Evaluation of the hybrid gain ensemble-variational data assimilation method applied to Central Weather Bureau’s FV3GFS global weather prediction system (2/3)

Project Details

Description

CWB has advanced its global operational system toward the next-generation system, the FV3GFS, coupled with a Gridpoint Statistical Interpolation (GSI) hybrid ensemble-variational (EnVar) data assimilation system (FV3GFS-GSI). This system is planned to be operational in 2021. Different from hybridizing the background error covariance in EnVar, a three-year project is planned to explore a different possibility of hybrid data assimilation and aims to develop the hybrid gain data assimilation (HGDA) system using the ensemble and variational DA components from the FV3GFS-GSI system. In the first year, single observation experiment had been conducted to investigate the properties of each DA system, including the FV3GFS-GSI, the FV3GFS-HGDA and its component DA systems. In this year, cycling experiments will be conducted to examine the applicability and feasibility for the FV3GFS-HGDA under a semi-operational environment. Since the HGDA has two scenarios for implementation, a series of sensitivity tests are necessary to justify the optimal combination weight for both scenarios. Except for a single weight value, an altitude-dependent combination weight would be a potential option for improving the hybrids if needed. In the last year of this three-year study, we will focus on implementing the QR-HGDA algorithm to evaluate the possibility of removing the ad-hoc determined combination weight in hybrids. The newly derived orthogonal analysis increment through QR-factorization can be used to understand the characteristics of the increments in the current FV3GFS-GSI system. Through the three-year study, we are able to develop a hybrid DA system and such experiences can feedback to the FV3GFS-GSI development. The optimal goal of this study is to enhance CWB’s ability in DA research and global forecast system in Taiwan.
StatusActive
Effective start/end date24/02/2231/12/22

UN Sustainable Development Goals

In 2015, UN member states agreed to 17 global Sustainable Development Goals (SDGs) to end poverty, protect the planet and ensure prosperity for all. This project contributes towards the following SDG(s):

  • SDG 13 - Climate Action

Keywords

  • Hybrid gain data assimilation
  • FV3GFS global model
  • data assimilation

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