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The High-Resolution Rapid Refresh (HRRR) is a convection-allowing implementation of the Advanced Research version of the Weather Research and Forecasting (WRF-ARW) Model with hourly data assimilation that covers the conterminous United States and Alaska and runs in real time at the NOAA/National Centers for Environmental Prediction (NCEP). Implemented operationally at NOAA/NCEP in 2014, the HRRR features 3-km horizontal grid spacing and frequent forecasts (hourly for CONUS and 3-hourly for Alaska). HRRR initialization is designed for optimal short-range forecast skill with a particular focus on the evolution of precipitating systems. Key components of the initialization are radar-reflectivity data assimilation, hybrid ensemble-variational assimilation of conventional weather observations, and a cloud analysis to initialize stratiform cloud layers. From this initial state, HRRR forecasts are produced out to 18 h every hour, and out to 48 h every 6 h, with boundary conditions provided by the Rapid Refresh system. Between 2014 and 2020, HRRR development was focused on reducing model bias errors and improving forecast realism and accuracy. Improved representation of the planetary boundary layer, subgrid-scale clouds, and land surface contributed extensively to overall HRRR improvements. The final version of the HRRR (HRRRv4), implemented in late 2020, also features hybrid data assimilation using flow-dependent covariances from a 3-km, 36-member ensemble (“HRRRDAS”) with explicit convective storms. HRRRv4 also includes prediction of wildfire smoke plumes. The HRRR provides a baseline capability for evaluating NOAA’s next-generation Rapid Refresh Forecast System, now under development.

The Rapid Refresh (RAP), an hourly updated assimilation and model forecast system, replaced the Rapid Update Cycle (RUC) as an operational regional analysis and forecast system among the suite of models at the NOAA/National Centers for Environmental Prediction (NCEP) in 2012. The need for an effective hourly updated assimilation and modeling system for the United States for situational awareness and related decision-making has continued to increase for various applications including aviation (and transportation in general), severe weather, and energy. The RAP is distinct from the previous RUC in three primary aspects: a larger geographical domain (covering North America), use of the community-based Advanced Research version of the Weather Research and Forecasting (WRF) Model (ARW) replacing the RUC forecast model, and use of the Gridpoint Statistical Interpolation analysis system (GSI) instead of the RUC three-dimensional variational data assimilation (3DVar). As part of the RAP development, modifications have been made to the community ARW model (especially in model physics) and GSI assimilation systems, some based on previous model and assimilation design innovations developed initially with the RUC. Upper-air comparison is included for forecast verification against both rawinsondes and aircraft reports, the latter allowing hourly verification. In general, the RAP produces superior forecasts to those from the RUC, and its skill has continued to increase from 2012 up to RAP version 3 as of 2015. In addition, the RAP can improve on persistence forecasts for the 1–3-h forecast range for surface, upper-air, and ceiling forecasts.

Lake-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 violent supercell tornado passed through the town of Spencer, South Dakota, on the evening of 30 May 1998 producing large gradients in damage severity. The tornado was rated at F4 intensity by damage survey teams. A Doppler On Wheels (DOW) mobile radar followed this tornado and observed the tornado at ranges between 1.7 and 8.0 km during various stages of the tornado's life. The DOW was deployed less than 4.0 km from the town of Spencer between 0134 and 0145 UTC, and during this time period, the tornado passed through Spencer, and peak Doppler velocity measurements exceeded 100 m s−1. Data gathered from the DOW during this time period contained high spatial resolution sample volumes of approximately 34 m × 34 m × 37 m along with frequent volume updates every 45–50 s. The high-resolution Doppler velocity data gathered from low-level elevation scans, when sample volumes are between 20 and 40 m AGL, are compared to extensive ground and aerial damage surveys performed by the National Weather Service (NWS) and the National Institute of Standards and Technology (NIST). Idealized radial profiles of tangential velocity are computed by fitting a model of an axisymmetric translating vortex to the Doppler radar observations, which compensates for velocity components perpendicular to the radar beam as well as the translational motion of the tornado vortex. Both the original single-Doppler velocity data and the interpolated velocity fields are compared with damage survey Fujita scale (F-scale) estimates throughout the town of Spencer. This comparison on a structure-by-structure basis revealed that radar-based estimates of the F-scale intensity usually exceeded the damage-survey-based F-scale both inside and outside the town of Spencer. In the town of Spencer, the radar-based wind field revealed two distinct velocity time series inside and outside the passage of the core-flow region. The center of the core-flow region tracked about 50 m farther north than the damage survey indicated because of the asymmetry induced by the 15 m s−1 translational motion of the tornado. The radar consistently measured the strongest winds in the lowest 200 m AGL with the most extreme Doppler velocities residing within 50 m AGL. Alternate measures of tornado wind field intensity that incorporated the effects of the duration of the extreme winds and debris were explored. It is suggested that damage may not be a simple function of peak wind gust and structural integrity, but that the duration of intense winds, directional changes, accelerations, and upwind debris loading may be critical factors.

