| contributor author | Rao, Suryachandra A.;Goswami, B. N.;Sahai, A. K.;Rajagopal, E. N.;Mukhopadhyay, P.;Rajeevan, M.;Nayak, S.;Rathore, L. S.;Shenoi, S. S. C.;Ramesh, K. J.;Nanjundiah, R. S.;Ravichandran, M.;Mitra, A. K.;Pai, D. S.;Bhowmik, S. K. R.;Hazra, A.;Mahapatra, S.;Saha, S. K.;Chaudhari, H. S.;Joseph, S.;Sreenivas, P.;Pokhrel, S.;Pillai, P. A.;Chattopadhyay, R.;Deshpande, M.;Krishna, R. P. M.;Das, Renu S.;Prasad, V. S.;Abhilash, S.;Panickal, S.;Krishnan, R.;Kumar, S.;Ramu, D. A.;Reddy, S. S.;Arora, A.;Goswami, T.;Rai, A.;Srivastava, A.;Pradhan, M.;Tirkey, S.;Ganai, M.;Mandal, R.;Dey, A.;Sarkar, S.;Malviya, S.;Dhakate, A.;Salunke, K.;Maini, Parvinder | |
| description abstract | In spite of the summer monsoon’s importance in determining the life and economy of an agriculture-dependent country like India, committed efforts toward improving its prediction and simulation have been limited. Hence, a focused mission mode program Monsoon Mission (MM) was founded in 2012 to spur progress in this direction. This article explains the efforts made by the Earth System Science Organization (ESSO), Ministry of Earth Sciences (MoES), Government of India, in implementing MM to develop a dynamical prediction framework to improve monsoon prediction. Climate Forecast System, version 2 (CFSv2), and the Met Office Unified Model (UM) were chosen as the base models. The efforts in this program have resulted in 1) unparalleled skill of 0.63 for seasonal prediction of the Indian monsoon (for the period 1981–2010) in a high-resolution (∼38 km) seasonal prediction system, relative to present-generation seasonal prediction models; 2) extended-range predictions by a CFS-based grand multimodel ensemble (MME) prediction system; and 3) a gain of 2-day lead time from very high-resolution (12.5 km) Global Forecast System (GFS)-based short-range predictions up to 10 days. These prediction skills are on par with other global leading weather and climate centers, and are better in some areas. Several developmental activities like coupled data assimilation, changes in convective parameterization, cloud microphysics schemes, and parameterization of land surface processes (including snow and sea ice) led to the improvements such as reducing the strong model biases in the Indian summer monsoon simulation and elsewhere in the tropics. | |
