High-Resolution Rapid Refresh Research Articles

Influence of Hurricane Wind Field Variability on Real‐Time Forecast Simulations of the Coastal Environment

AbstractDynamic conditions occur in the coastal ocean during severe storms. Forecasting these conditions is challenging, and large‐scale numerical models require significant computing power. In this paper, we describe a real‐time modeling system (DUNEX‐RT), developed in support of the During Nearshore Event experiment (DUNEX) off the coast of North Carolina, United States of America. The model is run with wave, current, and water level boundary conditions from larger‐scale models, and provides 36‐h forecasts of significant wave height, depth‐averaged velocity, and water levels every 6‐h using Delft3D‐SWAN. Observations and forecasts run at different times are compared and communicated via an interactive website to verify model performance in real‐time and to visualize uncertainty from changing inputs. Here, we evaluate model sensitivity to inputs from seven different atmospheric hindcasts and two atmospheric forecasts for Hurricane Dorian in September 2019. The results emphasize the importance of accurate wind forcing, with significant differences observed between the output model results for different input atmospheric forcing models and forecasts produced at different times. The best results were achieved using atmospheric forcing from the high resolution rapid refresh model, and overall, DUNEX‐RT had low errors at 33 wave, water level, and current sites across the system. The model results for water levels and significant wave heights were also accurate over a longer period of 49 days. Overall, the good forecast skill achieved for the wide range of conditions over this time results suggest that this high‐resolution regional approach could be applied to forecast conditions in other coastal areas.

A Method to Account for QPF Spatial Displacement Errors in Short-Term Ensemble Streamflow Forecasting

To account for spatial displacement errors common in quantitative precipitation forecasts (QPFs), a method using systematic shifting of QPF fields was tested to create ensemble streamflow forecasts. While previous studies addressed spatial displacement using neighborhood approaches, shifting of QPF accounts for those errors while maintaining the structure of predicted systems, a feature important in hydrologic forecasts. QPFs from the nine-member High-Resolution Rapid Refresh Ensemble were analyzed for 46 forecasts from 6 cases covering 17 basins within the National Weather Service North Central River Forecast Center forecasting region. Shifts of 55.5 and 111 km were made in the four cardinal and intermediate directions, increasing the ensemble size to 81 members. These members were input into a distributed hydrologic model to create an ensemble streamflow prediction. Overall, the ensemble using the shifted QPFs had an improved frequency of non-exceedance and probability of detection, and thus better predicted flood occurrence. However, false alarm ratio did not improve, likely because shifting multiple QPF ensembles increases the potential to place heavy precipitation in a basin where none actually occurred. A weighting scheme based on a climatology of displacements was tested, improving overall performance slightly compared to the approach using non-weighted members.

Classification of Precipitation Type in North China Using Model-Based Explicit Fields of Hydrometeors with Modified Thermodynamic Conditions

AbstractThe ability to forecast thermodynamic conditions aloft and near the surface is critical to the accurate forecasting of precipitation type at the surface. This paper presents an experimental version of a new scheme for diagnosing precipitation type. The method considers the optimum surface temperature threshold associated with each precipitation type and combines model-based explicit fields of hydrometeors with higher-resolution modified thermodynamic and topographic information to determine precipitation types in North China. Based on over 60 years of precipitation-type samples from North China, this study explores the climatological characteristics of the five precipitation types—snow, rain, ice pellets (IP), rain/snow mix (R/S MIX), and freezing rain (FZ)—as well as the suitable air temperature Ta and wet-bulb temperature Tw thresholds for distinguishing different precipitation types. Direct output from numerical weather prediction (NWP) models, such as temperature and humidity, was modified by downscaling and bias correction, as well as by incorporating the latest surface observational data and high-resolution topographic data. Validation of the precipitation-type forecasts from this scheme was performed against observations from the 2016 to 2019 winter seasons and two case studies were also analyzed. Compared with the similar diagnostic routine in the High-Resolution Rapid Refresh (HRRR) forecasting system used to predict precipitation type over North China, the skill of the method proposed here is similar for rain and better for snow, R/S MIX, and FZ. Furthermore, depiction of the diagnosed boundary between R/S MIX and snow is good in most areas. However, the number of misclassifications for R/S MIX is significantly larger than for rain and snow.

