01-05 December 2025
INCOIS, Hyderabad, India.
| Abstract Submission No. | ABS-03-0295 |
| Title of Abstract | Strategies of ensemble based coupled data assimilation for improved weather and short-term climate predictions |
| Authors | SREENIVAS PENTAKOTA*, PUSHPALATHA T, Anuj Gupta, DEBASIS K, SAGAR V GADE |
| Organisation | CEOAS, University of Hyderabad |
| Address | CEOAS, University of Hyderabad Hyderabad, Telangana, India Pincode: 500046 E-mail: sreenivas83@uohyd.ac.in |
| Country | India |
| Presentation | Oral |
| Abstract | Early prediction of sub-seasonal to inter-annual variations in Indian summer monsoon rainfall has multifaceted benefits (e.g., agriculture, economy, etc.). Hence any significant improvement in the prediction skill could be highly appreciated. The Ocean and Atmosphere observations have increased tremendously during recent decades due to satellites and improved observational networks. Incorporating the accurate state of the Earth system's components as the best initial conditions to the prediction model is imperative in minimizing forecast errors. Targeting a seamless prediction system, an ensemble-based flow-dependent coupled data assimilation system is developed for Climate Forecast System version 2 (CFSv2), namely the Indian Institute of Tropical Meteorology, University of Maryland- Weakly Coupled Analysis (IWCA). The quality of IWCA coupled analysis over other state of the art coupled and uncoupled analysis is reported in this study. Further 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 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. |
| Are you part of IIOE-2 endorsed project | no |
| Keywords | Coupled data assimilation, Ensemble methods, Kalman Filter, monsoon prediction |
| For Awards | no |