On the evening of 30 May 1998 atmospheric conditions across southeastern South Dakota led to the development of organized moist convection including several supercells. One such supercell was tracked by both a Weather Surveillance Radar-1988 Doppler (WSR-88D) from Sioux Falls, South Dakota (KFSD), and by a Doppler On Wheels (DOW) mobile radar. This supercell remained isolated for an hour and a half before being overtaken by a developing squall line. During this time period the supercell produced at least one strong and one violent tornado, the latter of which passed through Spencer, South Dakota, despite the absence of strong low-level environmental wind shear. The two tornadoes were observed both visually and with the DOW radar at ranges between 1.7 and 12.9 km. The close proximity to the tornadoes permitted the DOW radar to observe tornado-scale structures on the order of 35 to 100 m, while the nearest WSR-88D only resolved the parent mesocyclone in the supercell. The DOW observations revealed a persistent Doppler velocity couplet and associated ring reflectivity signature at the tip of the hook echo. The DOW radar data contained tornado strength winds over 35 m s−1 within 100 m AGL approximately 180 s prior to both the first spotter report and visual confirmation of the first tornado associated with this supercell. Following the formation of the second tornado, the DOW radar observations revealed a tornado-strength Doppler velocity couplet within 150 m AGL between two separate tornado tracks determined by a National Weather Service (NWS) damage survey. Based upon the DOW Doppler velocity data it appears that the second and third damage tracks from this supercell are produced from a single tornado. The time–height evolution of the Doppler velocity couplet spanning both tornadoes revealed a gradual increase in vertical vorticity across each tornado's core region within a few hundred meters AGL from near 0.2 to over 2.0 s−1 over a 45-min period. A corresponding reduction in vertical vorticity was observed aloft especially near 1000 m AGL where vorticity values decreased from near 1.0 to about 0.5 s−1 during this same time interval. The shear across the Doppler velocity couplet appears to undergo strengthening both at the surface and aloft during both tornadoes. An oscillatory fluctuation in the near-surface shear across the tornado core developed during the second tornado, with peak shear values as high as 206 m s−1, Doppler velocities over 106 m s−1, and peak ground-relative wind speeds reaching 118 m s−1. The period of this intensity oscillation appears to be around 120 s and was most prominent just prior to and during the passage of the tornado through Spencer. Coincident with the tornado passage through Spencer was a rapid descending of the reflectivity eye in the core of the tornado. A detailed comparison of surveyed tornado damage and radar-calculated tornado winds in Spencer is discussed in Part II.

The High-Resolution Rapid Refresh (HRRR) is a convection-allowing implementation of the Advanced Research version of the Weather Research and Forecast (WRF-ARW) Model that covers the conterminous United States and Alaska and runs hourly (for CONUS; every 3 h for Alaska) in real time at the National Centers for Environmental Prediction. The high-resolution forecasts support a variety of user applications including aviation, renewable energy, and prediction of many forms of severe weather. In this second of two articles, forecast performance is documented for a wide variety of forecast variables and across HRRR versions. HRRR performance varies across geographical domain, season, and time of day depending on both prevalence of particular meteorological phenomena and the availability of both conventional and nonconventional observations. Station-based verification of surface weather forecasts (2-m temperature and dewpoint temperature, 10-m winds, visibility, and cloud ceiling) highlights the ability of the HRRR to represent daily planetary boundary layer evolution and the development of convective and stratiform cloud systems, while gridded verification of simulated composite radar reflectivity and quantitative precipitation forecasts reveals HRRR predictive skill for summer and winter precipitation systems. Significant improvements in performance for specific forecast problems are documented for the upgrade versions of the HRRR (HRRRv2, v3, and v4) implemented in 2016, 2018, and 2020, respectively. Development of the HRRR model data assimilation and physics paves the way for future progress with operational convective-scale modeling.

