| Subseasonal Forecasting with an Icosahedral, Vertically Quasi-Lagrangian Coupled Model. Part I: Model Overview and Evaluation of Systematic Errors - :20557 | Office of Oceanic and Atmospheric Research (OAR)
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Subseasonal Forecasting with an Icosahedral, Vertically Quasi-Lagrangian Coupled Model. Part I: Model Overview and Evaluation of Systematic Errors
  • Published Date:
    2018
  • Source:
    Monthly Weather Review, 146(5), 1601-1617.
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Subseasonal Forecasting with an Icosahedral, Vertically Quasi-Lagrangian Coupled Model. Part I: Model Overview and Evaluation of Systematic Errors
Details:
  • Description:
    The atmospheric hydrostatic Flow-Following Icosahedral Model (FIM), developed for medium-range weather prediction, provides a unique three-dimensional grid structure-a quasi-uniform icosahedral horizontal grid and an adaptive quasi-Lagrangian vertical coordinate. To extend the FIM framework to subseasonal time scales, an icosahedral-grid rendition of the Hybrid Coordinate Ocean Model (iHYCOM) was developed and coupled to FIM. By sharing a common horizontal mesh, air-sea fluxes between the two models are conserved locally and globally. Both models use similar adaptive hybrid vertical coordinates. Another unique aspect of the coupled model (referred to as FIM-iHYCOM) is the use of the Grell-Freitas scale-aware convective scheme in the atmosphere. A multiyear retrospective study is necessary to demonstrate the potential usefulness and allow for immediate bias correction of a subseasonal prediction model. In these two articles, results are shown based on a 16-yr period of hindcasts from FIM-iHYCOM, which has been providing real-time forecasts out to a lead time of 4 weeks for NOAA's Subseasonal Experiment (SubX) starting July 2017. Part I provides an overview of FIM-iHYCOM and compares its systematic errors at subseasonal time scales to those of NOAA's operational Climate Forecast System version 2 (CFSv2). Part II uses bias-corrected hindcasts to assess both deterministic and probabilistic subseasonal skill of FIM-iHYCOM. FIM-iHYCOM has smaller biases than CFSv2 for some fields (including precipitation) and comparable biases for other fields (including sea surface temperature). FIM-iHYCOM also has less drift in bias between weeks 1 and 4 than CFSv2. The unique grid structure and physics suite of FIM-iHYCOM is expected to add diversity to multimodel ensemble forecasts at subseasonal time scales in SubX.

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