Wind Ramp Events Validation in NWP Forecast Models during the Second Wind Forecast Improvement Project (WFIP2) Using the Ramp Tool and Metric (RT&M)

AbstractThe second Wind Forecast Improvement Project (WFIP2) is a multiagency field campaign held in the Columbia Gorge area (October 2015–March 2017). The main goal of the project is to understand and improve the forecast skill of numerical weather prediction (NWP) models in complex terrain, particularly beneficial for the wind energy industry. This region is well known for its excellent wind resource. One of the biggest challenges for wind power production is the accurate forecasting of wind ramp events (large changes of generated power over short periods of time). Poor forecasting of the ramps requires large and sudden adjustments in conventional power generation, ultimately increasing the costs of power. A Ramp Tool and Metric (RT&M) was developed during the first WFIP experiment, held in the U.S. Great Plains (September 2011–August 2012). The RT&M was designed to explicitly measure the skill of NWP models at forecasting wind ramp events. Here we apply the RT&M to 80-m (turbine hub-height) wind speeds measured by 19 sodars and three lidars, and to forecasts from the High-Resolution Rapid Refresh (HRRR), 3-km, and from the High-Resolution Rapid Refresh Nest (HRRRNEST), 750-m horizontal grid spacing, models. The diurnal and seasonal distribution of ramp events are analyzed, finding a noticeable diurnal variability for spring and summer but less for fall and especially winter. Also, winter has fewer ramps compared to the other seasons. The model skill at forecasting ramp events, including the impact of the modification to the model physical parameterizations, was finally investigated.

Exploring Convection-Allowing Model Evaluation Strategies for Severe Local Storms Using the Finite-Volume Cubed-Sphere (FV3) Model Core

AbstractVerification methods for convection-allowing models (CAMs) should consider the finescale spatial and temporal detail provided by CAMs, and including both neighborhood and object-based methods can account for displaced features that may still provide useful information. This work explores both contingency table–based verification techniques and object-based verification techniques as they relate to forecasts of severe convection. Two key fields in severe weather forecasting are investigated: updraft helicity (UH) and simulated composite reflectivity. UH is used to generate severe weather probabilities called surrogate severe fields, which have two tunable parameters: the UH threshold and the smoothing level. Probabilities computed using the UH threshold and smoothing level that give the best area under the receiver operating curve result in very high probabilities, while optimizing the parameters based on the Brier score reliability component results in much lower probabilities. Subjective ratings from participants in the 2018 NOAA Hazardous Weather Testbed Spring Forecasting Experiment (SFE) provide a complementary evaluation source. This work compares the verification methodologies in the context of three CAMs using the Finite-Volume Cubed-Sphere Dynamical Core (FV3), which will be the foundation of the U.S. Unified Forecast System (UFS). Three agencies ran FV3-based CAMs during the five-week 2018 SFE. These FV3-based CAMs are verified alongside a current operational CAM, the High-Resolution Rapid Refresh version 3 (HRRRv3). The HRRR is planned to eventually use the FV3 dynamical core as part of the UFS; as such evaluations relative to current HRRR configurations are imperative to maintaining high forecast quality and informing future implementation decisions.

Identification of Combined Hydrological Models and Numerical Weather Predictions for Enhanced Flood Forecasting in a Semiurban Watershed

Flood forecasting in urban and semiurban catchments is often limited by the capability of the combined hydrological models and forecast inputs to predict floods accurately. The objective of this research is to develop an approach (1) to identify the best model forecast from multiple integrations of various hydrological models and numerical weather predictions (NWP), and (2) to find the best forecast combination method for an improved short-range flood forecasting. Seven selected hydrological models were coupled, each with two high-resolution NWP forecasts to provide several alternatives of deterministic hydrological forecasts at a catchment outlet. As such, the different model-input combinations were used to generate 14 hydrological forecasts. Hydrological forecast verification was then carried out over a one-year hindcast period. A comparison between six forecast combination methods, including a benchmark Bayesian model averaging (BMA) method, was also performed for the multiple available short-term streamflow forecasts. Results indicate that the coupling of the Sacramento soil moisture accounting (SACSMA) model with both High-Resolution Deterministic Precipitation System and High-Resolution Rapid Refresh inputs outperformed other model-input integrations. Maximum forecast errors in all model-input integration outputs occurred at forecast lead times of 12–14 h, corresponding to the time of concentration of the catchment. Providing constraints on the estimation of model weights was found to be a significant factor for obtaining an improved combined streamflow forecast. In general, the regression-based forecast combination method of the constrained ordinary least squares (CLS) has emerged as a possible alternative to the widely used BMA method for hydrology application.

