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Application of lake models coupled within earth-system prediction models, especially for predictions from days to weeks, requires accurate initialization of lake temperatures. Commonly used methods to initialize lake temperatures include interpolation of global sea-surface temperature (SST) analyses to inland lakes, daily satellite-based observations, or model-based reanalyses. However, each of these methods have limitations in capturing the temporal characteristics of lake temperatures (e.g., effects of anomalously warm or cold weather) for all lakes within a geographic region and/or during extended cloudy periods. An alternative lake-initialization method was developed which uses two-way-coupled cycling of a small-lake model within an hourly data assimilation system of a weather prediction model. The lake model simulated lake temperatures were compared with other estimates from satellite and in situ observations and interpolated-SST data for a multi-month period in 2021. The lake cycling initialization, now applied to two operational US NOAA weather models, was found to decrease errors in lake surface temperature from as much as 5–10 K vs. interpolated-SST data to about 1–2 K compared to available in situ and satellite observations. [ABSTRACT FROM AUTHOR]

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. SIGNIFICANCE STATEMENT: The large forecast uncertainty of convective situations, even at short lead times, coupled with the hazardous weather they produce, makes convective storm prediction one of the most significant shortrange forecast challenges confronting the operational numerical weather prediction community. Prediction of heavy precipitation events also requires accurate initialization of precipitation systems. An innovative assimilation technique using radar reflectivity data to initialize NOAA operational weather prediction models is described. This technique, which uses latent heating specified from radar reflectivity (and can accommodate lightning data and other convection/precipitation indicators), was first implemented in 2009 at NOAA/NCEP and continues to be used in 2022 in the NCEP-operational RAP and HRRR models, making it a backbone of the NOAA rapidly updated numerical weather prediction capability. [ABSTRACT FROM AUTHOR]

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]

Application of lake models coupled within earth-system prediction models, especially for short-term predictions from days to weeks, requires accurate initialization of lake temperatures. Here, we describe a lake initialization method by cycling within an hourly updated weather prediction model to constrain lake temperature evolution. We compare these simulated lake temperature values with other estimates from satellite and in situ and interpolated-SST data sets for a multi-month period in 2021. The lake cycling initialization, now applied to two operational US NOAA weather models, was found to decrease errors in lake temperature from as much as 5-10 K (using interpolated-SST data) to about 1-2 K (comparing with available in situ and satellite observations. [ABSTRACT FROM AUTHOR]

Subgrid variability of snow is important in studying surface‐atmosphere interactions as it affects grid‐scale processes. However, this dynamic variability is currently not well‐represented in most land‐surface models (LSMs). A stochastic snow model using the Fokker‐Planck Equation (FPE) has been developed specifically for representing subgrid variability of snow. In this study, the FPE snow model was coupled into the Rapid Update Cycle (RUC) LSM, which provides lower boundary conditions for the operational NOAA Rapid Refresh and High‐Resolution Rapid Refresh weather prediction systems. This coupled land‐surface model with subgrid snow processes is named the RUC‐Stochastic Snow (SS) LSM. The performance of RUC‐SS LSM and RUC LSM is analyzed in detail in initial offline single‐column testing over two 13‐km × 13‐km grid cells. The results show that RUC‐SS LSM has a much better capability in estimating snow cover fraction than RUC LSM, especially during the late part of the snow‐melting season. The simulated duration of the partially snow‐covered period at the snow‐melting stage is extended from a few days with RUC LSM to about one month with the RUC‐SS LSM, thus improving prediction of surface energy budget components. For example, the latent and sensible heat fluxes and skin temperature increase gradually in the RUC‐SS LSM during the transition from full coverage to snow‐free conditions. In contrast, this transition happens too quickly in RUC LSM, and the energy‐budget components change too abruptly. The results of RUC‐SS demonstrate its capability to account for subgrid variability of snow cover and show that considering subgrid variability of snow in LSMs is important for simulating surface‐atmosphere interactions. Key Points: A stochastic snow model is integrated into a land‐surface model to represent snow subgrid variability and its change over timeThis subgrid snow treatment is tested in an offline mode of the land‐surface model to demonstrate its effectivenessThe effects of snow subgrid variability on energy components and skin temperature in land‐surface models are quantified [ABSTRACT FROM AUTHOR]

The Rapid Refresh (RAP) and High-Resolution Rapid Refresh (HRRR), both operational at NOAA's National Centers for Environmental Prediction (NCEP) use the Thompson et al. mixed-phase bulk cloud microphysics scheme. This scheme permits predicted surface precipitation to simultaneously consist of rain, snow, and graupel at the same location under certain conditions. Here, the explicit precipitation-type diagnostic method is described as used in conjunction with the Thompson et al. scheme in the RAP and HRRR models. The postprocessing logic combines the explicitly predicted multispecies hydrometeor data and other information from the model forecasts to produce fields of surface precipitation type that distinguish between rain and freezing rain, and to also portray areas of mixed precipitation. This explicit precipitation-type diagnostic method is used with the NOAA operational RAP and HRRR models. Verification from two winter seasons from 2013 to 2015 is provided against METAR surface observations. An example of this product from a January 2015 south-central United States winter storm is also shown. [ABSTRACT FROM AUTHOR]

Because of limitations of variational and ensemble data assimilation schemes, resulting analysis fields exhibit some noise from imbalance in subsequent model forecasts. Controlling finescale noise is desirable in the NOAA's Rapid Refresh (RAP) assimilation/forecast system, which uses an hourly data assimilation cycle. Hence, a digital filter initialization (DFI) capability has been introduced into the Weather Research and Forecasting Model and applied operationally in the RAP, for which hourly intermittent assimilation makes DFI essential. A brief overview of the DFI approach, its implementation, and some of its advantages are discussed. Results from a 1-week impact test with and without DFI demonstrate that DFI is effective at reducing high-frequency noise in short-term operational forecasts as well as providing evidence of reduced errors in the 1-h mass and momentum fields. However, DFI is also shown to reduce the strength of parameterized deep moist convection during the first hour of the forecast. [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]