulation using the Weather Research and Forecasting Model was conducted. In the synoptic-scale flow pattern, one of two bifurcated jet streams passed over the Yellow Sea, and strong horizontal and vertical gradients of the wind occurred on the northern edge of the jet stream near the flight route. An upper-level frontal system on the cyclonic shear side of the jet intensified as it moved northward toward a strengthening upper-level trough in northeastern China. The developed jet–frontal system induced strong vertical wind shear and tropopause folding, which extended down to about z = 5 km, near the observed turbulence region. Despite a relatively high stability with an intrusion of stratospheric air with tropopause folding, the strong vertical wind shear led to a small Richardson number in the incident region, which in turn induced the aviation turbulence through the Kelvin–Helmholtz instability. Although small-scale mountain waves were evident during the passage of flight before the incident time, breaking of these waves was not likely the key factor for the observed turbulence, given that the wave amplitudes were weak and that the strong zonal wind on the upstream of the mountain waves prohibited wave saturation and breakdown.

Description: Currently, there are several spaceborne microwave instruments suitable for the detection and quantitative estimation of snowfall. To test and improve retrieval snowfall algorithms, ground validation datasets that combine detailed characterization of snowfall microphysics and spatial precipitation measurements are required. To this endpoint, measurements of snow microphysics are combined with large-scale weather radar observations to generate such a dataset. The quantitative snowfall estimates are computed by applying event-specific relations between the equivalent reflectivity factor and snowfall rate to weather radar observations. The relations are derived using retrieved ice particle microphysical properties from observations that were carried out at the University of Helsinki research station in Hyytiälä, Finland, which is about 64 km east of the radar. For each event, the uncertainties of the estimate are also determined. The feasibility of using this type of data to validate spaceborne snowfall measurements and algorithms is demonstrated with the NASA GPM Microwave Imager (GMI) snowfall product. The detection skill and retrieved surface snowfall precipitation of the GPROF detection algorithm, versions V04A and V05A, are assessed over southern Finland. On the basis of the 26 studied overpasses, probability of detection (POD) is 0.90 for version V04A and 0.84 for version V05A, and corresponding false-alarm rates are 0.09 and 0.10, respectively. A clear dependence of detection skill on cloud echo top height is shown: POD increased from 0.8 to 0.99 (V04A) and from 0.61 to 0.94 (V05A) as the cloud echo top altitude increased from 2 to 5 km. Both versions underestimate the snowfall rate by factors of 6 (V04A) and 3 (V05A).

Description: Hourly rainfall from automatic weather stations and reanalysis data from MERRA-2 are used to investigate the diurnal variation of precipitation in Hong Kong, a site along the southeast China coast with strong interactions between the monsoonal circulation and the land–sea breeze. The precipitation in Hong Kong is characterized by a spatially uniform diurnal cycle with the peak at about 0800 local time (LT), with rather weak dependence on local terrain. Precipitation unrelated to tropical cyclones (TCs) dominates the diurnal variation of precipitation, especially in the summer. The diurnal cycle exhibits a notable seasonal dependence, with the strongest signal in the summer. The morning peak of precipitation over Hong Kong is coincident with deep rising motion, linking to near-surface convergence and overlying weak divergence. The convergence may be attributed to the prevalence of the southerly monsoonal flow over the South China Sea (SCS) and to the northerly land breeze induced by the land–sea thermal contrast in the morning. The overlying weak divergence could be ascribed to the nocturnal–early morning acceleration of southerly flow over southeast China. Linked to the inverse relationship between monsoon intensity and the land–sea thermal contrast, the diurnal cycle of precipitation is strengthened when the SCS monsoon is active and weakened when the land–sea thermal contrast is high. Both the cloud-top radiative cooling effect and the enhanced radiative cooling over inland cloud-free areas also play roles in the development of the morning rainfall peak over Hong Kong.

Description: Episodes of extremely strong northerly winds (known as etesians) during boreal summer can cause hazardous conditions over the Aegean Archipelago (Greece) and represent a threat for the safe design, construction, and operation of wind energy turbines. Here, these extremes are characterized by employing a peak-over-threshold approach in the extended summer season (May–September) from 1989 to 2008. Twelve meteorological stations in the Aegean are used, and results are compared with 6-hourly wind speed data from five ERA-Interim–driven regional climate model (RCM) simulations from the European domain of the Coordinated Regional Climate Downscaling Experiment (EURO-CORDEX). The main findings show that, in the range of wind speeds for the maximum power output of the turbine, the most etesian-exposed stations could operate 90% at a hub height of 80 m. The central and northern Aegean are identified as areas prone to wind hazards, where medium- to high-wind (class II or I according to the International Electrotechnical Committee standards) wind turbines could be more suitable. In the central Aegean, turbines with a cutout wind speed 〉 25 m s−1 are recommended. Overall, RCMs can be considered a valuable tool for investigating wind resources at regional scale. Therefore, this study encourages a broader use of climate models for the assessment of future wind energy potential over the Aegean.

Description: Stratiform liquid-bearing clouds (LBCs), defined herein as either pure liquid or mixed-phase clouds, have a large impact on the surface radiation budget across the Arctic. LBCs lasting at least 6 h are observed at Summit, Greenland, year-round with a maximum in occurrence during summer. Mean cloud-base height is below 1 km for 85% of LBC cases identified, 59% have mean liquid water path (LWP) values between 10 and 40 g m−2, and most produce sporadic light ice-phase precipitation. During their occurrence, the atmosphere above the ice sheet is anomalously warm and moist, with southerly winds observed over much of the ice sheet, including at Summit. LBCs that occur when the North Atlantic Oscillation (NAO) is in the negative phase correspond to strong ridging centered over the Greenland Ice Sheet (GIS), allowing for southwesterly flow over the GIS toward Summit. During the positive phase of the NAO, the occurrence of LBCs corresponds to a cyclone located off the southeastern coast of the ice sheet, which leads to easterly-to-southeasterly flow toward Summit. Furthermore, air parcels at Summit frequently originate from below the elevation of Summit, indicating that orographic lift along the ice sheet is a factor in the occurrence of LBCs at Summit. LBCs are more frequently observed during the negative NAO, and both the LWP and precipitation rate are larger in LBCs occurring during this phase. Mean LWP in LBCs occurring during the negative NAO is 15 g m−2 larger than in LBCs occurring during the positive phase.

Description: This study compares the predictability of surface wind components by linear statistical downscaling using data from both observations and comprehensive models [regional climate models (RCM) and NCEP-2 reanalysis] in three domains: North America (NAM), Europe–Mediterranean Basin (EMB), and East Asia (EAS). A particular emphasis is placed on predictive anisotropy, a phenomenon referring to unequal predictability of surface wind components in different directions. Simulated predictability by comprehensive models is generally close to that found in observations in flat regions of NAM and EMB, but it is overestimated relative to observations in mountainous terrain. Simulated predictability in EAS shows different structures. In particular, there are regions in EAS where predictability simulated by RCMs is lower than that in observations. Overestimation of predictability by comprehensive models tends to occur in regions of low predictability in observations and can be attributed to small-scale physical processes not resolved by comprehensive models. An idealized mathematical model is used to characterize the predictability of wind components. It is found that the signal strength along the direction of minimum predictability is the dominant control on the strength of predictive anisotropy. The biases in the model representation of the statistical relationship between free-tropospheric circulation and surface winds are interpreted in terms of inadequate simulation of small-scale processes in regional and global models, and the primary cause of predictive anisotropy is attributed to such small-scale processes.

