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Ashok Kumar

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Ashok Kumar, Parvinder Maini, and S. V. Singh

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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.

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

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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.

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Rama Shankar Yadav, Suneet Dwivedi, and Ashok Kumar Mittal

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Despite the widespread use of the Lorenz model as a conceptual model for predictability studies in meteorology, only Evans et al. seem to have studied the prediction of occurrence of regime changes and their duration. In this paper, simpler rules are presented for forecasting regime changes and their lengths, with near-perfect forecasting accuracy. It is found that when |x(t)| is greater than a critical value xc, the current regime will end after it completes the current orbit. Moreover, the length n of the new regime increases monotonically with the maximum value xm of |x(t)| in the previous regime. A best-fit cubic expression provides a very good estimate of n for the next regime, given xm for the previous regime.

Similar forecasting rules are also obtained for regime changes in the forced Lorenz model. This model was introduced by Palmer and used as a conceptual model to explore the effects of sea surface temperature on seasonal mean rainfall. It was found that for the forced Lorenz model, the critical value xc changed linearly with the forcing parameter providing bias to one of the regimes. Similar regime prediction rules have been found in some other two-regime attractors. It seems these forecasting rules are a generic property of a large class of two-regime attractors. Although as a conceptual model, the Lorenz model cannot be taken very literally, these results suggest a relationship between magnitudes of maximum anomaly in one regime, for example, the active spell, and duration of the subsequent break spell.

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P. Swapna, M. K. Roxy, K. Aparna, K. Kulkarni, A. G. Prajeesh, K. Ashok, R. Krishnan, S. Moorthi, A. Kumar, and B. N. Goswami

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With the goal of building an Earth system model appropriate for detection, attribution, and projection of changes in the South Asian monsoon, a state-of-the-art seasonal prediction model, namely the Climate Forecast System version 2 (CFSv2) has been adapted to a climate model suitable for extended climate simulations at the Indian Institute of Tropical Meteorology (IITM), Pune, India. While the CFSv2 model has been skillful in predicting the Indian summer monsoon (ISM) on seasonal time scales, a century-long simulation with it shows biases in the ocean mixed layer, resulting in a 1.5°C cold bias in the global mean surface air temperature, a cold bias in the sea surface temperature (SST), and a cooler-than-observed troposphere. These biases limit the utility of CFSv2 to study climate change issues. To address biases, and to develop an Indian Earth System Model (IITM ESMv1), the ocean component in CFSv2 was replaced at IITM with an improved version, having better physics and interactive ocean biogeochemistry. A 100-yr simulation with the new coupled model (with biogeochemistry switched off) shows substantial improvements, particularly in global mean surface temperature, tropical SST, and mixed layer depth. The model demonstrates fidelity in capturing the dominant modes of climate variability such as the ENSO and Pacific decadal oscillation. The ENSO–ISM teleconnections and the seasonal leads and lags are also well simulated. The model, a successful result of Indo–U.S. collaboration, will contribute to the IPCC’s Sixth Assessment Report (AR6) simulations, a first for India.

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