The National Center for Atmospheric Research (NCAR) and the U.S. Army Test and Evaluation Command have developed a multiscale, rapid-cycling, real-time, four-dimensional data-assimilation and forecasting system that has been in operational use at five Army test ranges since 2001. This system was employed to provide operational modeling support for the Joint Urban 2003 (JU2003) Dispersion Experiment, conducted in Oklahoma City, Oklahoma, during July 2003. To better support this mission, modifications were made to the nonlocal boundary layer (BL) parameterization (known as the Medium Range Forecast scheme) of the fifth-generation Pennsylvania State University–NCAR Mesoscale Model, in order to improve BL forecasts. The NCEP–Oregon State University–Air Force–Hydrologic Research Laboratory land surface model was also improved to better represent urban forcing. Verification of the operational model runs and retrospectively simulated cases show 1) a significantly reduced low bias in the forecast surface wind speed and 2) more realistic daytime BL heights. During JU2003, the forecast urban heat island, urban dry bubble, and urban BL height agree reasonably well with observations and conceptual models. An analysis of three-dimensional atmospheric structures, based on model analyses for eight clear-sky days during the field program, reveals some interesting features of the Oklahoma City urban BL, including complex thermally induced circulations and associated convergence/divergence zones, a nocturnal thermal shadow downwind of the urban area, and the reduction of low-level jet wind speeds by more vigorous nocturnal mixing over the city.

Record‐breaking rainfall of 524.1 mm in 24 hr occurred in the coastal metropolitan city of Guangzhou, China, during 6–7 May 2017 and caused devastating flooding. Observation analysis and a nested very large eddy simulation (VLES) with Weather Research and Forecasting (WRF) model were conducted to investigate various factors that contributed to the heavy rainfall, including synoptic weather pattern, topographic effects, cold pool, and urban effects. First, the warm and moist southerly flow in the lower troposphere over the trumpet‐shaped topography of the Pearl River Delta continuously provided fuel for the development of the severe rainfall. Consequently, the southerly flow from the sea in the south strengthened with the development of the convection. Meanwhile, the precipitation‐produced weak cold pool supported a stationary outflow boundary, where new convective cells were continuously initiated and drifted downstream. The interaction between the cold outflows and the warm moist southerly flows in the lower troposphere formed a back‐building convective system, which produced local persistent heavy rainfall that lasted for more than 5 hr and reached record levels. Sensitivity experiments in which the urban area was removed from the model indicate that the urban forcing affected the timing and location of convective initiation and helped concentrate the maximum rain core. The nested WRF‐LES successfully simulated this heavy rainfall, and the model's advantages are noted for forecasting such local severe weather.

The National Center for Atmospheric Research (NCAR) recently updated the comprehensive wind power forecasting system in collaboration with Xcel Energy addressing users’ needs and requirements by enhancing and expanding integration between numerical weather prediction and machine-learning methods. While the original system was designed with the primary focus on day-ahead power prediction in support of power trading, the enhanced system provides short-term forecasting for unit commitment and economic dispatch, uncertainty quantification in wind speed prediction with probabilistic forecasting, and prediction of extreme events such as icing. Furthermore, the empirical power conversion machine-learning algorithms now use a quantile approach to data quality control that has improved the accuracy of the methods. Forecast uncertainty is quantified using an analog ensemble approach. Two methods of providing short-range ramp forecasts are blended: the variational doppler radar analysis system and an observation-based expert system. Extreme events, specifically changes in wind power due to high winds and icing, are now forecasted by combining numerical weather prediction and a fuzzy logic artificial intelligence system. These systems and their recent advances are described and assessed.

This study builds upon previous efforts to document the performance of the U.S. Army Test and Evaluation Command’s Four-Dimensional Weather Modeling System using conventional metrics. Winds, temperature, and specific humidity were verified for almost 15 000 forecasts at five U.S. Army test ranges using near-surface mesonet data. The primary objective was to use conventional metrics to characterize the degree to which forecast accuracy varies from range to range, within the diurnal cycle, with elapsed forecast time, and among the seasons. It was found that there are large interrange differences in forecast error, with larger errors typically associated with the ranges located near complex orography. Similarly, significant variations in accuracy were noted for different times in the diurnal cycle, but the diurnal dependency varied greatly among the ranges. Factor of 2 differences in accuracy were also found across the seasons.