Preprints
https://doi.org/10.5194/gmd-2024-89
https://doi.org/10.5194/gmd-2024-89
Submitted as: model evaluation paper
 | 
07 Aug 2024
Submitted as: model evaluation paper |  | 07 Aug 2024
Status: this preprint is currently under review for the journal GMD.

IITM High-Resolution Global Forecast Model Version 1: An attempt to resolve monsoon prediction deadlock

R. Phani Murali Krishna, Siddharth Kumar, Athippatta Gopinathan Prajeesh, Peter Bechtold, Nils Wedi, Kumar Roy, Malay Ganai, B. Revanth Reddy, Snehlata Tirkey, Tanmoy Goswami, Radhika Kanase, and Parthasarathi Mukhopadhyay

Abstract. The prediction of Indian monsoon rainfall variability affecting a country with a population of billions remained one of the major challenges of the numerical weather prediction community. While in recent years, there has been a significant improvement in predicting the synoptic scale transients associated with the monsoon circulation, the intricacies of rainfall variability remained a challenge. Here, an attempt is made to develop a global model using a dynamic core of a cubic octahedral grid that provides a higher resolution of 6.5 km over the global tropics. This high-resolution model has been developed to resolve the monsoon convection. Reforecasts with the IITM High-resolution Global Forecast Model (HGFM) have been run daily from June through September 2022. The HGFM model has a wave number truncation of 1534 in the cubic octahedral grid. The monsoon events have been predicted with a ten-day lead time. The HGFM model is compared to the operational GFS T1534. While the HGFM provides skills comparable to the GFS, it shows better skills for higher precipitation thresholds. This model is currently being run in experimental mode and will be made operational.

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R. Phani Murali Krishna, Siddharth Kumar, Athippatta Gopinathan Prajeesh, Peter Bechtold, Nils Wedi, Kumar Roy, Malay Ganai, B. Revanth Reddy, Snehlata Tirkey, Tanmoy Goswami, Radhika Kanase, and Parthasarathi Mukhopadhyay

Status: open (until 02 Oct 2024)

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R. Phani Murali Krishna, Siddharth Kumar, Athippatta Gopinathan Prajeesh, Peter Bechtold, Nils Wedi, Kumar Roy, Malay Ganai, B. Revanth Reddy, Snehlata Tirkey, Tanmoy Goswami, Radhika Kanase, and Parthasarathi Mukhopadhyay
R. Phani Murali Krishna, Siddharth Kumar, Athippatta Gopinathan Prajeesh, Peter Bechtold, Nils Wedi, Kumar Roy, Malay Ganai, B. Revanth Reddy, Snehlata Tirkey, Tanmoy Goswami, Radhika Kanase, and Parthasarathi Mukhopadhyay

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Short summary
The newly developed HGFM is an advanced iteration of the operational GFS model. The HGFM can produce forecasts at a spatial scale (~6 km in tropics). It demonstrates improved accuracy in short to medium-range weather prediction over Indian summer monsoon regions, as well as notable success in predicting extreme rainfall events. Following validation and testing, the model will be entrusted to operational forecasting agencies. Forecasts from this model could significantly affect billions of lives.