Description: A 2-yr cloud microphysical property dataset derived from ground-based remote sensors at the Atmospheric Radiation Measurement site near Barrow, Alaska, was used as input into a radiative transfer model to compute radiative heating rate (RHR) profiles in the atmosphere. Both the longwave (LW; 5–100 μm) and shortwave (SW; 0.2–5 μm) RHR profiles show significant month-to-month variability because of seasonal dependence in the vertical profiles of cloud liquid and ice water contents, with additional contributions from the seasonal dependencies of solar zenith angle, water vapor amount, and temperature. The LW and SW RHR profiles were binned to provide characteristic profiles as a function of cloud type and liquid water path (LWP). Single-layer liquid-only clouds are shown to have larger (10–30 K day−1) LW radiative cooling rates at the top of the cloud layer than single-layer mixed-phase clouds; this is due primarily to differences in the vertical distribution of liquid water between the two classes. However, differences in SW RHR profiles at the top of these two classes of clouds are less than 3 K day−1. The absolute value of the RHR in single-layer ice-only clouds is an order of magnitude smaller than in liquid-bearing clouds. Furthermore, for double-layer cloud systems, the phase and condensed water path of the upper cloud strongly modulate the radiative cooling both at the top and within the lower-level cloud. While sensitivity to cloud overlap and phase has been shown previously, the characteristic RHR profiles are markedly different between the different cloud classifications.

Description: A statistical downscaling algorithm is introduced to forecast surface wind speed at a location. The downscaling algorithm consists of resolved and unresolved components to yield a time series of synthetic wind speeds at high time resolution. The resolved component is a bias-corrected numerical weather prediction model forecast of the 10-m wind speed at the location. The unresolved component is a simulated time series of the high-frequency component of the wind speed that is trained to match the variance and power spectral density of wind observations at the location. Because of the stochastic nature of the unresolved wind speed, the downscaling algorithm may be repeated to yield an ensemble of synthetic wind speeds. The ensemble may be used to generate probabilistic predictions of the sustained wind speed or wind gusts. Verification of the synthetic winds produced by the downscaling algorithm indicates that it can accurately predict various features of the observed wind, such as the probability distribution function of wind speeds, the power spectral density, daily maximum wind gust, and daily maximum sustained wind speed. Thus, the downscaling algorithm may be broadly applicable to any application that requires a computationally efficient, accurate way of generating probabilistic forecasts of wind speed at various time averages or forecast horizons.

Description: Volcanic ash poses an ongoing risk to safety in the airspace worldwide. The accuracy with which volcanic ash dispersion can be forecast depends on the conditions of the atmosphere into which it is emitted. In this study, meteorological ensemble forecasts are used to drive a volcanic ash transport and dispersion model for the 2010 Eyjafjallajökull eruption in Iceland. From analysis of these simulations, the authors determine why the skill of deterministic-meteorological forecasts decreases with increasing ash residence time and identify the atmospheric conditions in which this drop in skill occurs most rapidly. Large forecast errors are more likely when ash particles encounter regions of large horizontal flow separation in the atmosphere. Nearby ash particle trajectories can rapidly diverge, leading to a reduction in the forecast accuracy of deterministic forecasts that do not represent variability in wind fields at the synoptic scale. The flow-separation diagnostic identifies where and why large ensemble spread may occur. This diagnostic can be used to alert forecasters to situations in which the ensemble mean is not representative of the individual ensemble-member volcanic ash distributions. Knowledge of potential ensemble outliers can be used to assess confidence in the forecast and to avoid potentially dangerous situations in which forecasts fail to predict harmful levels of volcanic ash.

Description: A 10-yr geostationary (GEO) overshooting cloud-top (OT) detection database using Multifunction Transport Satellite (MTSAT) Japanese Advanced Meteorological Imager (JAMI) observations has been developed over the Australian region. GEO satellite imagers collect spatially and temporally detailed observations of deep convection, providing insight into the development and evolution of hazardous storms, particularly where surface observations of hazardous storms and deep convection are sparse and ground-based radar or lightning sensor networks are limited. Hazardous storms often produce one or more OTs that indicate the location of strong updrafts where weather hazards are typically concentrated, which can cause substantial impacts on the ground such as hail, damaging winds, tornadoes, and lightning and to aviation such as turbulence and in-flight icing. The 10-yr OT database produced using an automated OT detection algorithm is demonstrated for analysis of storm frequency, diurnally, spatially, and seasonally relative to known features such as the Australian monsoon, expected regions of hazardous storms along the southeastern coastal regions of southern Queensland and New South Wales, and the preferential extratropical cyclone track along the Indian Ocean and southern Australian coast. A filter based on atmospheric instability, deep-layer wind shear, and freezing level was used to identify OTs that could have produced hail. The filtered OT database is used to generate a hail frequency estimate that identifies a region extending from north of Brisbane to Sydney and the Goldfields–Esperance region of eastern Western Australia as the most hail-prone regions.

Description: Decadal prediction is a relatively new branch of climate science that bridges the gap between seasonal climate forecasts and multidecadal-to-century projections of climate change. This paper develops a three-step framework toward the potential application of decadal temperature predictions using the Community Climate System Model, version 4 (CCSM4). In step 1, the predictions are evaluated and it is found that the temperature hindcasts show skill over some regions of the United States and Canada. In step 2, the predictions are manipulated using two methods: a deterministic-anomaly approach (like climate change projections) and a probabilistic tercile-based approach (like seasonal forecasts). In step 3, the predictions are translated by adding a delta (for the anomaly manipulation) and conducting a weighted resample (for the probabilistic manipulation), as well as using a new hybrid method. Using the 2010 initialized hindcast, the framework is demonstrated for predicting 2011–15 over two case-study watersheds [Ottawa (Canada) and Colorado]. For the Colorado watershed, there was a noticeable shift toward higher temperatures, and the delta, weighted resample, and hybrid translations all were better at capturing the observed temperatures than was an approach that used climatological values. For the Ottawa watershed, the observed temperatures over the period of prediction were only subtly different than the climatological values; therefore, the difference between the translation methods was less noticeable. The advantages and disadvantages of the manipulation and translation approaches are discussed, as well as how their use will depend on the user context. The authors emphasize that skill evaluations should be tailored to particular applications and identify additional steps that are needed before the decadal temperature predictions can be readily incorporated into applications.

