Impact of the Ensemble Kalman Filter Based Coupled Data Assimilation System on Seasonal Prediction of Indian Summer Monsoon Rainfall

• Published in: Geophysical Research Letters
• Main Authors: Sagar V. Gade, Pentakota Sreenivas, Suryachandra A. Rao, Ankur Srivastava, Maheswar Pradhan
• Format: Article
• Language: English
• Published: Wiley 2022-08-01
• Subjects: seasonal prediction; Indian summer monsoon; coupled data assimilation; LETKF; CFSv2; ensemble methods
• Online Access: https://doi.org/10.1029/2021GL097184
• ISSN: 0094-8276; 1944-8007

Abstract The sensitivity of seasonal prediction (June to September) of Indian monsoon to initial state from two variants of coupled data assimilation (CDA) products, viz. the Climate Forecast System (CFS) Reanalysis (CFSR) and Indian Institute of Tropical Meteorology, University of Maryland‐ Weakly Coupled Analysis (IWCA) is explored in this study. The IWCA implements the local ensemble transform Kalman filter, and incorporates theoretically advanced features of flow‐dependency and ensemble‐based analysis compared to CFSR. The CFS version‐2 predictions using IWCA simulate the large‐scale monsoon features, and convection centers well, and improve prediction skills compared to CFSR predictions. The enhanced analysis quality and Ocean‐Atmospheric cross‐domain equilibrium in IWCA reduce initial shocks in springtime predictions. Further, the sustained ensemble consistency aided to simulate the variability better and improved the seasonal predictions. The study strongly advocates the adaptation of advanced CDA methods for seasonal monsoon and probable seamless predictions.