The implementation of a bias‐correction and signal amplification technique to the National Center for Environmental Prediction's Climate Forecast System‐based Grand Ensemble Prediction System Multi‐Model Ensemble outputs is studied for improvements in track predictions of three cyclonic storms over North Indian Ocean. Bias‐correction method involves the removal of lead‐dependent climatological bias from multi‐model ensemble forecasts by using European Centre for Medium‐Range Weather Forecasts Re‐analysis (ERA‐Interim) daily‐averaged data sets as observations. The corrected data are then subjected to signal amplification procedure involving a two‐point space and time correction of ensembles based on the leading signal (ensemble mean), whereby large uncertainties and disagreements between different model outputs are reduced. Results show that bias‐correction and signal amplification technique is, indeed, improving the track forecasts of selected cyclonic storm cases with significant reduction in track errors even at longer lead times.

Flawless subseasonal prediction of tropical cyclogenesis and evolution over the narrow basin of North Indian Ocean (NIO) demands accurate rendition of the crucial parameters that influence the development of cyclonic storms. While many genesis potential indices are used for climatological monitoring and prediction of cyclogenesis globally, their skill in subseasonal prediction of individual storm development, especially near coastlines are limited. Thus, an improved genesis potential parameter (IGPP) is introduced in this study which can capture both cyclogenesis and daily evolution of cyclonic systems over NIO. The IGPP is a revised version of Kotal-Genesis Potential Parameter (KGPP) implemented by India Meteorological Department (IMD) for short-range operational cyclogenesis prediction over NIO. Daily averaged ERA-5 and ERA-Interim data sets are used for analysis and comparison of selected cyclonic storms over NIO for the period 1989-2018. Results reveal that false alarms and overestimation of values present in KGPP are remarkably reduced by using IGPP for all the analyzed storms. Moreover, IGPP outperforms KGPP in distinguishing between developing and nondeveloping storms by accurately representing the storm genesis, evolution, rapid intensification and intensity variations. Thus IGPP can be implemented operationally for improving the real-time prediction of cyclogenesis and storm evolution over NIO. Plain Language Summary Accurate representation of weather parameters that are crucial for the formation and development of tropical cyclones over a small basin like North Indian Ocean (NIO) is critical for biweekly storm predictions. Most studies utilize different cyclogenesis parameters in genesis potential indices to capture annual/interannual frequency of cyclone formations around the globe. While these parameters are successful in studying the average storm genesis and frequencies at longer time scales, forecasts in real-time over a landlocked and narrow basin such as NIO often fail to capture rapid storm development. This study introduces an improved parameter for capturing the genesis and daily evolution of individual storms thus modifying the presently used parameter by India Meteorological Department thus making it more suitable for NIO. Comparison of old and improved parameters using daily averaged reanalysis data sets reveal that new parameter is better than the older one with accurate representation of storm evolution and noticeable reduction in false storm signals present in the old parameter for all the storm cases analyzed here.

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