Impact of the Ensemble Kalman Filter Based Coupled Data Assimilation System on Seasonal Prediction of Indian Summer Monsoon Rainfall
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...
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| Format: | Article |
| Language: | English |
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Wiley
2022-08-01
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| Series: | Geophysical Research Letters |
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| Online Access: | https://doi.org/10.1029/2021GL097184 |
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| author | Sagar V. Gade Pentakota Sreenivas Suryachandra A. Rao Ankur Srivastava Maheswar Pradhan |
| author_facet | Sagar V. Gade Pentakota Sreenivas Suryachandra A. Rao Ankur Srivastava Maheswar Pradhan |
| author_sort | Sagar V. Gade |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-ac97e03474ee486597b131d4714aa829 |
| institution | OA Journals |
| issn | 0094-8276 1944-8007 |
| language | English |
| publishDate | 2022-08-01 |
| publisher | Wiley |
| record_format | Article |
| series | Geophysical Research Letters |
| spelling | doaj-art-ac97e03474ee486597b131d4714aa8292025-08-20T02:27:43ZengWileyGeophysical Research Letters0094-82761944-80072022-08-014915n/an/a10.1029/2021GL097184Impact of the Ensemble Kalman Filter Based Coupled Data Assimilation System on Seasonal Prediction of Indian Summer Monsoon RainfallSagar V. Gade0Pentakota Sreenivas1Suryachandra A. Rao2Ankur Srivastava3Maheswar Pradhan4Indian Institute of Tropical Meteorology Ministry of Earth Sciences (MoES) Pune IndiaIndian Institute of Tropical Meteorology Ministry of Earth Sciences (MoES) Pune IndiaIndian Institute of Tropical Meteorology Ministry of Earth Sciences (MoES) Pune IndiaIndian Institute of Tropical Meteorology Ministry of Earth Sciences (MoES) Pune IndiaIndian Institute of Tropical Meteorology Ministry of Earth Sciences (MoES) Pune IndiaAbstract 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.https://doi.org/10.1029/2021GL097184seasonal predictionIndian summer monsooncoupled data assimilationLETKFCFSv2ensemble methods |
| spellingShingle | Sagar V. Gade Pentakota Sreenivas Suryachandra A. Rao Ankur Srivastava Maheswar Pradhan Impact of the Ensemble Kalman Filter Based Coupled Data Assimilation System on Seasonal Prediction of Indian Summer Monsoon Rainfall Geophysical Research Letters seasonal prediction Indian summer monsoon coupled data assimilation LETKF CFSv2 ensemble methods |
| title | Impact of the Ensemble Kalman Filter Based Coupled Data Assimilation System on Seasonal Prediction of Indian Summer Monsoon Rainfall |
| title_full | Impact of the Ensemble Kalman Filter Based Coupled Data Assimilation System on Seasonal Prediction of Indian Summer Monsoon Rainfall |
| title_fullStr | Impact of the Ensemble Kalman Filter Based Coupled Data Assimilation System on Seasonal Prediction of Indian Summer Monsoon Rainfall |
| title_full_unstemmed | Impact of the Ensemble Kalman Filter Based Coupled Data Assimilation System on Seasonal Prediction of Indian Summer Monsoon Rainfall |
| title_short | Impact of the Ensemble Kalman Filter Based Coupled Data Assimilation System on Seasonal Prediction of Indian Summer Monsoon Rainfall |
| title_sort | impact of the ensemble kalman filter based coupled data assimilation system on seasonal prediction of indian summer monsoon rainfall |
| topic | seasonal prediction Indian summer monsoon coupled data assimilation LETKF CFSv2 ensemble methods |
| url | https://doi.org/10.1029/2021GL097184 |
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