Aviation Turbulence Forecasting at Upper Levels with Machine Learning Techniques Based on Regression Trees

AbstractWe explore the use of machine learning (ML) techniques, namely, regression trees (RT), for the purpose of aviation turbulence forecasting at upper levels [20–45 kft (~6–14 km) in altitude]. In particular, we develop a series of RT-based algorithms that include random forests (RF) and gradient-boosted regression trees (GBRT) methods. Numerical weather prediction model prognostic variables and derived turbulence diagnostics based on 6-h forecasts from the 3-km High-Resolution Rapid Refresh model are used as features to train these data-driven models. Training and evaluation are based on turbulence estimates of eddy dissipation rate (EDR) obtained from automated in situ aircraft reports. Our baseline RF model, consisting of 100 trees with 30 layers of maximum depth, significantly reduces forecast errors for EDR < 0.1 m2/3 s−1 (which corresponds roughly to null and light turbulence) when compared with a simple regression model, increasing the probability of detection and in turn reducing the number of false alarms. Model complexity reduction via GBRT and feature-relevance analyses is performed, indicating that considerable execution speedups can be achieved while maintaining the model’s predictive skill. Overall, the ML models exhibit enhanced performance in discriminating the EDR forecast among the light, moderate, and severe turbulence categories. In addition, these artificial intelligence techniques significantly simplify the generation of new NWP and grid-spacing specific turbulence forecast products.

Air quality and aerosol predictions at National Oceanic and Atmospheric Administration (NOAA) and their applications

Operational air quality predictions produced by NOAA/National Weather Service include predictions of ozone, fine particulate matter (PM2.5) and wildfire smoke for the United States and predictions of windblown dust for the contiguous 48 states. Prediction maps are distributed at https://airquality.weather.gov/ and as a web service at https://idpgis.ncep.noaa.gov/arcgis/rest/services/NWS_Forecasts_Guidance_Warnings. Community Multiscale Air Quality modeling system (CMAQ) used for ozone and PM2.5 predictions was most recently updated in December 2018, when a unified bias-correction procedure was introduced for ozone and PM2.5. Wildfire smoke dispersion is operationally predicted using the Hybrid Single Particle Lagrangian Integrated Trajectory Model (HYSPLIT). Wildfire smoke dispersion is included in version 4 of the Eulerian High Resolution Rapid Refresh (HRRR) model that is being evaluated for operational implementation. One of the largest uncertainties for prediction of air quality over the U.S. involves specification of wildfire smoke emissions. Different smoke emission approaches used in these systems will be discussed.NOAA is testing ozone and PM2.5 predictions extended from 48 to 72 hours. Global aerosol predictions, which include biomass burning aerosols, are being tested for inclusion in one member of the Global Ensemble Forecast System (GEFS) to replace the current standalone global aerosol prediction system. An overview of this testing will be presented.NOAA’s operational air quality predictions contribute to protection of lives and health in the United States. These predictions are used by state and local air quality forecasters to issue official air quality forecasts for their respective areas. General public accesses NOAA’s hourly predictions directly at https://airquality.weather.gov/. These predictions are also used in the applications about wildfire and health as well as in smoke vulnerability assessments lead by our partners at the U. S. Environmental Protection Agency (EPA) and Centers for Disease Control and Prevention (CDC).

What Does a Convection-Allowing Ensemble of Opportunity Buy Us in Forecasting Thunderstorms?

