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This paper describes important characteristics of an uncoupled high-resolution land data assimilation system (HRLDAS) and presents a systematic evaluation of 18-month-long HRLDAS numerical experiments, conducted in two nested domains (with 12- and 4-km grid spacing) for the period from 1 January 2001 to 30 June 2002, in the context of the International [H.sub.2]O Project (IHOP_2002). HRLDAS was developed at the National Center for Atmospheric Research (NCAR) to initialize land-state variables of the coupled Weather Research and Forecasting (WRF)-land surface model (LSM) for high-resolution applications. Both uncoupled HRDLAS and coupled WRF are executed on the same grid, sharing the same LSM, land use, soil texture, terrain height, time-varying vegetation fields, and LSM parameters to ensure the same soil moisture climatological description between the two modeling systems so that HRLDAS soil state variables can be used to initialize WRF-LSM without conversion and interpolation. If HRLDAS is initialized with soil conditions previously spun up from other models, it requires roughly 8-10 months for HRLDAS to reach quasi equilibrium and is highly dependent on soil texture. However, the HRLDAS surface heat fluxes can reach quasi-equilibrium state within 3 months for most soil texture categories. Atmospheric forcing conditions used to drive HRLDAS were evaluated against Oklahoma Mesonet data, and the response of HRLDAS to typical errors in each atmospheric forcing variable was examined. HRLDAS-simulated finescale (4 km) soil moisture, temperature, and surface heat fluxes agreed well with the Oklahoma Mesonet and IHOP_2002 field data. One case study shows high correlation between HRLDAS evaporation and the low-level water vapor field derived from radar analysis.

Data impact experiments are conducted employing the Polar Weather Research and Forecasting (WRF) model during the YOPP‐SH summer special observing period (SOP) using the Antarctic Mesoscale Prediction System (AMPS) framework to determine the forecast impact of numerous additional radiosondes collected during the SOP. Hybrid variational‐ensemble three‐dimensional data assimilation is performed on model forecast domains over Antarctica and the Southern Ocean using all regular observations normally available (Experiment "NoSOP") and using the same set plus the extra soundings launched for the SOP (Experiment "SOP"). The SOP results show better near‐surface temperature and wind‐speed forecasts than the NoSOP results, primarily over West Antarctica. Radiosonde profiles confirm that temperature and wind‐speed forecasts are improved throughout the troposphere with the addition of the SOP radiosonde data, but the results for relative humidity are variable. Temperatures are improved at lower levels early in the forecasts, whereas wind speeds are better at higher levels later in the forecasts. An evaluation against the ERA5 global reanalysis that provides a much broader perspective reveals that the improved forecast skill for the SOP experiment persists up to 72 hours for temperature, wind speed, and relative humidity. The gains, however, are primarily confined to the Antarctic continent, consistent with the additional radiosonde spatial coverage being mainly poleward of 60°S. With extra radiosondes concentrated over the Antarctic Peninsula, SOP forecasts of the region downstream of the Peninsula were significantly improved compared to NoSOP forecasts. In addition, it is found that the assimilation of the additional radiosonde data can have a greater impact on the forecasts of strong cyclones, as shown for a major coastal cyclone affecting West Antarctica, with improvements in its magnitude and track. The results also suggest that increasing radiosonde launches at lower southern latitudes would improve forecasts over the Southern Ocean, especially during austral winter. [ABSTRACT FROM AUTHOR]

At the East Antarctic deep ice core drilling site Dome C, daily precipitation measurements were initiated in 2006 and are being continued until today. The amounts and stable isotope ratios of the precipitation samples as well as crystal types are determined. Within the measuring period, the two years 2009 and 2010 showed striking contrasting temperature and precipitation anomalies, particularly in the winter seasons. The reasons for these anomalies are analysed using data from the mesoscale atmospheric model WRF (Weather Research and Forecasting Model) run under the Antarctic Mesoscale Prediction System (AMPS). 2009 was relatively warm and moist due to frequent warm air intrusions connected to amplification of Rossby waves in the circumpolar westerlies, whereas the winter of 2010 was extremely dry and cold. It is shown that while in 2010 a strong zonal atmospheric flow was dominant, in 2009 an enhanced meridional flow prevailed, which increased the meridional transport of heat and moisture onto the East Antarctic plateau and led to a number of high-precipitation/warming events at Dome C. This was also evident in a positive (negative) SAM (Southern Annular Mode) index and a negative (positive) ZW3 (zonal wave number three) index during the winter months of 2010 (2009). Changes in the frequency or seasonality of such event-type precipitation can lead to a strong bias in the air temperature derived from stable water isotopes in ice cores. [ABSTRACT FROM AUTHOR]

In May and June 2013, the National Center for Atmospheric Research produced real-time 48-h convection-allowing ensemble forecasts at 3-km horizontal grid spacing using the Weather Research and Forecasting (WRF) Model in support of the Mesoscale Predictability Experiment field program. The ensemble forecasts were initialized twice daily at 0000 and 1200 UTC from analysis members of a continuously cycling, limited-area, mesoscale (15 km) ensemble Kalman filter (EnKF) data assimilation system and evaluated with a focus on precipitation and severe weather guidance. Deterministic WRF Model forecasts initialized from GFS analyses were also examined. Subjectively, the ensemble forecasts often produced areas of intense convection over regions where severe weather was observed. Objective statistics confirmed these subjective impressions and indicated that the ensemble was skillful at predicting precipitation and severe weather events. Forecasts initialized at 1200 UTC were more skillful regarding precipitation and severe weather placement than forecasts initialized 12 h earlier at 0000 UTC, and the ensemble forecasts were typically more skillful than GFS-initialized forecasts. At times, 0000 UTC GFS-initialized forecasts had temporal distributions of domain-average rainfall closer to observations than EnKF-initialized forecasts. However, particularly when GFS analyses initialized WRF Model forecasts, 1200 UTC forecasts produced more rainfall during the first diurnal maximum than 0000 UTC forecasts. This behavior was mostly attributed to WRF Model initialization of clouds and moist physical processes. The success of these real-time ensemble forecasts demonstrates the feasibility of using limited-area continuously cycling EnKFs as a method to initialize convection-allowing ensemble forecasts, and future real-time high-resolution ensemble development leveraging EnKFs seems justified. [ABSTRACT FROM AUTHOR]