Since its initial release in 2000, the Weather Research and Forecasting (WRF) Model has become one of the world’s most widely used numerical weather prediction models. Designed to serve both research and operational needs, it has grown to offer a spectrum of options and capabilities for a wide range of applications. In addition, it underlies a number of tailored systems that address Earth system modeling beyond weather. While the WRF Model has a centralized support effort, it has become a truly community model, driven by the developments and contributions of an active worldwide user base. The WRF Model sees significant use for operational forecasting, and its research implementations are pushing the boundaries of finescale atmospheric simulation. Future model directions include developments in physics, exploiting emerging compute technologies, and ever-innovative applications. From its contributions to research, forecasting, educational, and commercial efforts worldwide, the WRF Model has made a significant mark on numerical weather prediction and atmospheric science.

An object-based verification methodology for the NSSL Experimental Warn-on-Forecast System for ensembles (NEWS-e) has been developed and applied to 32 cases between December 2015 and June 2017. NEWS-e forecast objects of composite reflectivity and 30-min updraft helicity swaths are matched to corresponding reflectivity and rotation track objects in Multi-Radar Multi-Sensor system data on space and time scales typical of a National Weather Service warning. Object matching allows contingency-table-based verification statistics to be used to establish baseline performance metrics for NEWS-e thunderstorm and mesocyclone forecasts. NEWS-e critical success index (CSI) scores of reflectivity (updraft helicity) forecasts decrease from approximately 0.7 (0.4) to 0.4 (0.2) over 3 h of forecast time. CSI scores decrease through the forecast period, indicating that errors do not saturate during the 3-h forecast. Lower verification scores for rotation track forecasts are primarily a result of a high-frequency bias. Comparison of different system configurations used in 2016 and 2017 shows an increase in skill for 2017 reflectivity forecasts, attributable mainly to improvements in the forecast initial conditions. A small decrease in skill in 2017 rotation track forecasts is likely a result of sample differences between 2016 and 2017. Although large case-to-case variation is present, evidence is found that NEWS-e forecast skill improves with increasing object size and intensity.

A technique for model initialization using three-dimensional radar reflectivity data has been developed and applied within the NOAA 13-km Rapid Refresh (RAP) and 3-km High-Resolution Rapid Refresh (HRRR) regional forecast systems. This technique enabled the first assimilation of radar reflectivity data for operational NOAA forecast models, critical especially for more accurate short-range prediction of convective storms. For the RAP, the technique uses a diabatic digital filter initialization (DFI) procedure originally deployed to control initial inertial gravity wave noise. Within the forward-model integration portion of diabatic DFI, temperature tendencies obtained from the model cloud/precipitation processes are replaced by specified latent heating–based temperature tendencies derived from the three-dimensional radar reflectivity data, where available. To further refine initial conditions for the convection-allowing HRRR model, a similar procedure is used in the HRRR, but without DFI. Both of these procedures, together called the “Radar-LHI” (latent heating initialization) technique, have been essential for initialization of ongoing precipitation systems, especially convective systems, within all NOAA operational versions of the 13-km RAP and 3-km HRRR models extending through the latest implementation upgrade at NCEP in 2020. Application of the latent heat–derived temperature tendency induces a vertical circulation with low-level convergence and upper-level divergence in precipitation systems. Retrospective tests of the Radar-LHI technique show significant improvement in short-range (0–6 h) precipitation system forecasts, as revealed by reflectivity verification scores. Results presented document the impact on HRRR reflectivity forecasts of the radar reflectivity initialization technique applied to the RAP alone, HRRR alone, and both the RAP and HRRR.

Accurate cloud and precipitation forecasts are a fundamental component of short-range data assimilation/model prediction systems such as the NOAA 3-km High-Resolution Rapid Refresh (HRRR) or the 13-km Rapid Refresh (RAP). To reduce cloud and precipitation spin-up problems, a non-variational assimilation technique for stratiform clouds was developed within the Gridpoint Statistical Interpolation (GSI) data assimilation system. One goal of this technique is retention of observed stratiform cloudy and clear 3D volumes into the subsequent model forecast. The cloud observations used include cloud-top data from satellite brightness temperatures, surface-based ceilometer data, and surface visibility. Quality control, expansion into spatial information content, and forward operators are described for each observation type. The projection of data from these observation types into an observation-based cloud-information 3D gridded field is accomplished via identification of cloudy, clear, and cloud-unknown 3D volumes. Updating of forecast background fields is accomplished through clearing and building of cloud water and cloud ice with associated modifications to water vapor and temperature. Impact of the cloud assimilation on short-range forecasts is assessed with a set of retrospective experiments in warm and cold seasons using the RAPv5 model. Short-range (1-9h) forecast skill is improved in both seasons for cloud ceiling and visibility and for 2-m temperature in daytime and with mixed results for other measures. Two modifications were introduced and tested with success: use of prognostic subgrid-scale cloud fraction to condition cloud building (in response to a high bias) and removal of a WRF-based rebalancing.