AbstractThe High Resolution Ensemble Forecast v2.1 (HREFv2.1), an operational convection-allowing model (CAM) ensemble, is an “ensemble of opportunity” wherein forecasts from several independently designed deterministic CAMs are aggregated and postprocessed together. Multiple dimensions of diversity in the HREFv2.1 ensemble membership contribute to ensemble spread, including model core, physics parameterization schemes, initial conditions (ICs), and time lagging. In this study, HREFv2.1 forecasts are compared against the High Resolution Rapid Refresh Ensemble (HRRRE) and the Multiscale data Assimilation and Predictability (MAP) ensemble, two experimental CAM ensembles that ran during the 5-week Spring Forecasting Experiment (SFE) in spring 2018. The HRRRE and MAP are formally designed ensembles with spread achieved primarily through perturbed ICs. Verification in this study focuses on composite radar reflectivity and updraft helicity to assess ensemble performance in forecasting convective storms. The HREFv2.1 shows the highest overall skill for these forecasts, matching subjective real-time impressions from SFE participants. Analysis of the skill and variance of ensemble member forecasts suggests that the HREFv2.1 exhibits greater spread and more effectively samples model uncertainty than the HRRRE or MAP. These results imply that to optimize skill in forecasting convective storms at 1–2-day lead times, future CAM ensembles should employ either diverse membership designs or sophisticated perturbation schemes capable of representing model uncertainty with comparable efficacy.

Evaluation of an Application of Probabilistic Quantitative Precipitation Forecasts for Flood Forecasting

Probabilistic streamflow forecasts using precipitation derived from ensemble-based Probabilistic Quantitative Precipitation Forecasts (PQPFs) are examined. The PQPFs provide rainfall amounts associated with probabilities of exceedance for all grid points, which are averaged to the watershed scale for input to the operational Sacramento Soil Moisture Accounting hydrologic model to generate probabilistic streamflow predictions. The technique was tested using both the High-Resolution Rapid Refresh Ensemble (HRRRE) and the High-Resolution Ensemble Forecast version 2.0 (HREF) for 11 river basins across the upper Midwest for 109 cases. The resulting discharges associated with low probability of exceedance values were too large; no events were observed having discharges above the 10% exceedance value predicted from the technique applied to both ensembles, and no events were observed having discharges above the 25% exceedance value from the HREF-based forecast. The large differences are due to using the same precipitation exceedance value at all points; it is unlikely that all watershed points would experience the heavy rainfall associated with the 5% probability of exceedance. The technique likely can be improved through calibration of the basin-average precipitation forecasts based on typical distributions of precipitation within the convective systems that dominate warm-season precipitation events or calibration of the resulting probabilistic discharge forecasts.

NWP and Radar Extrapolation: Comparisons and Explanation of Errors

AbstractThis paper examines nowcasts of precipitation from the High-Resolution Rapid Refresh (HRRRv2) model from the summer of 2017 along the Colorado Front Range. It was found that model nowcasts (2 h or less) of precipitation amount were less skillful than extrapolation of the KFTG WSR-88-D data at a spatial scale of 120 km. It was also found that local-scale (mesoscale) influences on rainfall intensity and amount have a much greater impact on rainfall intensity than large-scale (synoptic) influences. Thus, large-scale trends are not useful for modifying extrapolation nowcasts on the local scale. Errors in the HRRR nowcasts are attributed to an inability of the model and data assimilation to resolve convergence along outflow boundaries and other terrain-influenced mesogamma-scale flows that contribute to storm formation and evolution. While the HRRRv2 1-h nowcasts were strongly correlated with observed precipitation events, the nowcast precipitation amounts were in error by more than a factor of 2 about 50% of the time, with half of the cases being overestimates and half being underestimates. A large fraction of the HRRRv2 overestimates were associated with stratiform rain events. It is speculated that this was a result of misinterpretation of the radar bright band as more intense precipitation aloft by the data assimilation scheme. A large fraction of the HRRRv2 underestimates occurred when the data assimilation and model were unable to fully resolve the low-level convergence along small-scale, narrow boundaries that led to new storm initiation and/or storm growth.

