Submitted as: model evaluation paper 05 Oct 2021

Submitted as: model evaluation paper | 05 Oct 2021

Review status: this preprint is currently under review for the journal GMD.

Assessment of the data assimilation framework for the Rapid Refresh Forecast System v0.1 and impacts on forecasts of convective storms

Ivette H. Banos1, Will D. Mayfield2, Guoqing Ge3, Luiz F. Sapucci4, Jacob R. Carley5, and Louisa Nance2 Ivette H. Banos et al.
  • 1Graduate Program in Meteorology, National Institute for Space Research, São José dos Campos, São Paulo, Brazil
  • 2National Center for Atmospheric Research, Boulder, CO, USA
  • 3NOAA Global Systems Laboratory, and Cooperative Institute for Research in Environmental Sciences, CU Boulder, Boulder, CO, USA
  • 4Center for Weather Forecasts and Climate Studies, National Institute for Space Research, São Paulo, Brazil
  • 5NOAA/NCEP Environmental Modeling Center, College Park, MD, USA

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.

Ivette H. Banos et al.

Status: final response (author comments only)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on gmd-2021-289', Anonymous Referee #1, 11 Nov 2021
  • RC2: 'Comment on gmd-2021-289', Anonymous Referee #2, 20 Nov 2021
  • RC3: 'Comment on gmd-2021-289', Anonymous Referee #3, 23 Nov 2021

Ivette H. Banos et al.

Ivette H. Banos et al.


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Short summary
A prototype of NOAA next generation rapidly-updated, convection-allowing ensemble forecast system, or Rapid Refresh Forecast System (RRFS), is extensively tested and evaluated and the impact of using data assimilation on forecasts of convective storms is examined. Although the convection in RRFS tends to be overestimated in intensity and underestimated in its extent, the use of data assimilation proves to be crucial to improve short term forecasts of storms and precipitation in RRFS.