ALBERT

Description: In this study, a gale event that occurred on the lee side of a long narrow mountain was investigated, together with the associated mountain flows, using a realistic-case large-eddy simulation (LES) that is based on the Weather Research and Forecasting Model. The mountain is located on the southeastern Tibetan Plateau, where approximately 58 gales occur annually, mostly in the afternoons during the winter season. Benefitting from realistic topography and high horizontal resolution as fine as 111 m, the LES can replicate features similar to the wind fields observed during the gale period. Investigation of the early morning wind structure over the mountain revealed that weak inflows were blocked, reversed, and divided in the upstream area and that some weak lee waves, rotors, and two clear lee vortices were evident downstream. As the upstream wind accelerated and the boundary layer developed during the daytime, the lee waves became amplified with severe downslope wind and rotors. The interaction and coherent structure of the downslope wind, rotor, and vortices were investigated to show the severe wind distribution. The mountain drags associated with blocking and amplified lee waves are displayed to show the potential impact on the large-scale model. The linear lee-wave theory was adopted to explain the wave evolution during this event together with a discussion of the uncertainty around low-level nonlinear processes.

Description: This paper applied a commercial computational fluid dynamics code, STAR-CD, with the renormalization group k–ε turbulence model to simulate the flow and dispersion of contaminants released from a source on the windward side of a hill under different thermal stratifications. In the wake region, the influence of atmospheric stratification on the flow field is inconspicuous under neutral and unstable conditions because of the effect of mechanical disturbance. However, this influence becomes slightly conspicuous under stable conditions. When atmospheric stratification is stable, in the range of z/H 〈 1.0 (where z is height above the surface and H is height of the hill), the velocity deficits are smaller than those under neutral and unstable conditions. The maximum turbulence kinetic energy (TKE) appears in the wake regions, and the variation in TKE is significantly lower than that under neutral and unstable conditions. When atmospheric stratification is unstable, the vertical and horizontal spread of the plume is slightly greater than that under neutral and stable conditions and the maximum concentration is less than that under neutral conditions. When the Froude number is large (~11; Brunt–Väisälä frequency = 0.52), atmospheric stratification is slightly stable, the structure of flow around the hill is generally similar to that under neutral conditions, and the high-concentration regions are large on the windward side of the hill. Smaller high-concentration regions just appear on the windward side of the hill under unstable conditions. The pollutant concentrations in the wake region of the hill increase as a result of the effect of thermal stability, and the vertical spreading range of the plume along the downwind axis (x axis) is larger than that under neutral and stable conditions.

Description: For the first time, the cloud radiative effect (CRE) has been characterized for the Arctic site Ny-Ålesund, Svalbard, Norway, including more than 2 years of data (June 2016–September 2018). The cloud radiative effect, that is, the difference between the all-sky and equivalent clear-sky net radiative fluxes, has been derived based on a combination of ground-based remote sensing observations of cloud properties and the application of broadband radiative transfer simulations. The simulated fluxes have been evaluated in terms of a radiative closure study. Good agreement with observed surface net shortwave (SW) and longwave (LW) fluxes has been found, with small biases for clear-sky (SW: 3.8 W m−2; LW: −4.9 W m−2) and all-sky (SW: −5.4 W m−2; LW: −0.2 W m−2) situations. For monthly averages, uncertainties in the CRE are estimated to be small (~2 W m−2). At Ny-Ålesund, the monthly net surface CRE is positive from September to April/May and negative in summer. The annual surface warming effect by clouds is 11.1 W m−2. The longwave surface CRE of liquid-containing cloud is mainly driven by liquid water path (LWP) with an asymptote value of 75 W m−2 for large LWP values. The shortwave surface CRE can largely be explained by LWP, solar zenith angle, and surface albedo. Liquid-containing clouds (LWP 〉 5 g m−2) clearly contribute most to the shortwave surface CRE (70%–98%) and, from late spring to autumn, also to the longwave surface CRE (up to 95%). Only in winter are ice clouds (IWP 〉 0 g m−2; LWP 〈 5 g m−2) equally important or even dominating the signal in the longwave surface CRE.