Description: In atmospheric dispersion models driven by meteorological data from numerical weather prediction (NWP) models, it is necessary to include a parameterization for plume spread that is due to unresolved mesoscale motions. These are motions that are not resolved by the input NWP data but are larger in size than the three-dimensional turbulent motions represented by turbulence parameterizations. Neglecting the effect of these quasi-two-dimensional unresolved mesoscale motions has been shown to lead to underprediction of plume spread and overprediction of concentrations within the plume. NWP modeling is conducted at a range of resolutions that resolve different scales of motion. This suggests that any parameterization of unresolved mesoscale motions should depend on the resolution of the input NWP data. Spectral analysis of NWP data and wind observations is used to assess the mesoscale motions unresolved by the NWP model. Appropriate velocity variances and Lagrangian time scales for these motions are found by calculating the missing variance in the energy spectra and analyzing correlation functions. A strong dependence on the resolution of the NWP data is seen, resulting in larger velocity variances and Lagrangian time scales from the lower-resolution models. A parameterization of unresolved mesoscale motions on the basis of the NWP resolution is proposed.

Description: This paper presents a case study of a strong low-level jet (LLJ) that was observed about 20 km off the coast of Ocean City, Maryland, during a measurement campaign in the summer of 2013. Doppler wind lidar observations offshore, together with analyses of 4-km WRF Model data and NARR data, are used to reconstruct the forcing mechanisms that led to the growth and rapid collapse of the jet offshore as well as to differentiate the forcing mechanisms resulting in an LLJ farther inland. It was observed that the LLJ over the mid-Atlantic coastal plain decreased gradually throughout the early morning hours relative to the LLJ along the coastal ocean as a downslope wind moved eastward from the Appalachian Mountains. The forcing of the LLJ was a result of both thermal and mechanical mechanisms linked to the topography, while synoptic forcing from an approaching cold front led to a downslope wind. Data from a wind profiler near Cambridge, Maryland, also showed an LLJ, but forced by different regional conditions, emphasizing the difficulties of inferring wind conditions offshore from onshore observations. The sudden breakdown of the jet offshore appears to have been a result of an interaction with a downslope wind from the Appalachian Mountains. This particular case study highlights the 1) importance of both large-scale and regional forcing, 2) impact that topographical forcing farther inland had on offshore wind, and 3) different responses in the wind profile as a downslope wind moved across the mid-Atlantic region.

Description: Weather and climate variability strongly influence the people, infrastructure, and economy of Alaska. However, the sparse observational network in Alaska limits our understanding of meteorological variability, particularly of precipitation processes that influence the hydrologic cycle. Here, a new 14-yr (September 2002–August 2016) dataset for Alaska with 4-km grid spacing is described and evaluated. The dataset, generated with the Weather Research and Forecasting (WRF) Model, is useful for gaining insight into meteorological and hydrologic processes, and provides a baseline against which to measure future environmental change. The WRF fields are evaluated at annual, seasonal, and daily time scales against observation-based gridded and station records of 2-m air temperature, precipitation, and snowfall. Pattern correlations between annual mean WRF and observation-based gridded fields are r = 0.89 for 2-m temperature, r = 0.75 for precipitation, r = 0.82 for snow-day fraction, r = 0.55 for first snow day of the season, and r = 0.71 for last snow day of the season. A shortcoming of the WRF dataset is that spring snowmelt occurs too early over a majority of the state, due partly to positive 2-m temperature biases in winter and spring. Strengths include an improved representation of the interannual variability of 2-m temperature and precipitation and accurately simulated (relative to regional station observations) winter and summer precipitation maxima. This initial evaluation suggests that the 4-km WRF climate dataset robustly simulates meteorological processes and recent climatic variability in Alaska. The dataset may be particularly useful for applications that require high-temporal-frequency weather fields, such as driving hydrologic or glacier models. Future studies will provide further insight on its ability to represent other aspects of Alaska’s climate.

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 used the first detailed radar measurements of the vertical structure of precipitation obtained in the central Andes of southern Peru and Bolivia to investigate the diurnal cycle and vertical structure of precipitation and melting-layer heights in the tropical Andes. Vertically pointing 24.1-GHz Micro Rain Radars in Cusco, Peru (3350 m MSL, August 2014–February 2015), and La Paz, Bolivia (3440 m MSL, October 2015–February 2017), provided continuous 1-min profiles of reflectivity and Doppler velocity. The time–height data enabled the determination of precipitation timing, melting-layer heights, and the identification of convective and stratiform precipitation features. Rawinsonde data, hourly observations of meteorological variables, and satellite and reanalysis data provided additional insight into the characteristics of these precipitation events. The radar data revealed a diurnal cycle with frequent precipitation and higher rain rates in the afternoon and overnight. Short periods with strong convective cells occurred in several storms. Longer-duration events with stratiform precipitation structures were more common at night than in the afternoon. Backward air trajectories confirmed previous work indicating an Amazon basin origin of storm moisture. For the entire dataset, median melting-layer heights were above the altitude of nearby glacier termini approximately 17% of the time in Cusco and 30% of the time in La Paz, indicating that some precipitation was falling as rain rather than snow on nearby glacier surfaces. During the 2015–16 El Niño, almost half of storms in La Paz had melting layers above 5000 m MSL.

Description: The ability of cool roofs and vegetation to reduce urban temperatures and improve human thermal stress during heat wave conditions is investigated for the city of Melbourne, Australia. The Weather Research and Forecasting Model coupled to the Princeton Urban Canopy Model is employed to simulate 11 scenarios of cool roof uptake across the city, increased vegetation cover across the city, and a combination of these strategies. Cool roofs reduce urban temperatures during the day, and, if they are installed across enough rooftops, their cooling effect extends to the night. In contrast, increasing vegetation coverage reduces nighttime temperatures but results in minimal cooling during the hottest part of the day. The combination of cool roofs and increased vegetation scenarios creates the largest reduction in temperature throughout the heat wave, although the relationship between the combination scenarios is nonsynergistic. This means that the cooling occurring from the combination of both strategies is either larger or smaller than if the cooling from individual strategies were to be added together. The drier, lower-density western suburbs of Melbourne showed a greater cooling response to increased vegetation without enhancing human thermal stress due to the corresponding increase in humidity. The leafy medium-density eastern suburbs of Melbourne showed a greater cooling response to the installation of cool roofs. These results highlight that the optimal urban cooling strategies can be different across a single urban center.

Description: Unusually hot weather is a major concern to public health as well as other systems (e.g., ecological, economical, energy). This study utilized spatially continuous and homogenized observational surface climate data to examine changes in the regularity of heat waves in the continental United States. This included the examination of heat waves according only to daytime temperatures, nighttime temperatures, and both daytime and nighttime temperatures. Results confirmed a strong increase in the prevalence of heat waves between the mid-1970s and the dataset end (2015), and that increase was preceded by a mild decrease since the dataset beginning (1948). Results were unclear whether the prevalence of nighttime or simultaneous daytime–nighttime heat waves increased the most, but it was clear that increases were largest in the summer. The largest gains occurred in the West and Southwest, and a “warming hole” was most conspicuous in the northern Great plains. The changes in heat wave prevalence were similar to changes in the mean temperatures, and more so in the daytime heat waves. Daytime and nighttime heat waves coincided with one another more frequently in recent years than they did in the 1970s. Some parts of the United States (West Coast) were more likely than other parts to experience daytime and nighttime heat waves simultaneously. While linear trends were not sensitive to the climate dataset, trend estimation method, or heat wave definition, they were mildly sensitive to the start and end dates and extremely sensitive to the climate base period method (fixed in time or directly preceding any given heat wave).

