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Cool-Season Evaluation of FV3-LAM-Based CONUS-Scale Forecasts with Physics Configurations of Experimental RRFS Ensembles

  • Timothy A. Supinie
  • , Jun Park
  • , Nathan Snook
  • , Xiao Ming Hu
  • , Keith A. Brewster
  • , Ming Xue
  • , Jacob R. Carley
  • Center for the Analysis and Prediction of Storms
  • University of Oklahoma
  • National Oceanic and Atmospheric Administration

Research output: Contribution to journalArticlepeer-review

8 Scopus citations

Abstract

To help inform physics configuration decisions and help design and optimize a multi-physics Rapid Refresh Forecasting System (RRFS) ensemble to be used operationally by the National Weather Service, five FV3-LAM-based convection allowing forecasts were run on 35 cases between October 2020 and March 2021. These forecasts used ~3-km grid spacing on a CONUS domain with physics configurations including Thompson, NSSL, and Ferrier-Aligo microphysics schemes, Noah, RUC, and NoahMP land surface models, and MYNN-EDMF, K-EDMF, and TKE-EDMF PBL schemes. All forecasts were initialized from the 0000 UTC GFS analysis and run for 84 h. Also, a subset of 8 cases were run with 15 combinations of physics options, also including the Morrison-Gettelman microphysics and Shin-Hong PBL schemes, to help attribute behaviors to individual schemes and isolate the main contributors of forecast errors. Evaluations of both sets of forecasts find that the CONUS-wide 24-h precipitation > 1 mm is positively biased across all five forecasts. NSSL microphysics displays a low bias in QPF along the Gulf Coast. Analyses show that it produces smaller raindrops prone to evaporation. Additionally, TKE-EDMF PBL in combination with Thompson microphysics displays a positive bias in precipitation over the Great Lakes and in the ocean near Florida due to higher latent heat fluxes calculated over water. Furthermore, the K-EDMF PBL scheme produces temperature errors that result in a negative bias in snowfall over the southern Mountain West. Finally, recommendations for which physics schemes to use in future suites and the RRFS ensemble are discussed.

Original languageEnglish
Pages (from-to)2379-2398
Number of pages20
JournalMonthly Weather Review
Volume150
Issue number9
DOIs
StatePublished - Sep 2022
Externally publishedYes

Keywords

  • Model errors
  • Model evaluation/performance
  • Numerical weather prediction/forecasting
  • Regional models

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