6 papers
Exploring Ultra Rapid Data Assimilation Based on Ensemble Transform Kalman Filter with the Lorenz 96 Model
Fumitoshi Kawasaki, Atsushi Okazaki, Kenta Kurosawa +1
Ultra-rapid data assimilation (URDA) is a method that rapidly updates preemptive forecasts derived from observations without integrating a dynamical model each time additional obse…
Bridging Artificial Intelligence and Data Assimilation: The Data-driven Ensemble Forecasting System ClimaX-LETKF
Akira Takeshima, Kenta Shiraishi, Atsushi Okazaki +2
While machine learning-based weather prediction (MLWP) has achieved significant advancements, research on assimilating real observations or ensemble forecasts within MLWP models re…
Wasserstein GAN-Based Precipitation Downscaling with Optimal Transport for Enhancing Perceptual Realism
Kenta Shiraishi, Yuka Muto, Atsushi Okazaki +1
High-resolution (HR) precipitation prediction is essential for reducing damage from stationary and localized heavy rainfall; however, HR precipitation forecasts using process-drive…
Comparative Analysis of Black-Box Optimization Methods for Weather Intervention Design
Yuta Higuchi, Rikuto Nagai, Atsushi Okazaki +2
As climate change increases the threat of weather-related disasters, research on weather control is gaining importance. The objective of weather control is to mitigate disaster ris…
Convex Optimization of Initial Perturbations toward Quantitative Weather Control
Toshiyuki Ohtsuka, Atsushi Okazaki, Masaki Ogura +1
This study proposes introducing convex optimization to find initial perturbations of atmospheric states to realize specified changes in subsequent weather. In the proposed method,…
Ensemble data assimilation to diagnose AI-based weather prediction model: A case with ClimaX version 0.3.1
Shunji Kotsuki, Kenta Shiraishi, Atsushi Okazaki
Artificial intelligence (AI)-based weather prediction research is growing rapidly and has shown to be competitive with the advanced dynamic numerical weather prediction models. How…