High-Resolution Rapid Refresh Model Research Articles

Application of geostationary satellite and high-resolution meteorology data in estimating hourly PM2.5 levels during the Camp Fire episode in California

Wildland fire smoke contains large amounts of PM2.5 that can traverse tens to hundreds of kilometers, resulting in significant deterioration of air quality and excess mortality and morbidity in downwind regions. Estimating PM2.5 levels while considering the impact of wildfire smoke has been challenging due to the lack of ground monitoring coverage near the smoke plumes. We aim to estimate total PM2.5 concentration during the Camp Fire episode, the deadliest wildland fire in California history. Our random forest (RF) model combines calibrated low-cost sensor data (PurpleAir) with regulatory monitor measurements (Air Quality System, AQS) to bolster ground observations, Geostationary Operational Environmental Satellite-16 (GOES-16)’s high temporal resolution to achieve hourly predictions, and oversampling techniques (Synthetic Minority Oversampling Technique, SMOTE) to reduce model underestimation at high PM2.5 levels. In addition, meteorological fields at 3 km resolution from the High-Resolution Rapid Refresh model and land use variables were also included in the model. Our AQS-only model achieved an out of bag (OOB) R2 (RMSE) of 0.84 (12.00 μg/m3) and spatial and temporal cross-validation (CV) R2 (RMSE) of 0.74 (16.28 μg/m3) and 0.73 (16.58 μg/m3), respectively. Our AQS + Weighted PurpleAir Model achieved OOB R2 (RMSE) of 0.86 (9.52 μg/m3) and spatial and temporal CV R2 (RMSE) of 0.75 (14.93 μg/m3) and 0.79 (11.89 μg/m3), respectively. Our AQS + Weighted PurpleAir + SMOTE Model achieved OOB R2 (RMSE) of 0.92 (10.44 μg/m3) and spatial and temporal CV R2 (RMSE) of 0.84 (12.36 μg/m3) and 0.85 (14.88 μg/m3), respectively. Hourly predictions from our model may aid in epidemiological investigations of intense and acute exposure to PM2.5 during the Camp Fire episode.

Forecast Skill for California Heavy Precipitation Periods from the High-Resolution Rapid Refresh Model and the Coupled Ocean–Atmosphere Mesoscale Prediction System

Abstract Precipitation forecasts from the High-Resolution Rapid Refresh model (HRRR) of the National Centers for Environmental Prediction (NCEP) and the Navy’s Coupled Ocean–Atmosphere Mesoscale Prediction System (COAMPS) are examined during heavy precipitation periods in California. Precipitation forecast discrepancies between the two models are examined during a recent heavy winter precipitation episode in California from 6 to 8 December 2019. The skill of initial 12-h precipitation forecasts is examined objectively from 1 December 2018 to 28 February 2019 from the HRRR, COAMPS, and NCEP’s North American Mesoscale Forecast System (NAM-3km). The HRRR exhibited lower seasonal biases and higher skill based on several metrics applied to a sample of 48 12-h periods during California’s second wettest winter season during the past 20 years. Overall, the NAM-3km and COAMPS exhibited a large wet bias over the interior mountain regions while the HRRR model indicated a dry bias along the northern coastal region. All models tended to underestimate precipitation along the coastal mountains of Northern California. To highlight the regional and localized nature of forecast skill, the fraction skill score (FSS) metric is applied across ranges of spatial scales and precipitation values. For the domain as a whole, the HRRR had higher precipitation forecast skill compared to the other two models, particularly within radial distances of 20–30 km and moderate (10–50 mm) precipitation totals. FSS computed locally highlights the HRRR’s overall higher skill as well as enhanced skill in the southern half of the state.

