Search Results

You are looking at 1 - 3 of 3 items for

  • Author or Editor: Parvinder Maini x
  • Refine by Access: All Content x
Clear All Modify Search
Ashok Kumar, Parvinder Maini, and S. V. Singh

Abstract

An operational system for forecasting probability of precipitation (PoP) and yes/no forecast over 10 stations during monsoon season is developed. A perfect prog method (PPM) approach is followed for statistical interpretation of numerical weather prediction products. PPM model equations are developed by using analysis data obtained from the European Centre for Medium-Range Weather Forecasts for a period of 6 yr (1985–90). PoP forecasts are obtained from these equations by using global T-80 model output, which was installed at the National Centre for Medium Range Weather Forecasting in 1993. Results of verification study conducted during the monsoon season of 1995 covering various aspects of forecast skill and quality are also described.

Full access
Parvinder Maini, Ashok Kumar, L. S. Rathore, and S. V. Singh

Abstract

The inability of a general circulation model (GCM) to predict the surface weather parameters accurately necessitates statistical interpretation of numerical weather prediction (NWP) model output. Here a system for forecasting maximum and minimum temperatures has been developed and implemented for 12 locations in India based on the perfect prog method (PPM) approach. The analyzed data from the ECMWF for a period of 6 yr (1985–90) are used to develop PPM model equations. Daily forecasts for maximum and minimum temperatures are then obtained from these equations by using T-80 model output. In order to assess the skill and quality of the temperature forecasts, an attempt has been made to verify them by employing the conditional and marginal distribution of forecasts and observations using the data of four monsoon seasons from 1997 through 2000.

Full access
Suryachandra A. Rao, B. N. Goswami, A. K. Sahai, E. N. Rajagopal, P. Mukhopadhyay, M. Rajeevan, S. Nayak, L. S. Rathore, S. S. C. Shenoi, K. J. Ramesh, R. S. Nanjundiah, M. Ravichandran, A. K. Mitra, D. S. Pai, S. K. R. Bhowmik, A. Hazra, S. Mahapatra, S. K. Saha, H. S. Chaudhari, S. Joseph, P. Sreenivas, S. Pokhrel, P. A. Pillai, R. Chattopadhyay, M. Deshpande, R. P. M. Krishna, Renu S. Das, V. S. Prasad, S. Abhilash, S. Panickal, R. Krishnan, S. Kumar, D. A. Ramu, S. S. Reddy, A. Arora, T. Goswami, A. Rai, A. Srivastava, M. Pradhan, S. Tirkey, M. Ganai, R. Mandal, A. Dey, S. Sarkar, S. Malviya, A. Dhakate, K. Salunke, and Parvinder Maini

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.

Free access