Description: To study regional-scale carbon dioxide (CO2) transport, temporal variability, and budget over the Southern California Air Basin (SoCAB) during the California Research at the Nexus of Air Quality and Climate Change (CalNex) 2010 campaign period, a model that couples the Weather Research and Forecasting (WRF) Model with the Vegetation Photosynthesis and Respiration Model (VPRM) has been used. Our numerical simulations use anthropogenic CO2 emissions of the Hestia Project 2010 fossil-fuel CO2 emissions data products along with optimized VPRM parameters at “FLUXNET” sites, for biospheric CO2 fluxes over SoCAB. The simulated meteorological conditions have been validated with ground and aircraft observations, as well as with background CO2 concentrations from the coastal Palos Verdes site. The model captures the temporal pattern of CO2 concentrations at the ground site at the California Institute of Technology in Pasadena, but it overestimates the magnitude in early daytime. Analysis of CO2 by wind directions reveals the overestimate is due to advection from the south and southwest, where downtown Los Angeles is located. The model also captures the vertical profile of CO2 concentrations along with the flight tracks. The optimized VPRM parameters have significantly improved simulated net ecosystem exchange at each vegetation-class site and thus the regional CO2 budget. The total biospheric contribution ranges approximately from −24% to −20% (daytime) of the total anthropogenic CO2 emissions during the study period.

Description: Using cyclone-centered compositing and a database of extratropical-cyclone locations, the distribution of precipitation frequency and rate in oceanic extratropical cyclones is analyzed using satellite-derived datasets. The distribution of precipitation rates retrieved using two new datasets, the Global Precipitation Measurement radar–microwave radiometer combined product (GPM-CMB) and the Integrated Multisatellite Retrievals for GPM product (IMERG), is compared with CloudSat, and the differences are discussed. For reference, the composites of AMSR-E, GPCP, and two reanalyses are also examined. Cyclone-centered precipitation rates are found to be the largest with the IMERG and CloudSat datasets and lowest with GPM-CMB. A series of tests is conducted to determine the roles of swath width, swath location, sampling frequency, season, and epoch. In all cases, these effects are less than ~0.14 mm h−1 at 50-km resolution. Larger differences in the composites are related to retrieval biases, such as ground-clutter contamination in GPM-CMB and radar saturation in CloudSat. Overall the IMERG product reports precipitation more often, with larger precipitation rates at the center of the cyclones, in conditions of high precipitable water (PW). The CloudSat product tends to report more precipitation in conditions of dry or moderate PW. The GPM-CMB product tends to systematically report lower precipitation rates than the other two datasets. This intercomparison provides 1) modelers with an observational uncertainty and range (0.21–0.36 mm h−1 near the cyclone centers) when using composites of precipitation for model evaluation and 2) retrieval-algorithm developers with a categorical analysis of the sensitivity of the products to PW.

Description: This research is Part II of a two-part study that evaluates the ability of image-processing and select machine-learning algorithms to detect, classify, and track midlatitude mesoscale convective systems (MCSs) in radar-reflectivity images for the conterminous United States. This paper focuses on the tracking portion of this framework. Tracking is completed through a two-step process using slice (snapshots of instantaneous MCS intensity) data generated in Part I. The first step is to perform spatiotemporal matching, which associates slices through temporally adjacent radar-reflectivity images to generate swaths, or storm tracks. When multiple slices are found to be matches, a difference-minimization procedure is used to associate the most similar slice with the existing swath. Once this step is completed, a second step combines swaths that are spatiotemporally close. Tracking performance is assessed by calculating select metrics for all available swath-building perturbations to determine the optimal approach in tracking. Frequency maps and time series generated from the swaths suggest that the spatiotemporal occurrence of these swaths is reasonable as determined from previous work. Further, these events exhibit a diurnal cycle that is distinct from that of overall convection for the conterminous United States. Last, machine-learning predictions are found to limit areas of high MCS frequency to the central and eastern Great Plains.

Description: Low-level-jet (LLJ) periods are investigated by exploiting a long-term record of ground-based remote sensing Doppler wind lidar measurements supported by tower observations and surface flux measurements at the Jülich Observatory for Cloud Evolution (JOYCE), a midlatitude site in western Germany. LLJs were found 13% of the time during continuous observations over more than 4 yr. The climatological behavior of the LLJs shows a prevailing nighttime appearance of the jets, with a median height of 375 m and a median wind speed of 8.8 m s−1 at the jet nose. Significant turbulence below the jet nose only occurs for high bulk wind shear, which is an important parameter for describing the turbulent characteristics of the jets. The numerous LLJs (16% of all jets) in the range of wind-turbine rotor heights below 200 m demonstrate the importance of LLJs and the associated intermittent turbulence for wind-energy applications. Also, a decrease in surface fluxes and an accumulation of carbon dioxide are observed if LLJs are present. A comprehensive analysis of an LLJ case shows the influence of the surrounding topography, dominated by an open pit mine and a 200-m-high hill, on the wind observed at JOYCE. High-resolution large-eddy simulations that complement the observations show that the spatial distribution of the wind field exhibits variations connected with the orographic flow depending on the wind direction, causing high variability in the long-term measurements of the vertical velocity.

Description: The Short-Range Ensemble Forecast (SREF) system is verified and bias corrected for fire weather days (FWDs) defined as having an elevated probability of wildfire occurrence using a statistical Fire Weather Index (FWI) over a subdomain of the northeastern United States (NEUS) between 2007 and 2014. The SREF is compared to the Rapid Update Cycle and Rapid Refresh analyses for temperature, relative humidity, specific humidity, and the FWI. An additive bias correction is employed using the most recent previous 14 days [sequential bias correction (SBC)] and the most recent previous 14 FWDs [conditional bias correction (CBC)]. Synoptic weather regimes on FWDs are established using cluster analysis (CA) on North American Regional Reanalysis sea level pressure, 850-hPa temperature, 500-hPa temperature, and 500-hPa geopotential height. SREF severely underpredicts FWI (by two indices at FWI = 3) on FWDs, which is partially corrected using SBC and largely corrected with CBC. FWI underprediction is associated with a cool (ensemble mean error of −1.8 K) and wet near-surface model bias (ensemble mean error of 0.46 g kg−1) that decreases to near zero above 800 hPa. Although CBC improves reliability and Brier skill scores on FWDs, ensemble FWI values exhibit underdispersion. CA reveals three synoptic weather regimes on FWDs, with the largest cool and wet biases associated with a departing surface low pressure system. These results suggest the potential benefit of an operational analog bias correction on FWDs. Furthermore, CA may help elucidate model error during certain synoptic weather regimes.