Evaluating Operational and Experimental HRRR Model Forecasts of Atmospheric River Events in California

AbstractImproved forecasts of atmospheric river (AR) events, which provide up to half the annual precipitation in California, may reduce impacts to water supply, lives, and property. We evaluate quantitative precipitation forecasts (QPF) from the High-Resolution Rapid Refresh model version 3 (HRRRv3) and version 4 (HRRRv4) for five AR events that occurred in February–March 2019 and compare them to quantitative precipitation estimates (QPE) from Stage IV and Mesonet products. Both HRRR versions forecast spatial patterns of precipitation reasonably well, but are drier than QPE products in the Bay Area and wetter in the Sierra Nevada range. The HRRR dry bias in the Bay Area may be related to biases in the model temperature profile, while integrated water vapor (IWV), wind speed, and wind direction compare reasonably well. In the Sierra Nevada range, QPE and QPF agree well at temperatures above freezing. Below freezing, the discrepancies are due in part to errors in the QPE products, which are known to underestimate frozen precipitation in mountainous terrain. HRRR frozen QPF accuracy is difficult to quantify, but the model does have wind speed and wind direction biases near the Sierra Nevada range. HRRRv4 is overall more accurate than HRRRv3, likely due to data assimilation improvements, and possibly physics improvements. Applying a neighborhood maximum method impacted performance metrics, but did not alter general conclusions, suggesting closest gridbox evaluations may be adequate for these types of events. Improvements to QPF in the Bay Area and QPE/QPF in the Sierra Nevada range would be particularly useful to provide better understanding of AR events.

Differentiating Freezing Drizzle and Freezing Rain in HRRR Model Forecasts

AbstractSupercooled large drop (SLD) icing poses a unique hazard for aircraft and has resulted in new regulations regarding aircraft certification to fly in regions of known or forecast SLD icing conditions. The new regulations define two SLD icing categories based upon the maximum supercooled liquid water drop diameter (Dmax): freezing drizzle (100–500 μm) and freezing rain (>500 μm). Recent upgrades to U.S. operational numerical weather prediction models lay a foundation to provide more relevant aircraft icing guidance including the potential to predict explicit drop size. The primary focus of this paper is to evaluate a proposed method for estimating the maximum drop size from model forecast data to differentiate freezing drizzle from freezing rain conditions. Using in situ cloud microphysical measurements collected in icing conditions during two field campaigns between January and March 2017, this study shows that the High-Resolution Rapid Refresh model is capable of distinguishing SLD icing categories of freezing drizzle and freezing rain using a Dmax extracted from the rain category of the microphysics output. It is shown that the extracted Dmax from the model correctly predicted the observed SLD icing category as much as 99% of the time when the HRRR accurately forecast SLD conditions; however, performance varied by the method to define Dmax and by the field campaign dataset used for verification.

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.

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.

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.

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.

A Multiscale, Hydrometeorological Forecast Evaluation of National Water Model Forecasts of the May 2018 Ellicott City, Maryland, Flood

AbstractThe NOAA National Water Model (NWM) became operational in August 2016, producing the first ever real-time, distributed, continuous set of hydrologic forecasts over the continental United States (CONUS). This project uses integrated hydrometeorological assessment methods to investigate the utility of the NWM to predict catastrophic flooding associated with an extreme rainfall event that occurred in Ellicott City, Maryland, on 27–28 May 2018. Short-range forecasts (0–18-h lead time) from the NWM version 1.2 are explored, focusing on the quantitative precipitation forecast (QPF) forcing from the High-Resolution Rapid Refresh (HRRR) model and the corresponding NWM streamflow forecast. A comprehensive assessment of multiscale hydrometeorological processes are considered using a combination of object-based, grid-based, and hydrologic point-based verification. Results highlight the benefits and risks of using a distributed hydrologic modeling tool such as the NWM to connect operational CONUS-scale atmospheric forcings to local impact predictions. For the Ellicott City event, reasonably skillful QPF in several HRRR model forecast cycles produced NWM streamflow forecasts in the small Ellicott City basin that were suggestive of flash flood potential. In larger surrounding basins, the NWM streamflow response was more complex, and errors were found to be governed by both hydrologic process representation, as well as forcing errors. The integrated, hydrometeorological multiscale analysis method demonstrated here guides both research and ongoing model development efforts, along with providing user education and engagement to ultimately engender improved flash flood prediction.