The article discusses the progress related to the Antarctic Mesoscale Prediction System (AMPS) since it was launched in 2000. It explains that AMPS is a real-time numerical weather prediction system for Antarctica that uses the Weather Research and Forecasting (WRF) model. It highlights the evolution in the role of AMPS to support the logistical and biological needs of international users.

To bridge the gaps between traditional mesoscale modelling and microscale modelling, the National Center for Atmospheric Research, in collaboration with other agencies and research groups, has developed an integrated urban modelling system coupled to the weather research and forecasting (WRF) model as a community tool to address urban environmental issues. The core of this WRF/urban modelling system consists of the following: (1) three methods with different degrees of freedom to parameterize urban surface processes, ranging from a simple bulk parameterization to a sophisticated multi-layer urban canopy model with an indoor-outdoor exchange sub-model that directly interacts with the atmospheric boundary layer, (2) coupling to fine-scale computational fluid dynamic Reynolds-averaged Navier-Stokes and Large-Eddy simulation models for transport and dispersion (T&D) applications, (3) procedures to incorporate high-resolution urban land use, building morphology, and anthropogenic heating data using the National Urban Database and Access Portal Tool (NUDAPT), and (4) an urbanized high-resolution land data assimilation system. This paper provides an overview of this modelling system; addresses the daunting challenges of initializing the coupled WRF/urban model and of specifying the potentially vast number of parameters required to execute the WRF/urban model; explores the model sensitivity to these urban parameters; and evaluates the ability of WRF/urban to capture urban heat islands, complex boundary-layer structures aloft, and urban plume T&D for several major metropolitan regions. Recent applications of this modelling system illustrate its promising utility, as a regional climate-modelling tool, to investigate impacts of future urbanization on regional meteorological conditions and on air quality under future climate change scenarios. Copyright © 2010 Royal Meteorological Society [ABSTRACT FROM AUTHOR]

This paper describes important characteristics of an uncoupled high-resolution land data assimilation system (HRLDAS) and presents a systematic evaluation of 18-month-long HRLDAS numerical experiments, conducted in two nested domains (with 12- and 4-km grid spacing) for the period from 1 January 2001 to 30 June 2002, in the context of the International H2O Project (IHOP_2002). HRLDAS was developed at the National Center for Atmospheric Research (NCAR) to initialize land-state variables of the coupled Weather Research and Forecasting (WRF)–land surface model (LSM) for high-resolution applications. Both uncoupled HRDLAS and coupled WRF are executed on the same grid, sharing the same LSM, land use, soil texture, terrain height, time-varying vegetation fields, and LSM parameters to ensure the same soil moisture climatological description between the two modeling systems so that HRLDAS soil state variables can be used to initialize WRF–LSM without conversion and interpolation. If HRLDAS is initialized with soil conditions previously spun up from other models, it requires roughly 8–10 months for HRLDAS to reach quasi equilibrium and is highly dependent on soil texture. However, the HRLDAS surface heat fluxes can reach quasi-equilibrium state within 3 months for most soil texture categories. Atmospheric forcing conditions used to drive HRLDAS were evaluated against Oklahoma Mesonet data, and the response of HRLDAS to typical errors in each atmospheric forcing variable was examined. HRLDAS-simulated finescale (4 km) soil moisture, temperature, and surface heat fluxes agreed well with the Oklahoma Mesonet and IHOP_2002 field data. One case study shows high correlation between HRLDAS evaporation and the low-level water vapor field derived from radar analysis. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

A recent study by Carbone et al. revealed “episodes” of warm-season rainfall over North America characterized as coherently propagating signals often linking multiple mesoscale convective systems over spatial scales of 1000–3000 km and timescales of 1–3 days. The present study examines whether these propagating signals are found in two numerical weather prediction (NWP) models commonly used today, namely, the Eta Model from the National Centers for Environmental Prediction and the newly developed Weather Research and Forecast (WRF) model. The authors find that the diurnal cycle of rainfall over much of the United States east of the Rockies is poorly represented, particularly over the central United States, where a nocturnal rainfall maximum is observed. Associated with this nocturnal maximum is an axis of propagating rainfall emanating from the western High Plains in the late afternoon, extending across the Midwest overnight, and occasionally continuing to the Appalachians on the second day. This propagation is largely unrepresented in NWP models. Only where rainfall maximizes during the late afternoon and remains local do models perform reasonably well. Even in these areas there is a tendency, especially in the Eta Model, for rainfall to occur several hours too early. Using idealized simulations, the authors demonstrate that fundamental propagation errors arise using cumulus parameterizations contained in NWP models. The authors also show that errors in the timing of convection, combined with propagation errors, lead to a poor phase locking of predicted rainfall to diurnal and orographic forcing. This, in turn, degrades the coherence of propagating signals in diurnally averaged rainfall frequency diagrams. The authors suggest that until these “zeroth-order” statistical shortcomings in NWP models are rectified, prospects for accurate short-range, model-based prediction of warm-season rainfall remain poor. [ABSTRACT FROM AUTHOR]