For the past two decades, NOAA’s Global Systems Laboratory (GSL) developed and refined a regional hourly-updated high resolution numerical prediction system known as the High-Resolution Rapid Refresh (HRRR). This system is run operationally by NOAA and has been widely used for aviation, severe weather, renewable energy, and general short-range forecast applications. Prediction of aviation-related hazards has always been a main focus area for this model system and much of the associated model physics and data assimilation research has targeted accurate prediction of aviation hazards such as convection, low clouds and fog, icing, and turbulence. At present, GSL and the NOAA Environmental Modeling Center (EMC) are working together toward the implementation of a major upgrade to the HRRR, a new system known as the Rapid Refresh Forecast System (RRFS). This new system retains many of the same model physics and data assimilation aspects as the HRRR, but uses a new model dynamic core, the FV3, developed at the NOAA Geophysical Fluid Dynamics Laboratory (GFDL), and also has a much larger 3-km domain than the HRRR as well as an ensemble forecast system. Key components of the RRFS model system for cloud and icing prediction include customized versions of the MYNN boundary layer scheme and the Thompson aerosol-aware microphysics (including a cloud faction variable), and a non-variational cloud analysis scheme to help with the initialization of clouds from METAR and satellite-based cloud observations. Overall assessment of the RRFS vs. HRRR for cloud prediction skill, especially for aviation-related aspects (low ceilings, etc.) in the cold season, has revealed an improvement in the RRFS over the HRRR, which had a tendency to miss low stratus decks. The RRFS has continued the good HRRR skill for icing prediction, as revealed by assessments associated with the 2019 ICICLE field project data sets. As part of GSL’s aviation-related model work, grids from the HRRR have been provided to teams producing aviation-hazard specific guidance products for clouds and ceiling, icing, turbulence, and convection. At the conference, we will describe the relevant model physics and data assimilation components of the RRFS system and present results for cloud and icing forecasts.

The High-Resolution Rapid Refresh (HRRR) is a convection-allowing implementation of the Weather Research and Forecasting model (WRF-ARW) 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. 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 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. Significance StatementNOAA'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.

An accurate short-range cloud and precipitation forecast is a fundamental component of rapidly updating data assimilation/short-range model forecast systems such as the NOAA 3-km High-Resolution Rapid Refresh or the 13-km Rapid Refresh (RAP). To reduce cloud and precipitation spin-up problems, a cloud/hydrometeor non-variational assimilation technique for stratiform clouds was developed within the Gridpoint Statistical Interpolation (GSI) data assimilation system. The goal of this technique was retention into the subsequent model forecast, as appropriate, of observed stratiform cloud and observed clear 3-d volumes. New observation impact studies show that the ceiling forecasts are particularly improved by use of this cloud/hydrometeor assimilation in the HRRR/RAP model in both summer and winter season. Daytime 2m temperature and dewpoint forecasts are also improved in the summer period, important for convective storms. Improved design of the MYNN boundary-layer turbulence scheme is also shown to benefit HRRR/RAP ceiling prediction and is now also being tested in the FV3 3km stand-along regional model and in the NOAA FV3 Global Forecast System. Improved boundary-layer prediction, also through other parameterization additions for gravity-wave drag and land/lake modeling, is demonstrated and isolated to these modifications.

Biases in 2m temperature and downward shortwave radiation have been reduced from physics improvements in late 2018 for short-range (6h-24h) forecasts in the US NOAA HRRR (3km, High-Resolution Rapid Refresh) and RAP (13km, Rapid Refresh, HRRR-parent) hourly-updated models. These improvements will be implemented at the NOAA/NCEP in early 2020 These refinements are critical for improved energy (solar and wind), aviation (ceiling), and severe storm (convective environment) prediction. Previous HRRR/RAP predictions from NCEP show a deficiency in cloud attenuation and excessive precipitation. These future improvements result from better radar/cloud assimilation and better PBL physics. The introduction of 3-km ensemble data assimilation for the HRRR is the most important assimilation modification. Improved subgrid-scale clouds into the MYNN PBL scheme were shown to be essential for improved cloud and boundary-layer depth accuracy.

(1) NOAA Earth System Research Laboratory, Boulder, CO USA (stan.benjamin@noaa.gov), (3) Environment and Climate Change Canada (ECCC), Toronto, Canada (ismail.gultepe@canada.ca), (2) NOAA SSEC/CIMSS and University of Wisconsin, Madison, WI 53706, USA, (4) University of Ontario Inst. of Tech. (UOIT), Faculty of Engineering and Applied Science, Oshawa, Ont., Canada, (5) University of Ontario Inst. of Tech. (UOIT), ACE, Oshawa, Ontario, Canada, (6) NOAA/NCEP Environmental Modeling Center, and Science Applications Inter. Corp., Camp Springs, Maryland, USA

Goals and General Description The HRRRE is an experimental convective-allowing ensemble analysis and forecasting system run at NOAA/ESRL/GSD. It is being developed and tested for three main reasons: (1) improving 0-12 h high-resolution forecasts through ensemble-based, multi-scale data assimilation, (2) testing ensemble-design concepts for 0-36 h forecasts produced with a single model, and (3) providing a foundation for experimental, on-demand, very-high-resolution applications such as Warn-on-Forecast. HRRRE has been run experimentally with initial versions in 2016 and 2017. This document describes the HRRRE version that will run during the 2018 HWT Spring Experiment. The deterministic HRRR currently assimilates observations with a hybrid ensemble-variational (EnVAR) method, and the background ensemble for this assimilation is the 80-member GDAS (GFS) ensemble. One idea being tested in the HRRRE is using a higher-resolution, convective-allowing ensemble instead for assimilation. A background 1