Comparison of Lightning Forecasts from the High-Resolution Rapid Refresh Model to Geostationary Lightning Mapper Observations

Abstract The ability of the High-Resolution Rapid Refresh (HRRR) model to forecast the location of convective storms is of interest for a variety of applications. Since lightning is often present with intense convection, lightning observations from the Geostationary Lightning Mapper (GLM) on GOES-East are used to evaluate the performance of the HRRR lightning forecasts from May through September during 2018 and 2019. Model skill is presented in terms of the fractions skill score (FSS) evaluated within circular neighborhoods with radial distances from 30 to 240 km. Case studies of individual events illustrate that the HRRR lightning forecasts FSS varies from storm to storm. Mean FSS is summarized for the months with peak lightning activity (June–August) for the west, central, and east United States. Our results suggest that forecasters should use HRRR lightning forecasts to indicate general tendencies for the occurrence, region, and timing of thunderstorms in a broad region rather than expect high forecast accuracy for lightning locally. For example, when FSS is evaluated within small neighborhoods (30-km radius), mean FSS drops sharply after the first two hours of model integration in all regions and during all hours of the day. However, when evaluated within larger neighborhoods (60-km radius and larger), FSS in the western United States and northern Mexico remains high for all lead times in the late afternoon and early evening. This result is likely due to the model capturing the tendency for convection to break out over higher terrain during those hours.

A Verification Approach Used in Developing the Rapid Refresh and Other Numerical Weather Prediction Models

Developing and improving numerical weather prediction models such as the Rapid Refresh (RAP) and High-Resolution Rapid Refresh (HRRR) requires a well-designed, easy-to-use evaluation capability using observations. Owing to the very complex nonlinear interactions between the data assimilation system and the representation of various physics components in the model, changes to one aspect of the modeling system to address a particular shortcoming within the model may have detrimental impacts in another area. Thus, the model verification approach used in the Global Systems Division of the NOAA Earth System Research Laboratory—which actively develops the RAP and HRRR models and other forecasting systems—is designed to allow hypothesis-driven testing of different aspects of the model using observations. In this approach, model changes easily and quickly can be quantified by automatically comparing simulated geophysical variables against many different types of observations that are collected operationally by various agencies, including the National Weather Service. We have implemented this approach in the Model Analysis Tool Suite (MATS). A key aspect of MATS is the use of a database-driven system that stores partial sums of model minus observation pairs over specified geographical regions in order to reduce the dimensionality of the data and, thus, improve the response time of the system. These partial sums are created and stored in a manner that allows the data to be visualized in different ways, thereby providing new insights into the ability of that particular version of the model to replicate the observed atmospheric conditions.

Quantifying Extreme Precipitation Forecasting Skill in High-Resolution Models Using Spatial Patterns: A Case Study of the 2016 and 2018 Ellicott City Floods

Recent historic floods in Ellicott City, MD, on 30 July 2016 and 27 May 2018 provide stark examples of the types of floods that are expected to become more frequent due to urbanization and climate change. Given the profound impacts associated with flood disasters, it is crucial to evaluate the capability of state-of-the-art weather models in predicting these hydrometeorological events. This study utilizes an object-based approach to evaluate short range (<12 h) hourly forecast precipitation from the High-Resolution Rapid Refresh (HRRR) versus observations from the National Centers for Environmental Prediction (NCEP) Stage IV precipitation analysis. For both datasets, a binary precipitation field is delineated using thresholds that span trace to extreme precipitation rates. Next, spatial metrics of area, perimeter, solidity, elongation, and fragmentation, as well as centroid positions for the forecast and observed fields are calculated. A Mann–Whitney U-test reveals biases (using a confidence level of 90%) related to the spatial attributes and locations of model forecast precipitation. Results indicate that traditional pixel-based precipitation verification metrics are limited in their ability to quantify and characterize model skill. In contrast, an object-based methodology offers encouraging results in that the HRRR can skillfully predict the extreme precipitation rates that are anticipated with anthropogenic climate change. Yet, there is still room for improvement, since model forecasts of extreme convective rainfall tend to be slightly too numerous and fragmented compared with observations. Lastly, results are sensitive to the HRRR model’s representation of synoptic-scale and mesoscale processes. Therefore, detailed surface analyses and an “ingredients-based” approach should remain central to the process of forecasting excessive rainfall.

