Xuguang Wang;
Junkyung Kay;
Bo Huang
School of Meteorology, University of Oklahoma, Norman, OK
Daryl Kleist;
Ting Lei
NCEP Environmental Modeling Center (EMC), College Park, MD
The 8th EnKF Data Assimilation Workshop
Development of the GSI-based Hybrid 4DEnVar System with Multi-resolution Ensembles and Multi-scale Covariance Localization for Global Numerical Weather Prediction
Talk:
Wang_Kay_Bo_2018_EnKF_v1_xwang_v4.pptx
GSI-based 4DEnVar is further developed to have the capability to ingest ensemble at multi-resolutions. Due to the constraints of limited computational resources, ensemble background is run at a lower resolution in the operational GSI–based 4DEnVar for global numerical prediction. In the GSI-based operational 4DEnVar update, analysis increments including both the flow dependent and static components are generated at a reduced low resolution (hereafter, Single Resolution-Low, SR-Low). In the mean time, current GSI-based 4DEnVar has a capability to generate the analysis increment at high resolution using high-resolution static background error covariance (BEC) and low-resolution ensemble BEC (hereafter, Dual Resolution, DR). Although these SR-Low and DR configurations can save computational costs, ensemble does not contribute to the highly flow-dependent analysis increment at smaller scales in both SR-Low and DR. To remedy these problems, we further develop the 4DEnVar algorithm so that a mixture of low-resolution and high resolution ensemble BEC (hereafter, Multi Resolution Ensemble, MR-ENS) is used. The new capability allows 4DEnVar to ingest ensembles from various sources at various resolutions. The newly developed MR-ENS 4DEnVar system is examined during a 5-week period, 2400 UTC July – 0000 UTC 30 August 2013. Power spectrum of analysis increment shows that the MR-ENS analysis increment has larger power than the DR and SR-Low due to the contribution from the high-resolution ensemble BEC at small scales. Results show the MR-ENS provided better global and hurricane track forecasts compared to DR and SR-Low. To further reveal the impact of MR-ENS, an experiment where all ensemble members are run at the high resolution is conducted (HR). Although MR-ENS is much less expensive than HR, the accuracy of global forecasts by MR-ENS is comparable to HR.
In addition, with the addition of all sky radiance assimilation and with the increased model resolution, a large range of scales are resolved and directly updated by GSI 4DEnVar.
Therefore a scale-dependent covariance localization method is implemented in GSI 4DEnVar. The impact of the multi-scale localization with and without considering the cross scales correlation is examined and revealed through a warm season cycling data assimilation experiment.
GSI-based 4DEnVar is further developed to have the capability to ingest ensemble at multi-resolutions. Due to the constraints of limited computational resources, ensemble background is run at a lower resolution in the operational GSI–based 4DEnVar for global numerical prediction. In the GSI-based operational 4DEnVar update, analysis increments including both the flow dependent and static components are generated at a reduced low resolution (hereafter, Single Resolution-Low, SR-Low). In the mean time, current GSI-based 4DEnVar has a capability to generate the analysis increment at high resolution using high-resolution static background error covariance (BEC) and low-resolution ensemble BEC (hereafter, Dual Resolution, DR). Although these SR-Low and DR configurations can save computational costs, ensemble does not contribute to the highly flow-dependent analysis increment at smaller scales in both SR-Low and DR. To remedy these problems, we further develop the 4DEnVar algorithm so that a mixture of low-resolution and high resolution ensemble BEC (hereafter, Multi Resolution Ensemble, MR-ENS) is used. The new capability allows 4DEnVar to ingest ensembles from various sources at various resolutions. The newly developed MR-ENS 4DEnVar system is examined during a 5-week period, 2400 UTC July – 0000 UTC 30 August 2013. Power spectrum of analysis increment shows that the MR-ENS analysis increment has larger power than the DR and SR-Low due to the contribution from the high-resolution ensemble BEC at small scales. Results show the MR-ENS provided better global and hurricane track forecasts compared to DR and SR-Low. To further reveal the impact of MR-ENS, an experiment where all ensemble members are run at the high resolution is conducted (HR). Although MR-ENS is much less expensive than HR, the accuracy of global forecasts by MR-ENS is comparable to HR.
In addition, with the addition of all sky radiance assimilation and with the increased model resolution, a large range of scales are resolved and directly updated by GSI 4DEnVar.
Therefore a scale-dependent covariance localization method is implemented in GSI 4DEnVar. The impact of the multi-scale localization with and without considering the cross scales correlation is examined and revealed through a warm season cycling data assimilation experiment.
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