Reply on RC2

Ivette Hernandez Banos

doi:10.5194/gmd-2021-289-ac2

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)
In this analysis, assimilated temperature observations include those from aircraft, surface marine synoptic stations, METeorological Aerodrome Reports (METAR), and Mesoscale Network (MESONET) observations
The analysis residuals for temperature are generally small for aircraft and METAR

Summary

Introduction

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). Developed and use high resolution models operationally for short range weather forecast guidance (e.g., Bannister et al, 2020). These models have provided more realistic forecasts of hazardous weather events where deep convection is explicitly resolved (e.g., Lean et al, 2008). In models with grid spacing less than 4 km, the deep cumulus parameterization is turned off and convection is treated explicitly, though not necessary completely resolved. Such configurations are often called convection-allowing models (e.g., Schwartz and Sobash, 2019)

Methods

Results

Conclusion