Description: We report on measurements of drop size distributions (DSD) using collocated instruments (a Droplet Measurement Technologies, Inc., Meteorological Particle Spectrometer and a 2D-video disdrometer) from two locations with different rainfall climates (Greeley, Colorado, and Huntsville, Alabama, with measurements from the latter that include the outer rainbands of Hurricane Irma). The combination of the two instruments gives what we term as the “full” DSD spectra, the shape of which generally cannot be represented by the standard gamma model, but instead requires the additional flexibility of the generalized gamma model, which includes two shape parameters (μ and c). The double-moment normalization of DSDs using the third and fourth moments is used to arrive at the intrinsic shapes of the DSD with two shape parameters that are shown to capture simultaneously the drizzle mode as well as the precipitation mode, together with a “plateau” region between the two. The estimation of μ and c is done with a global search using nonlinear least squares, and the error residuals are examined to check the sensitivity of the parameters to a preselected, allowed tolerance around the minimum error in the μ, c plane. This leads to a range of plausible fits for a given normalized DSD mainly governed by the c parameter. The stability or invariance of the shape of the normalized DSDs from the two sites is examined, and on average the shapes are similar with some variability at the large normalized diameter end that is explained by the aforementioned range of plausible fits. Heuristic goodness-of-fit methods are described that demonstrate that the generalized gamma model outperforms the standard gamma model with only one shape parameter (μ).

Description: This research evaluates the ability of image-processing and select machine-learning algorithms to identify midlatitude mesoscale convective systems (MCSs) in radar-reflectivity images for the conterminous United States. The process used in this study is composed of two parts: segmentation and classification. Segmentation is performed by identifying contiguous or semicontiguous regions of deep, moist convection that are organized on a horizontal scale of at least 100 km. The second part, classification, is performed by first compiling a database of thousands of precipitation clusters and then subjectively assigning each sample one of the following labels: 1) midlatitude MCS, 2) unorganized convective cluster, 3) tropical system, 4) synoptic system, or 5) ground clutter and/or noise. The attributes of each sample, along with their assigned label, are used to train three machine-learning algorithms: random forest, gradient boosting, and “XGBoost.” Results using a testing dataset suggest that the algorithms can distinguish between MCS and non-MCS samples with a high probability of detection and low probability of false detection. Further, the trained algorithm predictions are well calibrated, allowing reliable probabilistic classification. The utility of this two-step procedure is illustrated by generating spatial frequency maps of automatically identified precipitation clusters that are stratified by using various reflectivity and probabilistic prediction thresholds. These results suggest that machine learning can add value by limiting the amount of false-positive (non-MCS) samples that are not removed by segmentation alone.

Description: The wind stress formulation in an atmospheric model over shallow waters is investigated using year-long observations of the wind profile within the first 100 m of the atmosphere and mesoscale simulations. The model experiments use a range of planetary boundary layer parameterizations to quantify the uncertainty related to the turbulent closure assumptions and thus to isolate the dominant influence of the surface roughness formulation. Results indicate that a positive wind speed bias exists when common open-ocean formulations for roughness are adopted for a region with a water depth of 30 m. Imposition of a wind stress formulation that is consistent with previous shallow-water estimates is necessary to reconcile model wind speeds with observations, providing modeling evidence that supports the increase of surface drag over shallow waters. The possibility of including water depth in the parameterization of roughness length is examined.

Description: Accurate estimation of passive microwave land surface emissivity (LSE) is crucial for numerical weather prediction model data assimilation, for microwave retrievals of land precipitation and atmospheric profiles, and for a better understanding of land surface and subsurface characteristics. In this study, global instantaneous LSE is estimated for a 9-yr period from the Advanced Microwave Scanning Radiometer for Earth Observing System (AMSR-E) and for a 5-yr period from the Advanced Microwave Scanning Radiometer 2 (AMSR2) sensors. Estimates of LSE from both sensors were obtained by using an updated algorithm that minimizes the discrepancy between the differences in penetration depths from microwave and infrared remote sensing observations. Concurrent ancillary datasets such as skin temperature from the Moderate Resolution Imaging Spectroradiometer (MODIS) and profiles of air temperature and humidity from the Atmospheric Infrared Sounder are used. The latest collection 6 of MODIS skin temperature is used for the LSE estimation, and the differences between collections 6 and 5 are also comprehensively assessed. Analyses reveal that the differences between these two versions of infrared-based skin temperatures could lead to approximately a 0.015 difference in passive microwave LSE values, especially in arid regions. The comparison of global mean LSE features from the combined use of AMSR-E and AMSR2 with an independent product—Tool to Estimate Land Surface Emissivity from Microwave to Submillimeter Waves (TELSEM2)—shows spatial pattern correlations of order 0.92 at all frequencies. However, there are considerable differences in magnitude between these two LSE estimates, possibly because of differences in incidence angles, frequencies, observation times, and ancillary datasets.

Description: Chaff is a radar countermeasure typically used by military branches in training exercises around the United States. Chaff within view of the S-band WSR-88D beam can appear prominently on radar users’ displays. Knowledge of chaff characteristics is useful for radar users to discriminate between chaff and weather echoes and for automated algorithms to do the same. The WSR-88D network provides dual-polarimetric capabilities across the United States, leading to the collection of a large database of chaff cases. This database is analyzed to determine the characteristics of chaff in terms of the reflectivity factor and polarimetric variables on large scales. Particular focus is given to the dynamics of differential reflectivity ZDR in chaff and its dependence on height. In contrast to radar observations of chaff for a single event, this study is able to reveal a repeatable and new pattern of radar chaff observations. A discussion about the observed characteristics is presented, and hypotheses for the observed ZDR dynamics are put forth.

Description: Heat waves are projected to increase in magnitude and frequency throughout this century because of increasing global temperatures, making it critically important to acquire improved understanding of their genesis and interactions with large cities. This study presents an application of the method of factor separation to assess combined impacts of a synoptic-scale heat wave, urban land cover, and urban energy and momentum fluxes on temperatures and winds over New York City, New York, via use of high-resolution simulations (1-km grid spacing) with an urbanized version of the Weather Research and Forecasting (WRF) Model. Results showed that factors behaved different throughout the day, with synoptic conditions dominating afternoon temperature contributions (〉7°C). At night, combined urban surface factors contributed over 5°C during the heat wave and up to 1.5°C on non-heat-wave days. Positive interactions among all factors during morning and nighttime indicate an amplification of the urban heat island of up to 4°C during the heat wave. Midtown Manhattan vertical cross sections, where urban canopies are most dense, showed a change in the sign (from positive to negative) of the contribution of the urban fluxes between night and day below 500 m, possibly as a result of decreased radiative cooling from trapping by buildings and increased thermal storage by buildings as well as frictional effects that oppose the incoming warm air.

