Improving Monsoon Prediction With a Coupled EnKF Data Assimilation System
Our project, supported by the India Monsoon Mission Directorate, has the ultimate goal of developing a strongly coupled ocean-atmosphere data assimilation based on NCEP's Coupled Forecasting System coupled with the Local Ensemble Transform Kalman Filter (CFS-LETKF). The models will be coupled and vertical localization of the LETKF will be used for the DA coupling. This will allow the near surface atmospheric model variables to be influenced by the ocean observations, and the near surface ocean model variables to be influenced by the atmospheric observations. Student Travis Sluka is developing both a weakly and a strongly coupled DA for a simpler coupled GCM (SPEEDY-NEMO, kindly provided by Fred Kucharski, ICTP). This will allow to efficiently explore different approaches for coupled DA.
As part of this project, Dr. Guo-Yuan Lien developed an advanced GFS-LETKF, documented in http://code.google.com/p/miyoshi/wiki/GfsLetkf With this system, Dr. Lien achieved for the first time successful assimilation of TRMM/TMPA precipitation (Lien, 2014). In addition the implementation of the Ensemble Forecast Sensitivity to Observations (EFSO, Hotta, 2014) allows to select the precipitation observations that improve the short-range forecasts. The experiments that Lien carried out show that the assimilation in a cycle has a positive impact that increases with time.