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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. SIGNIFICANCE STATEMENT: NOAA's operational hourly updating, convection-allowing model, the High-Resolution Rapid Refresh (HRRR), is a key tool for short-range weather forecasting and situational awareness. Improvements in assimilation of weather observations, as well as in physics parameterizations, have led to improvements in simulated radar reflectivity and quantitative precipitation forecasts since the initial implementation of HRRR in September 2014. Other targeted development has focused on improved representation of the diurnal cycle of the planetary boundary layer, resulting in improved near-surface temperature and humidity forecasts. Additional physics and data assimilation changes have led to improved treatment of the development and erosion of low-level clouds, including subgrid-scale clouds. The final version of HRRR features storm-scale ensemble data assimilation and explicit prediction of wildfire smoke plumes. [ABSTRACT FROM AUTHOR]

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 convectivescale modeling. SIGNIFICANCE STATEMENT: NOAA's operational hourly updating convection-allowing model, the High-Resolution Rapid Refresh (HRRR), is a key tool for short-range weather forecasting and situational awareness. Improvements in assimilation of weather observations, as well as in physics parameterizations, has led to improvements in simulated radar reflectivity and quantitative precipitation forecasts since the initial implementation of HRRR in September 2014. Other targeted development has focused on improved representation of the diurnal cycle of the planetary boundary layer, resulting in improved near-surface temperature and humidity forecasts. Additional physics and data assimilation changes have led to improved treatment of the development and erosion of low-level clouds, including subgrid-scale clouds. The final version of HRRR features storm-scale ensemble data assimilation and explicit prediction of wildfire smoke plumes. [ABSTRACT FROM AUTHOR]

A stochastic parameter perturbation (SPP) scheme consisting of spatially and temporally varying perturbations of uncertain parameters in the Grell-Freitas convective scheme and the Mellor-Yamada-Nakanishi- Niino planetary boundary scheme was developed within the Rapid Refresh ensemble system based on the Weather Research and Forecasting Model. Alone the stochastic parameter perturbations generate insufficient spread to be an alternative to the operational configuration that utilizes combinations of multiple parameterization schemes. However, when combined with other stochastic parameterization schemes, such as the stochastic kinetic energy backscatter (SKEB) scheme or the stochastic perturbation of physics tendencies (SPPT) scheme, the stochastic ensemble system has comparable forecast performance. An additional analysis quantifies the added value of combining SPP and SPPT over an ensemble that uses SPPT only, which is generally beneficial, especially for surface variables. The ensemble combining all three stochastic methods consistently produces the best spread-skill ratio and generally outperforms the multiphysics ensemble. The results of this study indicate that using a single-physics suite ensemble together with stochastic methods is an attractive alternative to multiphysics ensembles and should be considered in the design of future high-resolution regional and global ensembles. [ABSTRACT FROM AUTHOR]

The RUC Land-Surface Model (LSM) is used as a land surface component in the NOAA operational Rapid Refresh (RAP) short-range weather prediction model over North America domain and in the High-Resolution Rapid Refresh (HRRR) over CONUS and Alaska domains. It is also implemented in the Next Generation Global Prediction System (NGGPS) as part of the advanced physics (RAP/HRRR) suite. The RUC LSM performance has been evaluated for almost two decades within the real-time operational weather prediction systems focused on storm-scale predictions for severe weather and safer aviation. Valuable feedback from the National Weather Service forecast offices has motivated further advances in RUC LSM towards providing a more accurate lower boundary condition, including in snow-covered regions. One of them is the new treatment of energy budget for grid cells partially covered with snow. Sub-grid variability of snow depth is important for the evolution of the atmospheric boundary layer, since spatial distribution of snow is generally very heterogeneous and its properties are quite different from properties of snow-free surfaces. In this paper, a new "mosaic" approach considers snow-covered and non-snow-covered portions of a grid cell independently, and independently determined surface fluxes are aggregated to feed back into the surface-layer scheme at the end of each time step. This new approach removes the constraint of keeping skin temperature of partially covered with snow grid cells at or below the freezing point, and helps to reduce cold biases in these regions. It also helps to improve the evolution of cycled model snow cover together with the new semi-empirical formulations for the density of frozen precipitation depending on hydrometeor type and surface temperature. With the new "mosaic" approach for patchy snow, specification of a snow-covered fraction of a grid cell becomes critical. A scheme that gives more objective evaluation of snow fraction and represents sub-grid variability of snow depth has been implemented in RUC LSM. This new parameterization scheme is a stochastic model that describes spatial distribution of snow through an advection-diffusion equation, which is also called Fokker-Planck Equation (FPE). This enhancement to the RUC snow model, referred to as RUC-FPE LSM, is currently being validated through participation in the new Earth System Model-Snow Model Intercomparison Project (ESM-SnowMIP), and also is being tested in the experimental versions of RAP/HRRR at the NOAA Earth System Research Laboratory (ESRL). The RUC-FPE LSM is planned to be implemented in the next operational versions of RAP and HRRR at the NOAA National Centers for Environmental Prediction (NCEP). Description of the snow model enhancements and results from RUC-FPE LSM validation will be presented at the meeting. [ABSTRACT FROM AUTHOR]

Abstract: Land surface parameterization schemes play a significant role in the accuracy of meso-local scale numerical models by accounting for the exchange of energy and water between the soil and the atmosphere. The role of land surface processes during large-scale cold-pooling events was studied with two land surface schemes (LSMs) in the Advanced Research Weather Forecasting model (ARW). Model evaluation was complex due to the surface and boundary layer interactions at different temporal and spatial scales as revealed by a scale dependent variance analysis. Wavelet analysis was used for the first time to analyze the model errors with specific focus on land surface processes. The ARW model was also evaluated for the formation of a low-level jet (LLJ). It is shown that vertical resolution in the model boundary layer played a significant role in determining the characteristics of LLJ, which influenced the lower boundary layer structure and moisture distribution. The results showed that the simulated low-level jet over southern Georgia was sensitive to the land surface parameterization and led to a significant difference in the boundary layer exchange. The jet shear played a crucial role in the maintenance of turbulence and weak shear caused excessive radiative cooling leading to unrealistic cold pools in the model. The results are important for regional downscaling as the excessive cold pools that are simulated in the model can go unnoticed. [ABSTRACT FROM AUTHOR]