Description: A 17-yr “climatology” of tropical-system activity, track, size, and 24-h intensity change in the southwest Indian Ocean (SWIO) is developed and analyzed in comparison with other intensively studied basins such as the North Atlantic Ocean. A first formulation of the empirical maximum potential intensity of SWIO tropical systems is also proposed, along with the climatology of sea surface temperatures from September to June. Systems with a 34-kt (1 kt = 0.514 m s−1) wind radius that does not exceed 46 km are considered to be very small or midget systems, on the basis of the 5th percentile of storm size distribution. Using the 95th percentile of overwater intensity changes, rapid intensification (RI) is statistically defined by a minimum increase of 15.4 m s−1 day−1 in the maximum 10-min mean surface wind speed (VMAX). This value is similar to the 30-kt threshold commonly used in the North Atlantic basin for 1-min sustained wind speeds. Rapid decay (RD) can be statistically defined by a minimum weakening of 13.9 m s−1 day−1, although the spread in the 5th percentile of intensity changes among the different intensity classes indicates that it is not as appropriate to use a unique RD threshold for all systems. It is shown that 43% of all tropical systems and all very intense tropical cyclones (VMAX ≥ 59.6 m s−1) underwent RI at least once during their lifetimes. It is highlighted that systems have a greater propensity to intensify rapidly for an initial intensity between 65 and 75 kt. Statistics indicate that operational intensity forecast errors are significantly greater at short range for RI cases while track errors are reduced.

Description: The Ontario Winter Lake-Effect Systems (OWLeS) field campaign during the winter season of 2013/14 provided unprecedented data with regard to the structure and behavior of long-lake-axis-parallel (LLAP) lake-effect storms. One of the interesting characteristics of LLAP storm bands is their ability to initiate lightning. The OWLeS datasets provide an opportunity to examine more thoroughly the kinematics and microphysics of lake-effect thunder-snowstorms than ever before. The OWLeS facilities and field personnel observed six lake-effect thunderstorms during December–January 2013/14. Most of them produced very little lightning (fewer than six cloud-to-ground strokes or intracloud pulses recorded by the National Lightning Detection Network). The 7 January 2014 storm had over 50 strokes and pulses, however, which resulted in 20 flashes over a 6-h period (0630–1230 UTC), making it the most electrically active storm during the field campaign. Relative to the 18 December 2013 storm, which only had three flashes, the 7 January 2014 case had a deeper boundary layer and greater instability. Also, 45% of the lightning during the 7 January storm was likely due to flashes initiated by wind turbines or other man-made antennas, along with all of the lightning observed during 18 December. No lightning was documented over Lake Ontario, the primary source of instability for these storms.

Description: Inference about time series in weather data can be made in several ways. Current practice focuses on computing summary measures, such as mean and variance, or constructing a reference year from small subsets of data derived from multiple years. Some applications require the selection of an instance of observed data over a fixed time frame, typically a year or more, for modeling. In addition, many current methods do not include rainfall as a parameter of interest. This paper reviews and refines existing methods for determining a reference year by creating a metric that measures the (abstract) distance between observed patterns of rainfall. The reference year is then chosen from a group of potential reference years. This method is computationally efficient, easily explained, and robust against differences in the index of reporting, to include leap years. Application of the distance metric to data from Philadelphia, Pennsylvania, and Norfolk, Virginia, shows that it appropriately identifies not only the years that are most typical for a location, but extreme years as well. Both are particularly useful in applications related to urban hydrology, which formed the basis for development of this method. Results also demonstrate that the proposed method is functionally different than existing methods. The distance metric represents an evolutionary step forward, overcomes some difficulties present from other approaches, and would be applicable to a number of cross-disciplinary applications.

Description: Turbulent wind data measured by sonic anemometers installed at various heights on a 140-m-tall micrometeorological tower located at a coastal site are used to obtain vertical profiles of the velocity standard deviations σi, Lagrangian decorrelation local time scales TLi, and eddy diffusivities Kα for distinct stability conditions. The novelty of the study lies in the use of turbulent data directly measured over the extension of the atmospheric surface layer at a coastal site for that purpose. Furthermore, the approach employs the Hilbert–Huang transform to determine the wind energy spectral peak frequencies. These are applied to the asymptotic spectral equation from Taylor statistical diffusion theory to obtain the turbulent dispersion parameters, which are shown to generally agree well with those provided by a classical autocorrelation approach. For neutral and stable situations the vertical profiles of momentum eddy diffusivities agree well with those derived from the spectral and autocorrelation method. Additionally, the turbulent integral time scales and eddy diffusivities determined by the method at a coastal location are found to overestimate those predicted from analytical expressions based on continental field observations. The turbulence parameters found are suitable to be employed in air pollution dispersion models.

Description: Recent improvements to an algorithm to be used operationally for downscaling screen temperatures from numerical weather prediction models are described. Testing against very high resolution dynamically downscaled screen temperatures and intensive field measurements taken during the Cold-Air Pooling Experiment (COLPEX) is performed. The improvements are based on a physical understanding of the processes involved in the formation of cold-air pools (CAPs) that is informed by recent research. The algorithm includes a parameterization of sidewall sheltering effects that lead to lower temperatures in valley-bottom CAPs on clear, calm nights. Advection and adjustment over exposed hilltops results in higher screen temperatures than on flat ground but lower temperatures relative to the free air above the valley at the same elevation, and a treatment of this effect has also been developed. These processes form the major contributions to the often dramatic small-scale variations in temperature in complex terrain in stable boundary layer conditions, even when height variation is fairly shallow. The improvements result in qualitatively better reproduction of subgrid temperature patterns in complex terrain during CAPs. Statistical forecast errors are subsequently improved.

Description: Dynamical downscaling (DDS) was conducted over Japan by using a regional atmospheric model with reanalysis data to investigate the rainfall duration bias over Kyushu, Japan, in July and August from 2006 to 2015. The model results showed that DDS had a positive rainfall duration bias over Kyushu and a dry bias over almost all of Kyushu, which were emphasized for extreme rainfall events. Investigated was the rainfall duration bias for heavy rainfall days, accompanied by synoptic-scale forcing, in which daily precipitation exceeded 30 mm day−1 and covered over 20% of the Kyushu area. Heavy rainfall days were sampled from observed rainfall data that were based on rain gauge and radar observations. A set of daily climatic variables of horizontal wind and equivalent potential temperature at 850 hPa and sea level pressure, around southwestern Japan, corresponding to the sampled dates, was selected to conduct a self-organizing map (SOM) and K-means method. The SOM and K-means method objectively classified three synoptic patterns related to heavy rainfall over Kyushu: strong monsoon, weak monsoon, and typhoon patterns. Rainfall duration had a positive bias in western Kyushu for the strong monsoon pattern and a positive bias in southern and east-coast Kyushu for the typhoon pattern, whereas there was little rainfall duration bias in the weak monsoon pattern. The bias for the typhoon pattern was related to rainfall events with a strong rainfall peak. The results suggest that bias correction for rainfall duration would be required for accurately estimating direct runoff in a catchment area in addition to the precipitation amount.

