The High-Resolution Rapid Refresh (HRRR): An Hourly Updating Convection-Allowing Forecast Model. Part I: Motivation and System Description

Abstract

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.

Forecasting lake‐/sea‐effect snowstorms, advancement, and challenges

“…Additional understanding through emerging technological tools in early NWP (Lavoie, 1972) and maturing operational NWP (Niziol et al, 1995) certainly assisted forecasters in the United States to better anticipate small‐scale convective snowstorms. As numerical guidance has progressed into the current generation of high‐resolution models that allow representation of convections (so‐called Convection Allowing Models or CAMs), greater detail of convective storm behavior can at least be partially represented, which can help forecasters communicate event specifics (Benjamin et al, 2016; Dowell et al, 2022; Olsson et al, 2018). Furthermore, available computational resources are now sufficient to support multiple simulations for a forecast cycle using such a high‐resolution model in an operational environment.…”

“…F I G U R E A Evaporation from Lake Erie and resulting lake-effect snow band, a view from the City Hall of Buffalo, NY, November 18, 2014 (Derek Gee/Buffalo News) representation of convections (so-called Convection Allowing Models or CAMs), greater detail of convective storm behavior can at least be partially represented, which can help forecasters communicate event specifics (Benjamin et al, 2016;Dowell et al, 2022;Olsson et al, 2018). Furthermore, available computational resources are now sufficient to support multiple simulations for a forecast cycle using such a high-resolution model in an operational environment.…”

“…This makes it difficult to accurately represent them in a single modeling framework. In most operational settings, at the time of writing, the highest resolution models are using an approximately 3 km horizontal grid (Benjamin et al, 2016; Dowell et al, 2022). The Warn‐on‐Forecast methods developed by the National Oceanic and Atmospheric Administration (NOAA) even utilize 1 km grid spacing over selected domains and short forecast periods of a few hours.…”

Evaluation of a cloudy cold-air pool in the Columbia River Basin in different versions of the HRRR model

“…Besides the conduction of a comprehensive 18-month long field campaign (Wilczak et al, 2019) and the development of support tools to assist the industry in wind power forecasting, model development efforts were a key component of WFIP2 (Olson et al, 2019b). The basis for the model developments were NOAA's Rapid Refresh (RAP, Benjamin et al (2016)) and High-Resolution Rapid Refresh (HRRR, Dowell et al (2022)) models. In this study, we used three different versions of the HRRR model to evaluate how the model developments impacted the characteristics of a strong and persistent cold pool event in the Columbia River Basin, which occurred within a 10-day period from 10-19 January 2017.…”

Inland lake temperature initialization via coupled cycling with atmospheric data assimilation

“…Thus, a second major improvement in 2020 with lake representation in the NOAA 3 km HRRR model occurred with the implementation of lagged data coupling with the 3-D hydrodynamic-ice model for the much larger Laurentian Great Lakes as described by Fujisaki-Manome et al (2020). These new improved lake treatments are in the newer HRRR version 4 (HRRRv4) replacing the previous HRRRv3 (differences described in Dowell et al, 2022;hereafter D22).…”