Modification of a linear regression-based multi-model super-ensemble technique and its application in forecasting of wave height during extreme weather conditions

Abstract

ABSTRACT In this study, we focus on the improvement of wave forecast of the Indian coastal region using a multi-model ensemble technique. Generally, a number of wave forecast are available for the same region from different wave models. The main objective of this study is to merge the wave forecasts available at Indian National Centre for Ocean Information Services from different wave models to obtain an improved wave forecast using a multi-model super-ensemble method [Krishnamurti et al. 1999. Improved weather and seasonal climate forecasts from multi-model super-ensemble. Science. 285:1548–1550] during extreme weather conditions and to modify Krishnamurthy’s techniques and validate with observations for a better prediction. Here, Multi-grid WAVEWATCH III, Simulating WAves Nearshore and MIKE 21 Spectral Waves are used for the generation of wave forecast. We propose a modification of Krishnamurthy’s linear regression-based ensemble model. By using both of these ensemble techniques, we perform a multi-model ensemble forecasting of significant wave height up to 24-h lead time in the Indian Ocean for three different cyclones (Nilofar, Hudhud and Phailin) and during the southwest monsoon. A comparison of ensemble predictions and individual model predictions with the actual observations showed generally satisfactory performance of the chosen tools. At the time of severe cyclones such as Hudhud and Phailin, our modified technique shows significantly better prediction than the linear regression-based ensemble technique.