Abstract
The Rapid Refresh Forecast System (RRFS) is currently under development and aims to replace the National Centers for Environmental Prediction (NCEP) operational suite of regional and convective scale modeling systems in the next upgrade. In order to achieve skillful forecasts comparable to the current operational suite, each component of the RRFS needs to be configured through exhaustive testing and evaluation. The current data assimilation component uses the Gridpoint Statistical Interpolation (GSI) system. In this study, various data assimilation algorithms and configurations in GSI are assessed for their impacts on RRFS analyses and forecasts of a squall line over Oklahoma on 4 May 2020. Results show that a baseline RRFS run without data assimilation is able to represent the observed convection, but with stronger cells and large location errors. With data assimilation, these errors are reduced, especially in the 4 and 6 h forecasts using 75 % of the ensemble background error covariance (BEC) and with the supersaturation removal function activated in GSI. Decreasing the vertical ensemble localization radius in the first 10 layers of the hybrid analysis results in overall less skillful forecasts. Convection and precipitation are overforecast in most forecast hours when using planetary boundary layer pseudo-observations, but the root mean square error and bias of the 2 h forecast of 2 m dew point temperature are reduced by 1.6 K during the afternoon hours. Lighter hourly accumulated precipitation is predicted better when using 100 % ensemble BEC in the first 4 h forecast, but heavier hourly accumulated precipitation is better predicted with 75 % ensemble BEC. Our results provide insight into current capabilities of the RRFS data assimilation system and identify configurations that should be considered as candidates for the first version of RRFS.
Highlights
The increase in computational resources over the last several decades has allowed a considerable increase in horizontal resolution in numerical weather prediction (NWP) (e.g., Bauer et al, 2015; Yano et al, 2018)
Convection and precipitation are overforecast in most forecast hours when using planetary boundary layer pseudo-observations, but the root mean square error and bias of the 2 h forecast of 2 m dew point temperature are reduced by 1.6 K during the afternoon hours
Lighter hourly accumulated precipitation is predicted better when using 100 % ensemble background error covariance (BEC) in the first 4 h forecast, but heavier hourly accumulated precipitation is better predicted with 75 % ensemble BEC
Summary
The increase in computational resources over the last several decades has allowed a considerable increase in horizontal resolution in numerical weather prediction (NWP) (e.g., Bauer et al, 2015; Yano et al, 2018). In the framework of the 2018 NOAA Hazardous Weather Testbed Spring Forecasting Experiment, Gallo et al (2021) discussed the strengths as well as elements that need improvement in FV3-based convection-allowing models when compared to HRRR, highlighting the overproduction of high reflectivity values (45 dBZ) in the storms. Forecasts at such scales strongly depend on the quality of the initial conditions and the ability of the analysis algorithm to provide accurate state estimates of finescale spatiotemporal structures that are of inherent interest in convection-allowing NWP, such as ongoing convection, complex circulations associated with subtle boundaries (e.g. dry lines), etc To achieve such analyses with reasonable fidelity, dense and accurate observations are needed in the data assimilation window. The workflow used to streamline all components of the system and the cycling configuration are presented
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