| Abstract Submission No. | ABS-14-0257 |
| Title of Abstract | Impact of LETKF-based coupled Data Assimilation on Seasonal Prediction of Indian Summer Monsoon |
| Authors | Sagar Vinod Gade* |
| Organisation | IITM Pune |
| Address | A-601, Riverine Greens, Pashan Pune, Maharashtra, India Pincode: 411021 E-mail: sagargade.jrf@tropmet.res.in |
| Country | India |
| Presentation | Oral |
| Abstract | This study investigates the effectiveness of advanced Data Assimilation (DA) methods in improving seasonal predictions of the Indian Summer Monsoon (ISM) by examining two extreme event cases. The coupled reanalysis products, namely the Indian Institute of Tropical Meteorology, University of Maryland-Weakly Coupled Analysis (IWCA), and the Climate Forecast System Reanalysis (CFSR), and the uncoupled reanalysis products from NCMRWF-GFS and INCOIS-GODAS. Given the increasing frequency and severity of droughts and floods, accurately predicting extreme events is crucial for effective preparedness and mitigation. The IWCA implements the local ensemble transform Kalman filter, incorporating theoretically advanced flow-dependency features 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 improved prediction skill (~22%) compared to CFSR predictions, with a gain of one month lead time. The enhanced analysis quality and cross-domain equilibrium in IWCA reduce initial shocks in springtime predictions. Further, the sustained ensemble consistency aided in simulating the variability better and improved the seasonal forecasts. The study strongly advocates adopting advanced CDA methods for seasonal monsoons and probable seamless predictions. Hindcasts using coupled and uncoupled Initial Conditions (ICs) from the CFSv2 model are generated to assess their respective predictive capabilities for extreme events. Notably, the IWCA forecast captured consecutive years of Indian monsoon droughts, excess rainfall events, and associated atmospheric and oceanic conditions. This demonstrates the enhanced forecasting ability provided by advanced DA methods in accurately predicting the ISM and facilitating better preparedness and mitigation measures for extreme events. |
| Keywords | Coupled data assimilation, seasonal prediction of ISM, extreme event prediction, LETKF data assimilation |
| For Awards | yes |