Simulation of Indian summer monsoon using the Japan Meteorological Agency's seasonal ensemble prediction system
| Autor: | Nitin Patil, D S Pai, Kailas Sonawane, Mahendra Benke, D. R. Pattanaik, O. P. Sreejith |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2015 |
| Předmět: |
Rainfall
Ocean Meteorology Forecast skill Ensemble Prediction System Project Monsoon circulation Time Indian monsoon rainfall Hindcast Southwest Monsoon Coupled Model Anomalies Sst Summer monsoon rainfall Indian Monsoon Rainfall Indian summer monsoon Climatology Ensemble prediction Climate Forecast System Empirical Prediction General Earth and Planetary Sciences Environmental science Jma Model Seasonal Forecast Of Monsoon Model Forecast Skill |
| Zdroj: | IndraStra Global. |
| ISSN: | 2381-3652 |
| Popis: | The performance of a dynamical seasonal forecast system is evaluated for the prediction of summer monsoon rainfall over the Indian region during June–September (JJAS) by using hindcast of the Japan Meteorological Agency (JMA) seasonal ensemble prediction system (EPS) model, based on five ensembles of March, April and May initial states for a period of 32 years (1979–2010). The hindcast climatology during JJAS simulates the mean monsoon circulation at lower and upper tropospheres very well in JMA model using March, April and May ensembles with a more realistic simulation of Webster and Yang’s broad scale monsoon circulation index. The JMA hindcast climatology during JJAS simulates the rainfall maxima over the west-coast of India and the head Bay of Bengal reasonably well, although, the latter is slightly shifted southwestward. Associated with better forecast skills of El Nino in the JMA model, the interannual variability of All India Summer Monsoon Rainfall (AISMR) during the 32-year period has also been very well simulated with a high significant (99% level) correlation in April ensemble followed by that of March and May ensembles. Thus, the present analysis indicates that the JMA seasonal forecast model can prove to be a useful tool for the dynamical seasonal forecast of AISMR. |
| Databáze: | OpenAIRE |
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