A review of the development and implementation of a tropical cyclone prediction system for North Indian Ocean in a multi-model ensemble framework
Keywords:North Indian Ocean cyclogenesis, Tropical cyclone prediction system, Cyclogenesis potential parameter, Objective tracking algorithm, Multi-model ensemble prediction system
The seasonal genesis parameters for tropical cyclogenesis developed by W. M. Gray, is widely used for the climatological and seasonal monitoring of cyclogenesis over the tropical oceans. Over the North Indian Ocean (NIO), cyclogenesis and evolution is monitored and predicted in the short, medium and extended ranges by India Meteorological Department with the implementation of different deterministic and probabilistic forecasting techniques. This paper provides a review of an in-house developed tropical cyclone prediction system involving an improved storm evolution index and an objective tracking algorithm for detecting cyclogenesis, evolution and storm tracks from post-processed Multi-model ensemble (MME) outputs from the Climate Forecast System-based Grand Ensemble Prediction System (CGEPS) implemented for operational extended range prediction.
In the first part, the reliability of cyclogenesis prediction when more than one storm systems develop simultaneously is discussed using a case study. Prominent cyclogenesis indices and constituent parameters are used to analyse the atmospheric and oceanic features which affected the evolution two consecutive storms over NIO by using ERA-Interim daily averaged datasets. The performance of indices from MME outputs is also analysed. Further the reliability of objective track prediction system is discussed by using ERA-5 and ERA-Interim datasets. Finally, the performance of the CGEPS-MME in predicting the recent tropical cyclones, Amphan and Nisarga are discussed in detail. Real-time implementation of this prediction system has proven to be critical in providing early guidance on the formation of storms, enabling the cyclone warning community to be on alert thereby providing enough lead time for better planning and mitigation strategies.
How to Cite
Copyright (c) 2021 MAUSAM
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
All articles published by MAUSAM are licensed under the Creative Commons Attribution 4.0 International License. This permits anyone.
Anyone is free:
- To Share - to copy, distribute and transmit the work
- To Remix - to adapt the work.
Under the following conditions:
- Share - copy and redistribute the material in any medium or format
- Adapt - remix, transform, and build upon the material for any purpose, even