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Weather forecasting is an important subject in the field of meteorology all over the world. The pattern and amount of rainfall are the essential factors that affect agricultural systems. The present paper describes an empirical study for modeling and forecasting the time series of monthly rainfall patterns for Coimbatore, Tamil Nadu. The Box-Jenkins Seasonal Autoregressive Integrated Moving Average (SARIMA) methodology has been adopted for model identification, diagnostic checking and forecasting for this region. The best SARIMA models were selected based on the Autocorrelation Function (ACF) and Partial Autocorrelation Function (PACF) and the minimum values of Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC). The study has shown that the SARIMA (0,0,0)(2,0,0)12 model was appropriate for analysing and forecasting the future rainfall patterns. The Root Means Square Error (RMSE) values were found to be 52.37 and proved that the above model was the best model for further forecasting the rainfall.

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Timely and accurate medium range weather information is critical to conquer the impact of highly dynamic next few days’ weather on the farming. Advances in weather forecast models, as well as their increased resolution, have resulted in more accurate and realistic forecasts. An attempt was made during 2019 – 2021 at Agro Climate Research Centre, Tamil Nadu Agricultural University, Coimbatore to develop cluster of village level (@ 3km resolution) Medium Range Weather Forecast (MRWF) for Tamil Nadu with higher accuracy. In this study, Weather Research and Forecast Model (WRF v4.2.1) with four microphysics viz., Kessler, WSM3, WSM5, WSM6 schemes were tested for Tamil Nadu during CWP, HWP, SWM and NEM 2020. The MRWF generated from the WRF model v4.2.1 with WSM3 had better BSF, higher Forecast Accuracy Index (FAI) and Forecast Usability Percent (FUP) for Tamil Nadu followed by Kessler scheme. The WSM5 and WSM6 were poor performer during the study. In general, CWP had higher FAI followed by HWP, NEM & SWM. The FAI from WSM3 was 0.65 - 0.74 during NEM and 0.55 - 0.69 during SWM. Among the season, the MRWF generated during SWM were over forecasted the rainfall quantity, where the NEM and HWP had better rainfall forecast nearing actuals. The FUP was higher in NEM followed by CWP, SWM & HWP, which was 57 – 88 per cent during NEM and 46 – 82 per cent during SWM. A decreasing trend in the quantitative FUP was observed with increase in lead times, irrespective of the microphysics and seasons. Finally, the study concluded that the accuracy of village level medium range rainfall forecasts from WRF model v4.2.1 varied temporally by season and the WSM3 microphysics option having superiority in all seasons.