Description: In this study, we classify wind patterns that impacted PM10 concentrations in the Seoul Metropolitan Area (SMA), South Korea, from 2012 to 2016 and analyze their contributions to annual variability in particulate matter smaller than 10 μm in diameter (PM10). Using a k-means clustering analysis, we identify major wind patterns affecting PM10 concentrations from 2002 to 2016. We confirm that the impact of wind pattern changes on PM10 variability in the SMA from 2012 to 2016 was relatively greater than the impact from 2002 to 2011. We find that PM10 from 2012 to 2016 was mainly affected by wind patterns that were 1) associated with the transport of foreign emissions (our clusters H2, H4, and H5) and 2) favorable for ventilation (our clusters L1 and L2). This finding shows that PM10 variability was determined by overall variations in the respective wind patterns particularly associated with high (over 80 μg m−3) and low (below 30 μg m−3) PM10 concentrations. The results from 2012 to 2016 CMAQ simulations indicate that the effects of meteorological conditions (e.g., wind, temperature, humidity, and so on) on PM10 vary from year to year. The calculated PM10 anomalies from 2012 to 2016 were −4.97, 3.55, 1.73, 0.15, and −0.46 μg m−3, suggesting that the wind patterns in 2012 produced the least PM10 and those in 2013 produced the most.

Description: In applications of climate information, coarse-resolution climate projections commonly need to be downscaled to a finer grid. One challenge of this requirement is the modeling of subgrid variability and the spatial and temporal dependence at the finer scale. Here, a postprocessing procedure for temperature projections is proposed that addresses this challenge. The procedure employs statistical bias correction and stochastic downscaling in two steps. In the first step, errors that are related to spatial and temporal features of the first two moments of the temperature distribution at model scale are identified and corrected. Second, residual space–time dependence at the finer scale is analyzed using a statistical model, from which realizations are generated and then combined with an appropriate climate change signal to form the downscaled projection fields. Using a high-resolution observational gridded data product, the proposed approach is applied in a case study in which projections of two regional climate models from the Coordinated Downscaling Experiment–European Domain (EURO-CORDEX) ensemble are bias corrected and downscaled to a 1 km × 1 km grid in the Trøndelag area of Norway. A cross-validation study shows that the proposed procedure generates results that better reflect the marginal distributional properties of the data product and have better consistency in space and time when compared with empirical quantile mapping.

Description: Climate warming in the North China Plain (NCP) is expected to greatly affect corn production. On the basis of a comprehensive consideration of the double-cropping system, we investigated the impacts of climate warming in the past 55 years on the planting boundaries and areas of varieties of summer corn with different maturity levels. In addition, we tried to explore the probable reasons for the changes in planting boundaries. Climate warming caused a northward shift in the planting boundaries of summer corn, resulting in the expansion of the total planting area. However, the trend for the planting area of each belt of corn maturity was not always consistent. Because of the advanced planting date and delayed physiological maturation date, the growing season of corn in the NCP has been prolonged in the past 55 years. Climate warming also increased the active accumulated temperature with a threshold of 10° (AAT10) during the corn growing season by 73.2°C decade−1, which was mainly caused by the increase in the number of days with a daily temperature over 10°C. In summary, the planting boundaries of varieties of summer corn with different maturity levels have greatly changed due to climate change, and corn production in the NCP could benefit from climate warming through the greater planting area and longer growing season.

Description: In this paper, the sensitivity of the Weather Research and Forecasting (WRF) Model to the nudging parameters in simulating July–August (JJA) precipitation was assessed with 16 experiments over the Coordinated Regional Climate Downscaling Experiment East Asia II (CORDEX-EA-II) domain. The effects of various nudging parameters in spectral nudging (referred to as SN) and grid nudging (referred to as AN) experiments are examined, including wavenumbers, relaxation time, nudging levels, and nudging variables for SN and relaxation time and nudging variables for AN. Results showed that the applications of spectral nudging and grid nudging methods in WRF simulations can improve the model’s ability to reproduce the JJA extreme precipitation event and accompanying large-scale fields in 2003. The major findings include 1) spectral nudging is superior to grid nudging in simulating heavy rainfall and low-level circulation, 2) nudging both kinematic and thermodynamic variables is efficient to better simulate the JJA precipitation for both SN and AN simulations, 3) in SN simulations, the options of wavenumbers display stronger impact on JJA precipitation if nudging solely the kinematic variables instead of both kinematic and thermodynamic variables over wet subregions, and 4) the free developed large-scale processes associated with small nudging wavenumbers can diminish the improvement from nudging both kinematic and thermodynamic variables in simulating subseasonal variations of precipitation. Overall, the experiment that adopts spectral nudging of both kinematic and thermodynamic variables, 1-h relaxation time, and four or eight nudging wavenumbers captures the characteristics of summer climate more reasonably.

