Diffusion that is implicit in the odd-ordered advection schemes in early versions of the Advanced Research core of the Weather Research and Forecasting (WRF) model is sometimes insufficient to remove noise from kinematical fields. The problem is worst when grid-relative wind speeds are low and when stratification is nearly neutral or unstable, such as in weakly forced daytime boundary layers, where noise can grow until it competes with the physical phenomena being simulated. One solution to this problem is an explicit, sixth-order numerical diffusion scheme that preserves the WRF model’s high effective resolution and uses a flux limiter to ensure monotonicity. The scheme, and how it was added to the WRF model, are explained. The scheme is then demonstrated in an idealized framework and in simulations of salt breezes and lake breezes in northwestern Utah.

This paper reviews the benefits of a better understanding of atmospheric exchange processes over mountains. These processes affect weather and climate variables that are important in meteorological applications related to many scientific disciplines and sectors of the economy. We focus this review on examples of meteorological applications in hydrology, ecology, agriculture, urban planning, wind energy, transportation, air pollution, and climate change. These examples demonstrate the benefits of a more accurate knowledge of atmospheric exchange processes over mountains, including a better understanding of snow redistribution, microclimate, land-cover change, frost hazards, urban ventilation, wind gusts, road temperatures, air pollution, and the impacts of climate change. The examples show that continued research on atmospheric exchange processes over mountains is warranted, and that a recognition of the potential benefits can inspire new research directions. An awareness of the links between basic research topics and applications is important to the success and impact of new efforts that aim at better understanding atmospheric exchange processes over mountains. To maximize the benefits of future research for meteorological applications, coordinated international efforts involving scientists studying atmospheric exchange processes, as well as scientists and stakeholders representing many other scientific disciplines and economic sectors are required.

The authors demonstrate that much can be learned about the performance of a numerical weather prediction (NWP) model by examining the temporal modes of its simulated rainfall. Observations from the Weather Surveillance Radar-1988 Doppler (WSR-88D) network are used to evaluate the rainfall frequency, and its diurnal and semidiurnal modes, in simulations made by a preliminary version of the Weather Research and Forecasting (WRF) model for the conterminous United States during the summer of 2003. Simulations and observations were broadly similar in the normalized amplitudes of their diurnal and semidiurnal modes, but not in the modes' phases, and not in overall frequency of rain. Simulated rain fell too early, and light rain was too frequent. The model also did not produce the distinct, nocturnal maximum in rainfall frequency that is integral to the hydrologic cycle of the Great Plains. The authors provide evidence that there were regional and phenomenological dependencies to the WRF model's performance.

Monitoring the fuel moisture content (FMC) of 10 h dead vegetation is crucial for managing and mitigating the impact of wildland fires. The combination of in situ FMC observations, numerical weather prediction (NWP) models, and satellite retrievals has facilitated the development of machine learning (ML) models to estimate 10 h dead FMC retrievals over the contiguous US (CONUS). In this study, ML models were trained using variables from the National Water Model, the High-Resolution Rapid Refresh (HRRR) NWP model, and static surface properties, along with surface reflectances and land surface temperature (LST) retrievals from the Visible Infrared Imaging Radiometer Suite (VIIRS) instrument on the Suomi-NPP satellite system. Extensive hyper-parameter optimization resulted in skillful FMC models compared to a daily climatography RMSE (+44%) and an hourly climatography RMSE (+24%). Notably, VIIRS retrievals played a significant role as predictors for estimating 10 h dead FMC, demonstrating their importance as a group due to their high band correlation. Conversely, individual predictors within the HRRR group exhibited relatively high importance according to explainability techniques. Removing both HRRR and VIIRS retrievals as model inputs led to a significant decline in performance, particularly with worse RMSE values when excluding VIIRS retrievals. The importance of the VIIRS predictor group reinforces the dynamic relationship between 10 h dead fuel, the atmosphere, and soil moisture. These findings underscore the significance of selecting appropriate data sources when utilizing ML models for FMC prediction. VIIRS retrievals, in combination with selected HRRR variables, emerge as critical components in achieving skillful FMC estimates.

The authors present a unique, scale-discriminating study of the environment-relative circulations within a mesoscale convective system (MCS) and mesoscale convective vortex (MCV). The MCS, a leading convective line and trailing stratiform region that became asymmetric, passed through the National Oceanic and Atmospheric Administration (NOAA) Profiler Network (NPN) in Kansas and Oklahoma on 1 August 1996. The MCV appeared in the MCS's stratiform region just prior to the system's mature stage and grew to a depth of over 12 km as the MCS dissipated. The MCV did not apparently survive to the next day. A spatial bandpass filter was used to divide observed wind into a component that was predominantly synoptic background wind and a component that was predominantly a mesoscale perturbation on that background wind. A mesoscale updraft, mesoscale downdraft, and divergent outflows in the lower and upper troposphere were evident after the synoptic background wind was removed, so these four circulations were ... Abstract The authors present a unique, scale-discriminating study of the environment-relative circulations within a mesoscale convective system (MCS) and mesoscale convective vortex (MCV). The MCS, a leading convective line and trailing stratiform region that became asymmetric, passed through the National Oceanic and Atmospheric Administration (NOAA) Profiler Network (NPN) in Kansas and Oklahoma on 1 August 1996. The MCV appeared in the MCS's stratiform region just prior to the system's mature stage and grew to a depth of over 12 km as the MCS dissipated. The MCV did not apparently survive to the next day. A spatial bandpass filter was used to divide observed wind into a component that was predominantly synoptic background wind and a component that was predominantly a mesoscale perturbation on that background wind. A mesoscale updraft, mesoscale downdraft, and divergent outflows in the lower and upper troposphere were evident after the synoptic background wind was removed, so these four circulations were ...

The authors employ data from the NOAA Wind Profiler Network to present a scale-discriminating vorticity budget for a mesoscale convective vortex (MCV) that was generated by a mesoscale convective system (MCS) in Oklahoma and Kansas on 1 August 1996. A spatial bandpass filter was used to divide observed wind into mesoscale and synoptic components. Then the authors sought sources and sinks of vorticity in those two components over 9 h of the MCV's lifetime. The vorticity budget reveals that both the mesoscale and synoptic winds supplied significant vorticity to the MCV. The vortex's origin could not be proved, but data weakly suggest that tilting may have been mostly responsible. Convergence of absolute vorticity by the mesoscale wind was the reason the MCV grew deeper and stronger as the MCS weakened. Finally, tilting of synoptic and mesoscale vorticity by gradients in mesoscale vertical motion was responsible for a secondary deepening of the MCV as the MCS dissipated. The budget suggests that, if... Abstract The authors employ data from the NOAA Wind Profiler Network to present a scale-discriminating vorticity budget for a mesoscale convective vortex (MCV) that was generated by a mesoscale convective system (MCS) in Oklahoma and Kansas on 1 August 1996. A spatial bandpass filter was used to divide observed wind into mesoscale and synoptic components. Then the authors sought sources and sinks of vorticity in those two components over 9 h of the MCV's lifetime. The vorticity budget reveals that both the mesoscale and synoptic winds supplied significant vorticity to the MCV. The vortex's origin could not be proved, but data weakly suggest that tilting may have been mostly responsible. Convergence of absolute vorticity by the mesoscale wind was the reason the MCV grew deeper and stronger as the MCS weakened. Finally, tilting of synoptic and mesoscale vorticity by gradients in mesoscale vertical motion was responsible for a secondary deepening of the MCV as the MCS dissipated. The budget suggests that, if...