Description: Canadian Global Environmental Multiscale (GEM) numerical model output was compared with the meteorological data from an enhanced observational network to investigate the model’s ability to predict Lake Ontario lake breezes and their characteristics for two cases in the Greater Toronto Area—one in which the large-scale wind opposed the lake breeze and one in which it was in the same direction as the lake breeze. An enhanced observational network of surface meteorological stations, a C-band radar, and two Doppler wind lidars were deployed among other sensors during the 2015 Pan and Parapan American Games in Toronto. The GEM model was run for three nested domains with grid spacings of 2.5, 1, and 0.25 km. Comparisons between the model predictions and ground-based observations showed that the model successfully predicted lake breezes for the two events. The results indicated that using GEM 1 and 0.25 km increased the forecast accuracy of the lake-breeze location, updraft intensity, and depth. The accuracy of the modeled lake breeze timing was approximately ±135 min. The model underpredicted the surface cooling caused by the lake breeze. The GEM 0.25-km model significantly improved the temperature forecast accuracy during the lake-breeze circulations, reducing the bias by up to 72%, but it mainly underpredicted the moisture and overpredicted the surface wind speed. Root-mean-square errors of wind direction forecasts were generally high because of large biases and high variability of errors.

Description: Series of aerosol transport hindcasts for West Africa were conducted using the Weather Research and Forecasting (WRF) Model coupled to chemistry within the NASA-Unified WRF (NU-WRF) framework. The transport of biomass-burning aerosols in April and December 2009 was investigated over two types of simulation domains. One-month simulations with 9-km grid spacing for April or December 2009 covered most of North and West Africa and were evaluated by comparison with measurements of the total-column aerosol optical depth, Ångström exponent, and horizontal wind components at various pressure levels. The horizontal wind components at 700 hPa were identified as key factors in determining the transport patterns of biomass-burning aerosols from sub-Saharan West Africa to the Sahel. The vertical accumulation of biomass-burning aerosols close to 700 hPa was demonstrated in 1-day simulations with 1-km horizontal grid spacing. A new simple parameterization for the effects of heat release by biomass burning was designed for this resolution and tested together with the conventional parameterization based on fixed smoke injection heights. The aerosol vertical profiles were somewhat sensitive to the selection of parameterization, except for cases with the assumption of excessive heating by biomass burning. The new parameterization works reasonably well and offers flexibility to relate smoke transport to biomass-burning plume rise that can be correlated with the satellite fire radiative power measurements, which is advantageous relative to the conventional parameterization.

Description: Analyses of the radar-observed structure and derived rainfall statistics of warm-season convection developing columns of enhanced positive differential reflectivity ZDR over England’s southwest peninsula are presented here. Previous observations of ZDR columns in developing cumulonimbus clouds over England were rare. The observations presented herein suggest otherwise, at least in the southwesterly winds over the peninsula. The results are the most extensive of their kind in the United Kingdom; the data were collected using the National Centre for Atmospheric Science dual-polarization X-band radar (NXPol) during the Convective Precipitation Experiment (COPE). In contrast to recent studies of ZDR columns focused on deep clouds that developed in high-instability environments, the COPE measurements show relatively frequent ZDR columns in shallower clouds, many only 4–5 km deep. The presence of ZDR columns is used to infer that an active warm rain process has contributed to precipitation evolution in convection deep enough for liquid and ice growth to take place. Clouds with ZDR columns were identified objectively in three COPE deployments, with both discrete convection and clouds embedded in larger convective complexes developing columns. Positive ZDR values typically extended to 1–1.25 km above 0°C in the columns, with ZDR ≥ 1 dB sometimes extending nearly 4 km above 0°C. Values above 3 dB typically occurred in the lowest 500 m above 0°C, with coincident airborne measurements confirming the presence of supercooled raindrops. Statistical analyses indicated that the convection that produced ZDR columns was consistently associated with the larger derived rainfall rates when compared with the overall convective population sampled by the NXPol during COPE.

Description: Forecast climatologies are used to remove systematic errors from forecasts and to express forecasts as departures from normal. Forecast climatologies are computed from hindcasts by various averaging, smoothing, and interpolation procedures. Here the Climate Forecast System, version 2 (CFSv2), monthly forecast climatology provided by the NCEP Environmental Modeling Center (EMC) is shown to be biased in the sense of systematically differing from the hindcasts that are used to compute it. These biases, which are unexpected, are primarily due to fitting harmonics to hindcast data that have been organized in a particular format, which on careful inspection is seen to introduce discontinuities. Biases in the monthly near-surface temperature forecast climatology reach 2°C over North America for March targets and start times at the end of January. Biases in the monthly Niño-3.4 forecast climatology are also largest for start times near calendar-month boundaries. A further undesirable consequence of this fitting procedure is that the EMC forecast climatology varies discontinuously with lead time for fixed target month. Two alternative methods for computing the forecast climatology are proposed and illustrated. The proposed methods more accurately fit the hindcast data and provide a clearer representation of the CFSv2 model climate drift toward lower Niño-3.4 values for starts in March and April and toward higher Niño-3.4 values for starts in June, July, and August.

Description: Hydrologic extremes of drought and flooding stress water resources and damage communities in the Red River basin, located in the south-central United States. For example, the summer of 2011 was the third driest summer in Oklahoma state history and the driest in Texas state history. When the long-term drought conditions ended in the spring of 2015 as El Niño brought record precipitation to the region, there were also catastrophic floods that caused loss of life and property. Hydrologic extremes such as these have occurred throughout the historical record, but decision-makers need to know how the frequency of these events is expected to vary in a changing climate so that they can mitigate these impacts and losses. Therefore, the goals of this study focus on how these hydrologic extremes impact water resources in the Red River basin, how the frequency of such events is expected to change in the future, and how this study can aid local water-resource managers and decision-makers. Heavy-precipitation events were defined at the historical 90th and 99th percentiles, and severe-drought events were identified at a threshold of the standardized precipitation evapotranspiration index’s value of less than or equal to −1. The results show an increase in the frequency of severe-drought events in the western Red River basin and a rise in heavy-rainfall events in the east by the end of the century, especially under RCP 8.5. Therefore, decision-makers and water-resource managers will likely need to prepare for both hydrologic extremes depending on their location within the basin.

