Toward Convective-Scale Prediction within the Next Generation Global Prediction System

By Linjiong Zhou, Shian‐Jiann Lin, Jan‐Huey Chen, L. Harris, Xi Chen, S. Rees. Bulletin of The American Meteorological Society - (BAMS), 2019

139 citations recorded by the source corpus.

The Geophysical Fluid Dynamics Laboratory (GFDL) has developed a new variable-resolution global model with the ability to represent convective-scale features that serves as a prototype of the Next Generation Global Prediction System (NGGPS). The goal of this prediction system is to maintain the skill in large-scale features while simultaneously improving the prediction skill of convectively driven mesoscale phenomena. This paper demonstrates the new capability of this model in convective-scale prediction relative to the current operational Global Forecast System (GFS). This model uses the stretched-grid functionality of the Finite-Volume Cubed-Sphere Dynamical Core (FV3) to refine the global 13-km uniform-resolution model down to 4-km convection-permitting resolution over the contiguous United States (CONUS), and implements the GFDL single-moment 6-category cloud microphysics to improve the representation of moist processes. Statistics gathered from two years of simulations by the GFS and select configurations of the FV3-based model are carefully examined. The variable-resolution FV3-based model is shown to possess global forecast skill comparable with that of the operational GFS while quantitatively improving skill and better representing the diurnal cycle within the high-resolution area compared to the uniform mesh simulations. Forecasts of the occurrence of extreme precipitation rates over the southern Great Plains are also shown to improve with the variable-resolution model. Case studies are provided of a squall line and a hurricane to demonstrate the effectiveness of the variable-resolution model to simulate convective-scale phenomena.

Subfields: Atmospheric Science, Global and Planetary Change

View Paper: https://www.semanticscholar.org/paper/21fe7ed67f428d4a764b2f99894a369d1794e933

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Atmospheric Science

1. Stephan Rasp
2. Stephan Hoyer
3. I. Pisso

In Global and Planetary Change

1. Stephan Rasp
2. Stephan Hoyer
3. I. Pisso

Citations in the corpus

• Neural general circulation models for weather and climate (2023, background): “Machine-learning atmospheric models also require considerably less code, for example GraphCast 3 has 5,417 lines versus 376,578 lines for the National Oceanic and Atmospheric Administration’s FV3 atmospheric model 15 (see Supplementary Information section A for details).”