Coupling computational fluid dynamics with the high resolution rapid refresh model for forecasting dynamic line ratings

This study looks at forecasted dynamic line ratings in southern Idaho by using data from the high resolution rapid-refresh (HRRR) model for forecasted weather conditions. The HRRR model can provide accurate 18-h forecasts with a 15-min temporal resolution. Typical static ratings used for overhead transmission lines use overly conservative assumptions for local weather conditions, such as using the maximum solar irradiance and ambient temperature measured during the summer, combined with low wind speed for an entire season. The HRRR forecast model used here has high spatial resolution to provide local forecast conditions along the entire length of a transmission line. The area that is of interest in this study is in southern Idaho, spanning a total of 15,000 square kilometers. The forecasted weather data are coupled with a computational fluid dynamics (CFD) model of the wind in the region for fine-scale resolution of convective cooling rates on the midpoint of each individual transmission-line span. This high-fidelity approach can be used to find the minimum ampacity across all given midpoints of a transmission line to determine the limit to be used for the line rating. The ability to increase the overhead line rating above the conservative static approach with the forecasted weather data provides a large potential to alleviate congestion and provide data for utility-market transactions, as well as benefits for wind energy generation through concurrent cooling. This study shows that for the region of interest, using forecasted weather data coupled with CFD modeling can calculate DLR ampacity rating above static over 90% of the time, with a small relative error in the forecasted ampacity over time.

High-Resolution Rapid Refresh Model Data Analytics Derived on the Open Science Grid to Assist Wildland Fire Weather Assessment

AbstractTerabytes of weather data are generated every day by gridded model simulations and in situ and remotely sensed observations. With this accelerating accumulation of weather data, efficient computational solutions are needed to process, archive, and analyze the massive datasets. The Open Science Grid (OSG) is a consortium of computer resources around the United States that makes idle computer resources available for use by researchers in diverse scientific disciplines. The OSG is appropriate for high-throughput computing, that is, many parallel computational tasks. This work demonstrates how the OSG has been used to compute a large set of empirical cumulative distributions from hourly gridded analyses of the High-Resolution Rapid Refresh (HRRR) model run operationally by the Environmental Modeling Center of the National Centers for Environmental Prediction. These cumulative distributions derived from a 3-yr HRRR archive are computed for seven variables, over 1.9 million grid points, and each hour of the calendar year. The HRRR cumulative distributions are used to evaluate near-surface wind, temperature, and humidity conditions during two wildland fire episodes—the North Bay fires, a wildfire complex in Northern California during October 2017 that was the deadliest and costliest in California history, and the western Oklahoma wildfires during April 2018. The approach used here illustrates ways to discriminate between typical and atypical atmospheric conditions forecasted by the HRRR model. Such information may be useful for model developers and operational forecasters assigned to provide weather support for fire management personnel.