PyDDA: A Pythonic Direct Data Assimilation Framework for Wind Retrievals

This software assimilates data from an arbitrary number of weather radars together with other spatial wind fields (eg numerical weather forecasting model data) in order to retrieve high resolution three dimensional wind fields. PyDDA uses NumPy and SciPy’s optimization techniques combined with the Python Atmospheric Radiation Measurement (ARM) Radar Toolkit (Py-ART) in order to create wind fields using the 3D variational technique (3DVAR). PyDDA is hosted and distributed on GitHub at https://github.com/openradar/PyDDA . PyDDA has the potential to be used by the atmospheric science community to develop high resolution wind retrievals from radar networks. These retrievals can be used for the evaluation of numerical weather forecasting models and plume modelling. This paper shows how wind fields from 2 NEXt generation RADar (NEXRAD) WSR-88D radars and the High Resolution Rapid Refresh can be assimilated together using PyDDA to create a high resolution wind field inside Hurricane Florence. Funding statement: The development of this software is supported by the Climate Model Development and Validation (CMDV) activity which is funded by the Office of Biological and Environmental Research in the US Department of Energy Office of Science.

Boundary Layer and Surface Verification of the High-Resolution Rapid Refresh, Version 3

AbstractWhile numerical weather prediction models have made considerable progress regarding forecast skill, less attention has been paid to the planetary boundary layer. This study leverages High-Resolution Rapid Refresh (HRRR) forecasts on native levels, 1-s radiosonde data, and (primarily airport) surface observations across the conterminous United States. We construct temporally and spatially averaged composites of wind speed and potential temperature in the lowest 1 km for selected months to identify systematic errors in both forecasts and observations in this critical layer. We find near-surface temperature and wind speed predictions to be skillful, although wind biases were negatively correlated with observed speed and temperature biases revealed a robust relationship with station elevation. Above ≈250 m above ground level, below which radiosonde wind data were apparently contaminated by processing, biases were small for wind speed and potential temperature at the analysis time (which incorporates sonde data) but became substantial by the 24-h forecast. Wind biases were positive through the layer for both 0000 and 1200 UTC, and morning potential temperature profiles were marked by excessively steep lapse rates that persisted across seasons and (again) exaggerated at higher elevation sites. While the source or cause of these systematic errors are not fully understood, this analysis highlights areas for potential model improvement and the need for a continued and accessible archive of the data that make analyses like this possible.

Improvements to Lake-Effect Snow Forecasts Using a One-Way Air–Lake Model Coupling Approach

AbstractLake-effect convective snowstorms frequently produce high-impact, hazardous winter weather conditions downwind of the North American Great Lakes. During lake-effect snow events, the lake surfaces can cool rapidly, and in some cases, notable development of ice cover occurs. Such rapid changes in the lake-surface conditions are not accounted for in existing operational weather forecast models, such as the National Oceanic and Atmospheric Administration’s (NOAA) High-Resolution Rapid Refresh (HRRR) model, resulting in reduced performance of lake-effect snow forecasts. As a milestone to future implementations in the Great Lakes Operational Forecast System (GLOFS) and HRRR, this study examines the one-way linkage between the hydrodynamic-ice model [the Finite-Volume Community Ocean Model coupled with the unstructured grid version of the Los Alamos Sea Ice Model (FVCOM-CICE), the physical core model of GLOFS] and the atmospheric model [the Weather Research and Forecasting (WRF) Model, the physical core model of HRRR]. The realistic representation of lake-surface cooling and ice development or its fractional coverage during three lake-effect snow events was achieved by feeding the FVCOM-CICE simulated lake-surface conditions to WRF (using a regional configuration of HRRR), resulting in the improved simulation of the turbulent heat fluxes over the lakes and resulting snow water equivalent in the downwind areas. This study shows that the one-way coupling is a practical approach that is well suited to the operational environment, as it requires little to no increase in computational resources yet can result in improved forecasts of regional weather and lake conditions.