Description: Synoptic-scale atmospheric conditions play a critical role in determining the frequency and intensity of snow-cover-ablation events. Using a synoptic weather-classification technique, distinct regional circulation patterns influencing the Great Lakes basin of North America are identified and examined in conjunction with daily snow-ablation events from 1960 to 2009. An ablation event is considered in this study to be an interdiurnal decrease in areal-weighted average snow depth of greater than 2.54 cm in magnitude over the entire Great Lakes basin. General meteorological characteristics associated with ablation-causing synoptic types are examined, and three individual case studies from prominent synoptic types are presented to understand the diversity of meteorological influences on regional snow ablation. Results indicate that a variety of synoptic weather conditions lead to snow ablation in the Great Lakes basin. The 10 most common synoptic types accounted for 66% of the 349 ablation events detected from 1960 to 2009. Snow ablation in the Great Lakes basin most commonly occurs when there is advection of warm and moist air into the region to provide the sensible and latent heat fluxes that are needed for melt, but ablation frequently occurs during rain-on-snow events and in instances of high pressure overhead. Ablation magnitude is highest during rain-on-snow synoptic types, and the interannual frequency of these types significantly decreased by 37% over 1960–2009. Conversely, the frequency of high-pressure-overhead synoptic types significantly increased by more than 30% from 1960 to 2009. Such changes may influence the hydrologic impact of these synoptic types on ablation over time.

Description: Spatiotemporal precipitation patterns were investigated on the western slopes of the central Andes Mountains by applying EOF and cluster analysis as well as the Weather Research and Forecasting (WRF) Model. In the semiarid catchment area in the highlands of Lima, Peru, the precipitation is assumed to be a cross-scale interplay of large-scale dynamics, varying sea surface temperatures (SSTs), and breeze-dominated slope flows. The EOF analysis was used to encompass and elucidate the upper-level circulation patterns dominating the transport of moisture. To delineate local precipitation regimes, a partitioning cluster analysis was carried out, which additionally should illustrate local effects such as the altitudinal gradient of the Andes. The results demonstrated that especially during the transition to the dry season, synoptic-scale circulation aloft controls the precipitation (correlation coefficients between 0.6 and 0.9), whereas in the remaining seasons the slope breezes due to the altitudinal gradient mainly determine the precipitation behavior. Further analysis with regard to the spatiotemporal precipitation variability revealed an inversion of the precipitation distribution along the elevational gradient within the study area, mainly during February (29%) and March (35%), that showed correlations with coastal SST patterns ranging between 0.56 and 0.67. WRF simulations of the underlying mechanisms disclosed that the large-scale circulation influences the thermally induced upslope flows while the strength of southeastern low-level winds related to the coastal SSTs caused a blocking of easterlies in the middle troposphere through a reduced anticyclonic effect. This interplay enables the generation of precipitation in the usually drier environment at lower elevations, which leads to a decrease in rainfall with increasing elevation.

Description: Using state-level monthly heating degree-day data, reconstructed per capita natural gas (NGr) consumption records for each state of the continental United States were calculated for 1895–2014 using linear regressions. The regressed monthly NGr values estimate the effects of twentieth- and early twenty-first-century climate variation on per capita natural gas usage, assuming a modern (1990–2013) consumption environment. Using these extended consumption records, the hypothetical effects of climate on past, current, and future natural gas (NG) use are estimated. By controlling for nonclimatic consumption effects, these extended reconstructions provide estimates of the sensitivity of NG consumption to historical climate variation, particularly long-term warming trends, occurring before the period of available consumption records. After detrending, the reconstructions are used to form improved estimates of interannual NG variation under current climate conditions. Given estimates of each state’s current consumption climatology and long-term trends in per capita consumption and current population trends, the net effect of warming and increasing population on future consumption is estimated. Significant long-term negative trends in per capita NG consumption are found in western and northeastern states and in Florida, while southeastern consumption effects reflect a multidecadal temperature cycle. Climate-related consumption effects found here are generally consistent with previous studies, with long-term trend effects limited to less than 12% and multidecadal regime effects limited to less than 9%. Given the stronger positive effects of increasing population on total state natural gas consumption, reduced per capita use associated with warming trends has a weak moderating effect on estimates of projected total consumption in 2043.

Description: During the North American monsoon global positioning system (GPS) Transect Experiment 2013, daily convective-permitting WRF simulations are performed in northwestern Mexico and the southern Arizona border region using the operational Global Forecast System (GFS) and North American Mesoscale Forecast System (NAM) models as lateral boundary forcing and initial conditions. Compared to GPS precipitable water vapor (PWV), the WRF simulations display a consistent moist bias in the initial specification of PWV leading to convection beginning 3–6 h early. Given appreciable observed rainfall, days are classified as strongly and weakly forced based only on the presence of an inverted trough (IV); gulf surges did not noticeably impact the development of mesoscale convective systems (MCSs) and related convection in northwestern Mexico. Strongly forced days display higher modeled precipitation forecast skill than weakly forced days in the slopes of the northern Sierra Madre Occidental (SMO) away from the crest, especially toward the west where MCSs account for the greatest proportion of all monsoon-related precipitation. A case study spanning 8–10 July 2013 illustrates two consecutive days when nearly identical MCSs evolved over northern Sonora. Although a salient MCS is simulated on the strongly forced day (9–10 July 2013) when an IV is approaching the core monsoon region, a simulated MCS is basically nonexistent on the weakly forced day (8–9 July 2013) when the IV is farther away. The greater sensitivity to the initial specification of PWV in the weakly forced day suggests that assimilation of GPS-derived PWV for these types of days may be of greatest value in improving model precipitation forecasts.

Description: It is necessary to calculate the saturation vapor pressure of water and of ice for some purposes in many disciplines. A number of formulas are available for this calculation. These formulas either are tedious or are not very accurate. In this study, a new formula has been developed by integrating the Clausius–Clapeyron equation. This new formula is simple and easy to remember. In comparison with the International Association for the Properties of Water and Steam reference dataset, the mean relative errors from this new formula are only 0.001% and 0.006% for the saturation vapor pressure of water and of ice, respectively, within a wide range of temperatures from −100° to 100°C. In addition, this new formula yields a mean relative error of 0.0005% within the commonly occurring temperature range (10°–40°C). Therefore, this new formula has significant advantages over the improved Magnus formula and can be used to calculate the saturation vapor pressure of water and of ice in a wide variety of disciplines.

Description: A statistical model for the occurrence of convective hazards was developed and applied to reanalysis data to detect multidecadal trends in hazard frequency. The modeling framework is based on an additive logistic regression for observed hazards that exploits predictors derived from numerical model data. The regression predicts the probability of a severe hazard, which is considered as a product of two components: the probability that a storm occurs and the probability of the severe hazard, given the presence of a storm [P(severe) = P(storm) × P(severe|storm)]. The model was developed using lightning data as an indication of thunderstorm occurrence and hazard reports across central Europe. Although it uses only two predictors per component, it is capable of reproducing the observed spatial distribution of lightning and yields realistic annual cycles of lightning, hail, and wind fairly accurately. The model was applied to ERA-Interim (1979–2016) across Europe to detect any changes in lightning, hail, and wind hazard occurrence. The frequency of conditions favoring lightning, wind, and large hail has increased across large parts of Europe, with the exception of the southwest. The resulting predicted occurrence of 6-hourly periods with lightning, wind, and large hail has increased by 16%, 29%, and 41%, respectively, across western and central Europe and by 23%, 56%, and 86% across Germany and the Alps during the period considered. It is shown that these changes are caused by increased instability in the reanalysis rather than by changes in midtropospheric moisture or wind shear.