Improving Global Weather Prediction in GFDL SHiELD Through an Upgraded GFDL Cloud Microphysics Scheme

Linjiong Zhou, Lucas Harris, Jan Huey Chen, Kun Gao, Huan Guo, Baoqiang Xiang, Mingjing Tong, J. Jacob Huff, Matthew Morin

Research output: Contribution to journalArticlepeer-review

30 Scopus citations

Abstract

We describe the third version of the Geophysical Fluid Dynamics Laboratory cloud microphysics scheme (GFDL MP v3) implemented in the System for High-resolution prediction on Earth-to-Local Domains (SHiELD). Compared to the GFDL MP v2, the GFDL MP v3 is entirely reorganized, optimized, and modularized into functions. The particle size distribution (PSD) of all hydrometeor categories is redefined to better mimic observations, and the cloud droplet number concentration (CDNC) is calculated from the Modern-Era Retrospective analysis for Research and Applications, version 2 (MERRA2) aerosol data. In addition, the GFDL MP has been redesigned so all processes use the redefined PSD to ensure overall consistency and easily permit the introduction of new PSDs and microphysical processes. A year's worth of global 13-km, 10-day weather forecasts were performed with the new GFDL MP. Compared to the GFDL MP v2, the GFDL MP v3 significantly improves SHiELD's predictions of geopotential height, air temperature, and specific humidity in the Troposphere, as well as the high, middle and total cloud fractions and the liquid water path. The predictions are improved even further by the use of reanalysis aerosol data to calculate CDNC, and also by using the more realistic PSD available in GFDL MP v3. However, the upgrade of the GFDL MP shows little impact on the precipitation prediction. Degradations caused by the new scheme are discussed and provide a guide for future GFDL MP development.

Original languageEnglish
Article numbere2021MS002971
JournalJournal of Advances in Modeling Earth Systems
Volume14
Issue number7
DOIs
StatePublished - Jul 2022

Keywords

  • aerosol and cloud
  • cloud microphysics
  • numerical weather prediction
  • particle size distribution

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