A high-amplitude atmospheric inertia–gravity wave-induced meteotsunami in Lake Michigan

On Friday, April 13, 2018, a high-amplitude atmospheric inertia–gravity wave packet with surface pressure perturbations exceeding 10 mbar crossed the lake at a propagation speed that neared the long-wave gravity speed of the lake, likely producing Proudman resonance. A set of meteotsunami waves struck the shores near Ludington, Michigan, a coastal community along the sandy dunes of Lake Michigan. During the event, harbor walls were overtopped, damage occurred to shoreline homes and boat docks, and water intake pumps were impacted due to the large change in water level. To fully understand the generation of this event and the impacts to the coastal community, we have carried out atmospheric and hydrodynamic model simulations of the inertia–gravity and meteotsunami waves. Atmospheric simulation of the inertia–gravity waves was performed using a high-resolution model for the Great Lakes region that mimics the National Oceanic and Atmospheric Administration High-Resolution Rapid Refresh operational model. Surface meteorological conditions were supplied to the Lake Michigan-Huron Operational Forecast System, an operational model used for hydrodynamic forecast guidance. This is the first documented case of a meteotsunami generated by an atmospheric inertia–gravity wave in the Great Lakes, and it provides an evaluation of existing and proposed operational infrastructure as it pertains to meteotsunami forecasting in the USA.

Estimating Near Real-Time Hourly Evapotranspiration Using Numerical Weather Prediction Model Output and GOES Remote Sensing Data in Iowa

This study evaluates the applicability of numerical weather prediction output supplemented with remote sensing data for near real-time operational estimation of hourly evapotranspiration (ET). Rapid Refresh (RAP) and High-Resolution Rapid Refresh (HRRR) systems were selected to provide forcing data for a Penman-Monteith model to calculate the Actual Evapotranspiration (AET) over Iowa. To investigate how the satellite-based remotely sensed net radiation ( R n ) estimates might potentially improve AET estimates, Geostationary Operational Environmental Satellite derived R n (GOES- R n ) data were incorporated into each dataset for comparison with the RAP and HRRR R n -based AET evaluations. The authors formulated a total of four AET models—RAP, HRRR, RAP-GOES, HRRR-GOES, and validated the respective ET estimates against two eddy covariance tower measurements from central Iowa. The implementation of HRRR-GOES for AET estimates showed the best results among the four models. The HRRR-GOES model improved statistical results, yielding a correlation coefficient of 0.8, a root mean square error (mm hr−1) of 0.08, and a mean bias (mm hr−1) of 0.02 while the HRRR only model results were 0.64, 0.09, and 0.04, respectively. Despite limited in situ observational data to fully test a proposed AET estimation, the HRRR-GOES model clearly showed potential utility as a tool to predict AET at a regional scale with high spatio-temporal resolution.

Evaluating the WFIP2 updates to the HRRR model using scanning Doppler lidar measurements in the complex terrain of the Columbia River Basin

The wind-energy (WE) industry relies on numerical weather prediction (NWP) forecast models as foundational or base models for many purposes, including wind-resource assessment and wind-power forecasting. During the Second Wind Forecast Improvement Project (WFIP2) in the Columbia River Basin of Oregon and Washington, a significant effort was made to improve NWP forecasts through focused model development, to include experimental refinements to the High Resolution Rapid Refresh (HRRR) model physics and horizontal grid spacing. In this study, the performance of an experimental version of HRRR that includes these refinements is tested against a control version, which corresponds to that of the operational HRRR run by National Oceanic and Atmospheric Administration/National Centers for Environmental Protection at the outset of WFIP2. The effects of horizontal grid resolution were also tested by comparing wind forecasts from the HRRR (with 3-km grid spacing) with those from a finer-resolution HRRR nest with 750-m grid spacing. Model forecasts are validated against accurate wind-profile measurements by three scanning, pulsed Doppler lidars at sites separated by a total distance of 71 km. Model skill and improvements in model skill, attributable to physics refinements and improved horizontal grid resolution, varied by season, by site, and during periods of atmospheric phenomena relevant to WE. In general, model errors were the largest below 150 m above ground level (AGL). Experimental HRRR refinements tended to reduce the mean absolute error (MAE) and other error metrics for many conditions, but degradation in skill (increased MAE) was noted below 150 m AGL at the two lowest-elevation sites at night. Finer resolution was found to produce the most significant reductions in the error metrics.

