@techreport{oai:ipsj.ixsq.nii.ac.jp:00237618,
 author = {Sameer, Deshmukh and Arata, Amemiya and Takumi, Honda and Elmor, Lang Ian and Sameer, Deshmukh and Arata, Amemiya and Takumi, Honda and Elmor, Lang Ian},
 issue = {24},
 month = {Aug},
 note = {Rainfall forecasting is an important application of scientiﬁc computing that has a far-reaching impact on humanity. Accurate and timely forecasts can make the difference between life and death for people affected by severe weather conditions. High quality forecasts depend mainly upon availability of data and computational resources. The MP-PAWR at Saitama University generates high quality distributions of 3D radar reﬂectivity at 30 second intervals. The data from this radar has been used to generate rainfall density forecasts using the SCALE-LETKF algorithm. The SCALE-LETKF algorithm is very inefﬁcient on currently available architectures, utilizing only about 5% of the available computational capacity of Fugaku. Improvement in the computational efﬁciency of SCALE-LETKF can hold tremendous value for the accuracy, speed and energy efﬁciency of rainfall forecasts. LETKF from SCALE-LETKF occupies a majority of the computational resources. LETKF is designed for processing large amounts of data, and has been written over several years, comprising more than 20,000 lines of FORTRAN code. We propose a mini-application that imitates the most important performance and numerical characteristics of LETKF. We then propose an alternative hardware accelerator that can potentially achieve higher computational efﬁciency than currently available hardware., Rainfall forecasting is an important application of scientiﬁc computing that has a far-reaching impact on humanity. Accurate and timely forecasts can make the difference between life and death for people affected by severe weather conditions. High quality forecasts depend mainly upon availability of data and computational resources. The MP-PAWR at Saitama University generates high quality distributions of 3D radar reﬂectivity at 30 second intervals. The data from this radar has been used to generate rainfall density forecasts using the SCALE-LETKF algorithm. The SCALE-LETKF algorithm is very inefﬁcient on currently available architectures, utilizing only about 5% of the available computational capacity of Fugaku. Improvement in the computational efﬁciency of SCALE-LETKF can hold tremendous value for the accuracy, speed and energy efﬁciency of rainfall forecasts. LETKF from SCALE-LETKF occupies a majority of the computational resources. LETKF is designed for processing large amounts of data, and has been written over several years, comprising more than 20,000 lines of FORTRAN code. We propose a mini-application that imitates the most important performance and numerical characteristics of LETKF. We then propose an alternative hardware accelerator that can potentially achieve higher computational efﬁciency than currently available hardware.},
 title = {Acceleration of Data Assimilation for Rainfall Prediction with Co-design of Hardware and Software},
 year = {2024}
}