An Evaluation of Paired Regional/Convection-Allowing Forecast Vertical Thermodynamic Profiles in Warm-Season, Thunderstorm-Supporting Environments

Abstract This study evaluates forecast vertical thermodynamic profiles and derived thermodynamic parameters from two regional/convection-allowing model pairs, the North American Mesoscale Forecast System and the North American Mesoscale Nest model pair and the Rapid Refresh and High Resolution Rapid Refresh model pair, in warm-season, thunderstorm-supporting environments. Differences in bias and mean absolute error between the regional and convection-allowing models in each of the two pairs, while often statistically significant, are practically small for the variables, parameters, and vertical levels considered, such that the smaller-scale variability resolved by convection-allowing models does not degrade their forecast skill. Model biases shared by the regional and convection-allowing models in each pair are documented, particularly the substantial cool and moist biases in the planetary boundary layer arising from the Mellor–Yamada–Janjić planetary boundary layer parameterization used by the North American Mesoscale model and the Nest version as well as the middle-tropospheric moist bias shared by the Rapid Refresh and High Resolution Rapid Refresh models. Bias and mean absolute errors typically have larger magnitudes in the evening, when buoyancy is a significant contributor to turbulent vertical mixing, than in the morning. Vertical thermodynamic profile biases extend over a deep vertical layer in the western United States given strong sensible heating of the underlying surface. The results suggest that convection-allowing models can fulfill the use cases typically and historically met by regional models in operations at forecast entities such as the Storm Prediction Center, a fruitful finding given the proposed elimination of regional models with the Next-Generation Global Prediction System initiative.

Gap-Filling Mobile Radar Observations of a Snow Squall in the San Luis Valley

Abstract On 24 January 2017, a convective snow squall developed in the San Luis Valley of Colorado. This squall produced rapidly varying winds at San Luis Valley airport in Alamosa, Colorado, with gusts up to 12 m s−1, and an associated visibility drop to 1.4 km from unlimited in less than 10 min. This snow squall was largely undetected by the operational WSR-88D network because of the Sangre de Cristo Range of the Rocky Mountains lying between the valley and the nearest WSR-88D in Pueblo, Colorado. This study presents observations of the snow squall from the X-band NOAA X-Pol radar, which was deployed in the San Luis Valley during the event. These observations document the squall developing from individual convective cells and growing upscale into a linear squall, with peak radial velocities of 15 m s−1. The environment conducive to the development of this snow squall is examined using data from the High-Resolution Rapid Refresh model, which shows an environment unstable to ascending surface-based parcels, with surface-based convective available potential energy (SBCAPE) values up to 600 J kg−1 in the San Luis Valley. The mobile radar data are integrated into the Multi-Radar Multi-Sensor (MRMS) mosaic to illustrate both the large improvement in detectability of this event gained from a gap-filling radar as well as the capability of MRMS to incorporate data from new radars designed to fill gaps in the current radar network.

Consistency Between Convection Allowing Model Output and Passive Microwave Satellite Observations

AbstractObservations from the Global Precipitation Measurement (GPM) core satellite were used along with precipitation forecasts from the High Resolution Rapid Refresh (HRRR) model to assess and interpret differences between observed and modeled storms. Using a feature‐based approach, precipitating objects were identified in both the National Centers for Environmental Prediction Stage IV multisensor precipitation product and HRRR forecast at lead times of 1, 2, and 3 h at valid times corresponding to GPM overpasses. Precipitating objects were selected for further study if (a) the observed feature occurred entirely within the swath of the GPM Microwave Imager (GMI) and (b) the HRRR model predicted it at all three forecast lead times. Output from the HRRR model was used to simulate microwave brightness temperatures (Tbs), which were compared to those observed by the GMI. Simulated Tbs were found to have biases at both the warm and cold ends of the distribution, corresponding to the stratiform/anvil and convective areas of the storms, respectively. Several experiments altered both the simulation microphysics and hydrometeor classification in order to evaluate potential shortcomings in the model's representation of precipitating clouds. In general, inconsistencies between observed and simulated brightness temperatures were most improved when transferring snow water content to supercooled liquid hydrometeor classes.