Description: Empirical studies have led to improvements in evaluating and quantifying the tornado threat. However, more work is needed to put the research onto a solid statistical foundation. Here the authors begin to build this foundation by introducing and then demonstrating a statistical model to estimate damage rating (enhanced Fujita scale) probabilities. A goal is to alert researchers to available statistical technology for improving severe weather warnings. The model is cumulative logistic regression and the parameters are determined using Bayesian inference. The model is demonstrated by estimating damage rating probabilities from values of known environmental factors on days with many tornadoes in the United States. Controlling for distance to nearest town/city, which serves as a proxy variable for damage target density, the model quantifies the chance that a particular tornado will be assigned any damage rating given specific environmental conditions. Under otherwise average conditions, the model estimates a 65% chance that a tornado occurring in a city or town will be rated EF0 when bulk shear (1000–500-hPa layer) is weak (10 m s−1). This probability drops to 38% when the bulk shear is strong (40 m s−1). The model quantifies the corresponding increases in the chance of the same tornado receiving higher damage ratings. Quantifying changes to the probability distribution on the ordered damage rating categories is a natural application of cumulative logistic regression.

Description: In the 1990s, the National Weather Service and the Federal Aviation Administration began deploying the Automated Surface Observing Systems (ASOS). These systems provided the capability to report real-time weather observations, including some types of present weather, as frequently as once every minute. Over 900 of these ASOS stations were installed across the United States, replacing most of the human observers. Despite the benefits offered, many issues were noted, including the inability to discern and report certain precipitation types, particularly drizzle, freezing drizzle, and ice pellets. These and other issues resulted in human observers being retained at roughly 130 ASOS airport locations around the country where high-quality weather observations are essential because of air traffic volume or other factors. The human observers at these locations work in conjunction with the ASOS, manually augmenting the automated weather observations when the ASOS provides erroneous data or when an ASOS observation is missing. To assess the impact of automation on present weather observations, the differences in present weather reports for two decades will be highlighted: 1979–88 (when only human observers reported the present weather observations) and 2005–14 (after the full ASOS network became operational). Comparisons between the decades will be further analyzed to determine the differences at the ASOS locations at which human observers were retained in the later decade, as well as the ASOS locations at which no humans were retained. Both the positive and negative impacts of automation, with an emphasis on aviation impacts, are presented.

Description: The Flash Flood and Intense Rainfall (FFaIR) Experiment developed within the Hydrometeorology Testbed (HMT) of the Weather Prediction Center (WPC) is a pseudo-operational platform for participants from across the weather enterprise to test emerging flash flood forecasting tools and issue experimental forecast products. This study presents the objective verification portion of the 2017 edition of the experiment, which examines the performance from a variety of guidance tools (deterministic models, ensembles, and machine-learning techniques) and the participants’ forecasts, with occasional reference to the participants’ subjective ratings. The skill of the model guidance used in the FFaIR Experiment is evaluated using performance diagrams verified against the Stage IV analysis. The operational and FFaIR Experiment versions of the excessive rainfall outlook (ERO) are evaluated by assessing the frequency of issuances, probabilistic calibration, Brier skill score (BSS), and area under relative operating characteristic (AuROC). An ERO first-guess field called the Colorado State University Machine-Learning Probabilities method (CSU-MLP) is also evaluated in the FFaIR Experiment. Among convection-allowing models, the Met Office Unified Model generally performed optimally throughout the FFaIR Experiment when using performance diagrams (at the 0.5- and 1-in. thresholds; 1 in. = 25.4 mm), whereas the High-Resolution Rapid Refresh (HRRR), version 3, performed best subjectively. In terms of subjective and objective ensemble scores, the HRRR ensemble scored optimally. The CSU-MLP overpredicted lower risk categories and underpredicted higher risk categories, but it shows future promise as an ERO first-guess field. The EROs issued by the FFaIR Experiment forecasters had improved BSS and AuROC relative to the operational ERO, suggesting that the experimental guidance may have aided forecasters.