Weather Hazard Risk Quantification for sUAS Safety Risk Management

AbstractAs the number of applications for small unmanned (i.e., remotely operated) aircraft systems (sUAS) continues to grow, comprehensive safety risk assessment studies are required to ensure their safe integration into the National Airspace System. One source of hazards for sUAS that has not been extensively addressed is adverse weather. A framework is presented for analyzing weather forecast data to provide sUAS operators with risk assessment information that they can use for making risk-aware decisions. The sUAS Weather Risk Model (sWRM) framework quantifies weather hazard risk for sUAS operations in rural to urban environments using weather forecast, population density, structure density, and sUAS data. sWRM is developed by following the safety risk management guidelines from the U.S. Federal Aviation Administration. Development of sWRM highlights a number of aerospace and meteorological research areas that must be addressed prior to weather risk models for sUAS becoming operational. Primary among these research areas is developing widely available finescale (<1 km) weather forecasts and conducting extensive sUAS flight-report studies to accurately estimate parameters of Bayesian belief network conditional probability tables used in the proposed framework. As a proof of concept, sWRM was applied over Boulder, Colorado, using the High-Resolution Rapid Refresh weather product. This initial demonstration of sWRM highlights the potential effectiveness of a detailed risk assessment model that takes into account high-resolution weather and environmental data.

Evaluation of high-resolution rapid refresh (HRRR) forecasts for extreme precipitation

This study evaluates the accuracy of short-range (1-h to 18-hr lead-time) forecasts from the High-Resolution Rapid Refresh (HRRR) model for five extreme storms in the United States: (1) the September 21–23, 2016, frontal storms in Iowa, (2) the April 28-May 1, 2017, frontal storms in the Southern Midwestern US, (3) the August 25–31, 2017, Hurricane Harvey storms in Texas, (4) the September 13–17, 2018, Hurricane Florence storms in the Carolinas, and (5) the September 4–6, 2019, Hurricane Dorian storms in the Carolinas. The evaluation was carried out by comparison with gauge-corrected Multi-Radar/Multi-Sensor (MRMS-GC) products. In terms of temporal variability, there was a good agreement between the forecasted and observed precipitation on an hourly basis. Thus, the HRRR products provide relatively reliable forecasts. However, the forecasts were mostly biased: they tend to overestimate rainfall for both hurricanes, underestimate rainfall for tropical storms in Iowa, and produce almost unbiased estimates for the frontal storms in Southern Midwestern US. In terms of spatial pattern, the forecasts are able to capture the spatial pattern of hurricanes, however, they produce too many, localized, high-rain intensities for the frontal storms than what the observations show. With regard to the effect of lead times, the 1-h lead forecasts have often lower accuracy than the other lead-time forecasts, while there was no much systematic difference in accuracy among the 2-h to 18-h lead-time forecasts. The bias estimates in the forecast are also examined at different spatial scales, ranging from 2 km × 2 km all the way to 128 km × 128 km. The results show that the bias estimated at smaller spatial scales vary within a large range, mostly within the range of −100% to +100%, indicating that the bias estimates obtained at large scale (hundreds of km grids) are not applicable to bias estimates at small scales, and vice versa. Local-bias correction approaches are therefore preferable over global bias-correction approaches.

An Evaluation of a Hybrid, Terrain-Following Vertical Coordinate in the WRF-Based RAP and HRRR Models

Abstract A new hybrid, sigma-pressure vertical coordinate was recently added to the Weather Research and Forecasting (WRF) Model in an effort to reduce numerical noise in the model equations near complex terrain. Testing of this hybrid, terrain-following coordinate was undertaken in the WRF-based Rapid Refresh (RAP) and High-Resolution Rapid Refresh (HRRR) models to assess impacts on retrospective and real-time simulations. Initial cold-start simulations indicated that the majority of differences between the hybrid and traditional sigma coordinate were confined to regions downstream of mountainous terrain and focused in the upper levels. Week-long retrospective simulations generally resulted in small improvements for the RAP, and a neutral impact in the HRRR when the hybrid coordinate was used. However, one possibility is that the inclusion of data assimilation in the experiments may have minimized differences between the vertical coordinates. Finally, analysis of turbulence forecasts with the new hybrid coordinate indicate a significant reduction in spurious vertical motion over the full length of the Rocky Mountains. Overall, the results indicate a potential to improve forecast metrics through implementation of the hybrid coordinate, particularly at upper levels, and downstream of complex terrain.