A Polarimetric Analysis of Ice Microphysical Processes in Snow, Using Quasi-Vertical Profiles

AbstractThis study implements a new quasi-vertical profile (QVP) methodology to investigate the microphysical evolution and significance of intriguing winter polarimetric signatures and their statistical correlations. QVPs of transitional stratiform and pure snow precipitation are analyzed using WSR-88D S-band data, alongside their corresponding environmental thermodynamic High-Resolution Rapid Refresh model analyses. QVPs of KDP and ZDR are implemented to demonstrate their value in interpreting elevated ice processes. Several fascinating and repetitive signatures are observed in the QVPs for differential reflectivity ZDR and specific differential phase KDP, in the dendritic growth layer (DGL), and at the tops of clouds. The most striking feature is maximum ZDR (up to 6 dB) in the DGL occurring near the −10-dBZ ZH contour within low KDP and during shallower and warmer cloud tops. Conversely, maximum KDP (up to 0.3° km−1) in the DGL occurs within low ZDR and during taller and colder cloud tops. Essentially, ZDR and KDP in the DGL are anticorrelated and strongly depend on cloud-top temperature. Analyses also show correlations indicating larger ZDR within lower ZH in the DGL and larger KDP within greater ZH in the DGL. The high-ZDR regions are likely dominated by growth of a mixture of highly oblate dendrites and/or hexagonal plates, or prolate needles. Regions of high KDP are expected to be overwhelmed with snow aggregates and crystals with irregular or nearly spherical shapes, seeded at cloud tops. Furthermore, QVP indications of hexagonal plate crystals within the DGL are verified using in situ microphysical measurements, demonstrating the reliability of QVPs in evaluating ice microphysics in upper regions of winter clouds.

Offshore wind speed estimates from a high‐resolution rapidly updating numerical weather prediction model forecast dataset

AbstractIn association with the Department of Energy–funded Position of Offshore Wind Energy Resources (POWER) project, we present results from compositing a 3‐year dataset of 80‐m (above ground level) wind forecasts from the 3‐km High‐Resolution Rapid Refresh (HRRR) model over offshore regions for the contiguous United States. The HRRR numerical weather prediction system runs once an hour and features hourly data assimilation, providing a key advantage over previous model‐based offshore wind datasets. On the basis of 1‐hour forecasts from the HRRR model, we highlight the different climatological regimes of the nearshore environment, characterizing the mean 80‐m wind speed as well as the frequency of exceeding 4, 12, and 25 m s−1 for east and west coast, Gulf of Mexico, and Great Lake locations. Preliminary verification against buoy measurements demonstrates good agreement with observations. This dataset can inform the placement of targeted measurement systems in support of improving resource assessments and wind forecasts to advance offshore wind energy goals both in New England and other coastal regions of the United States.

Confirmation of Elevated Methane Emissions in Utah's Uintah Basin With Ground‐Based Observations and a High‐Resolution Transport Model

AbstractLarge CH4 leak rates have been observed in the Uintah Basin of eastern Utah, an area with over 10,000 active and producing natural gas and oil wells. In this paper, we model CH4 concentrations at four sites in the Uintah Basin and compare the simulated results to in situ observations at these sites during two spring time periods in 2015 and 2016. These sites include a baseline location (Fruitland), two sites near oil wells (Roosevelt and Castlepeak), and a site near natural gas wells (Horsepool). To interpret these measurements and relate observed CH4 variations to emissions, we carried out atmospheric simulations using the Stochastic Time‐Inverted Lagrangian Transport model driven by meteorological fields simulated by the Weather Research and Forecasting and High Resolution Rapid Refresh models. These simulations were combined with two different emission inventories: (1) aircraft‐derived basin‐wide emissions allocated spatially using oil and gas well locations, from the National Oceanic and Atmospheric Administration (NOAA), and (2) a bottom‐up inventory for the entire U.S., from the Environmental Protection Agency (EPA). At both Horsepool and Castlepeak, the diurnal cycle of modeled CH4 concentrations was captured using NOAA emission estimates but was underestimated using the EPA inventory. These findings corroborate emission estimates from the NOAA inventory, based on daytime mass balance estimates, and provide additional support for a suggested leak rate from the Uintah Basin that is higher than most other regions with natural gas and oil development.