Analyzing the Accuracy of Medium Range Weather Forecasts: A Case Study from Vellayani, Kerala, India

Improving forecast accuracy for agriculture is crucial due to its reliance on timely and reliable weather information for optimizing farming practices. Timely weather forecasts help farmers optimize resource use by enabling them to make informed decisions regarding irrigation, sowing, harvesting, and pest management, reducing the risks associated with unexpected weather changes. This study evaluates the accuracy of Medium-Range Weather Forecasts (MRWF) issued by the India Meteorological Department (IMD) for rainfall and temperature in Vellayani, Thiruvananthapuram, during 2022 and 2023 across four seasons. Various verification metrics, including Ratio Score, RMSE, Probability of Detection (POD), False Alarm Rate (FAR), Critical Success Index (CSI), and Bias Score (BAIS), were employed to assess forecast performance. Ratio Score and RMSE provided an overall accuracy assessment, with RMSE highlighting significant deviations. Event-based metrics such as POD, FAR, and CSI evaluated the accuracy of event-specific forecasts, while Bias quantified tendencies to over-predict or under-predict. Together, these metrics offered a comprehensive evaluation of forecast reliability. The results showed mixed accuracy. Maximum temperature forecasts improved slightly, from 49.04% accuracy in 2022 to 52.6% in 2023. But, overall accuracy for maximum and minimum temperature predictions declined, particularly during summer and winter, with a rise in non-usable forecasts for minimum temperatures. Rainfall forecasts revealed a decline in the summer ratio score (48.91 to 33.7) but improved during the southwest monsoon (54.1 to 59.2). Annual ratio scores remained stable, increasing marginally from 53.4 to 53.7. CSI and POD values indicated consistent monsoon performance but highlighted challenges during summer and the northeast monsoon. FAR improved in winter but increased in summer. These findings highlight significant challenges in forecasting during critical agricultural periods, underscoring the need to enhance predictive capabilities to support sustainable farming and resilience to climate variability in the region. This analysis shows the importance of enhancing weather forecast precision during critical agricultural seasons. This will help to mitigate risks associated with climate variability. Improved predictive capabilities can address the increasing challenges posed by climate change, support sustainable farming practices and bolster the resilience of agricultural systems in the region.