Response of a Global Spectral Model for Simulation of Indian Summer Monsoon Rainfall

2020 ◽  
Vol 6 (2) ◽  
pp. 33-46
Author(s):  
Srabanti Ballav ◽  
Sandipan Mukherjee ◽  
Ashok P. Dimri

The present work highlights response of a global spectral model T80L18 with respect to Indian summer monsoon rainfall (ISMR) during 8 years period of 1996-2003. The model performance is evaluated for day-1, day-3 and day-4 retrospective 24-hour accumulated rainfall forecasts from 0300 UTC to the next day 0300 UTC using in-situ rainfall observations of 4491 stations. The model performance is evaluated by assessing: (i) percentage departure and root mean square error (RMSE) of seasonal rainfall forecast, (ii) coefficient of variation (CoV) of seasonal rainfall forecast and observation, along with percentage departure of monthly rainfall forecast and (iii) model performance during a drought and a normal year of 2002 and 2003, respectively. Generally, it is noted that the T80L18 model underestimated high rainfall and overestimated low rainfall, however, with increasing forecast duration prediction over low rainfall areas improved. The model RMSE over central and western India is found to increase with increasing forecast duration; however, the same was found to decrease over Jammu and Kashmir. The CoV of day-1 rainfall forecast is found to be low over all India in comparison to the observed data. In the case of model performance evaluation during a drought and a normal year of 2002 and 2003, it is noted that the model produced higher rainfall over the rainfall deficit regions of observed distribution; whereas the heaviest observed rainfall region (>250 cm) is not well resolved by the model. In general, the T80L18 model performance is noted to be better over central India for mean seasonal rainfall prediction.

2021 ◽  
Author(s):  
Stella Jes Varghese ◽  
Kavirajan Rajendran ◽  
Sajani Surendran ◽  
Arindam Chakraborty

<p>Indian summer monsoon seasonal reforecasts by CFSv2, initiated from January (4-month lead time, L4) through May (0-month lead time, L0) initial conditions (ICs), are analysed to investigate causes for the highest Indian summer monsoon rainfall (ISMR) forecast skill of CFSv2 with February (3-month lead time, L3) ICs. Although theory suggests forecast skill should degrade with increase in lead-time, CFSv2 shows highest skill with L3, due to its forecasting of ISMR excess of 1983 which other ICs failed to forecast. In contrast to observation, in CFSv2, ISMR extremes are largely decided by sea surface temperature (SST) variation over central Pacific (NINO3.4) associated with El Niño-Southern Oscillation (ENSO), where ISMR excess (deficit) is associated with La Niña (El Niño) or cooling (warming) over NINO3.4. In 1983, CFSv2 with L3 ICs forecasted strong La Niña during summer, which resulted in 1983 ISMR excess. In contrast, in observation, near normal SSTs prevailed over NINO3.4 and ISMR excess was due to variation of convection over equatorial Indian Ocean, which CFSv2 fails to capture with all ICs. CFSv2 reforecasts with late-April/early-May ICs are found to have highest deterministic ISMR forecast skill, if 1983 is excluded and Indian monsoon seasonal biases are also reduced. During the transitional ENSO in Boreal summer of 1983, faster and intense cooling of NINO3.4 SSTs in L3, could be due to larger dynamical drift with longer lead time of forecasting, compared to L0. Boreal summer ENSO forecast skill is also found to be lowest for L3 which gradually decreases from June to September. Rainfall occurrence with strong cold bias over NINO3.4, is because of the existence of stronger ocean-atmosphere coupling in CFSv2, but with a shift of the SST-rainfall relationship pattern to slightly colder SSTs than the observed. Our analysis suggests the need for a systematic approach to minimize bias in SST boundary forcing in CFSv2, to achieve improved ISMR forecasts.</p>


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