arXiv subjects

Kenta Shiraishi

Publications and source records attributed to Kenta Shiraishi.

4 recordsLinked to original sources

arXiv2026

A Real-Time Remote-Sensing-Guided Decision-Support Framework for Cloud-Seeding Operations: A Field Demonstration Using Himawari-9 and C-band Phased Array Weather Radar

This study proposes a real-time remote-sensing-guided decision-support framework for cloud-seeding operations using high frequency geostationary satellite and ground weather radar observations. The framework integrates cloud assessment, human-in-the-loop decision support, and aircraft operation to translate high-frequency remote-sensing information into actionable guidance for seeding aircraft. We demonstrate the framework using 2.5-min Himawari-9 geostationary satellite observations and 60-s C-band phased-array weather radar (C-PAWR) observations during the preliminary dry-ice cloud-seeding field campaign conducted over Toyama Bay, Japan, in January 2026. In the 13 January case, the framework enabled the ground team to identify a developing cumulus cloud with a lifetime of approximately 20 min, communicate guidance to the aircraft, and conduct seeding immediately before the cloud began to dissipate naturally. Candidate seedable clouds were identified from Himawari-9 infrared indices, and their selection was supported by near-real-time C-PAWR observations of precipitation echoes. Because the released dry-ice amount was limited to 30 kg, this study does not attempt to attribute subsequent cloud evolution to seeding effects. Instead, the results demonstrate that rapid-scan satellite and ground radar observations can support real-time target selection and aircraft guidance for responsible, operationally feasible weather-intervention field experiments.

physics.ao-ph↗

arXiv2025

Bridging Artificial Intelligence and Data Assimilation: The Data-driven Ensemble Forecasting System ClimaX-LETKF

While machine learning-based weather prediction (MLWP) has achieved significant advancements, research on assimilating real observations or ensemble forecasts within MLWP models remains limited. We introduce ClimaX-LETKF, the first purely data-driven ML-based ensemble weather forecasting system. It operates stably over multiple years, independently of numerical weather prediction (NWP) models, by assimilating the NCEP ADP Global Upper Air and Surface Weather Observations. The system demonstrates greater stability and accuracy with relaxation to prior perturbation (RTPP) than with relaxation to prior spread (RTPS), while NWP models tend to be more stable with RTPS. RTPP replaces an analysis perturbation with a weighted blend of analysis and background perturbations, whereas RTPS simply rescales the analysis perturbation. Our experiments reveal that MLWP models are less capable of restoring the atmospheric field to its attractor than NWP models. This work provides valuable insights for enhancing MLWP ensemble forecasting systems and represents a substantial step toward their practical applications.

cs.LG↗

arXiv2025

Wasserstein GAN-Based Precipitation Downscaling with Optimal Transport for Enhancing Perceptual Realism

High-resolution (HR) precipitation prediction is essential for reducing damage from stationary and localized heavy rainfall; however, HR precipitation forecasts using process-driven numerical weather prediction models remains challenging. This study proposes using Wasserstein Generative Adversarial Network (WGAN) to perform precipitation downscaling with an optimal transport cost. In contrast to a conventional neural network trained with mean squared error, the WGAN generated visually realistic precipitation fields with fine-scale structures even though the WGAN exhibited slightly lower performance on conventional evaluation metrics. The learned critic of WGAN correlated well with human perceptual realism. Case-based analysis revealed that large discrepancies in critic scores can help identify both unrealistic WGAN outputs and potential artifacts in the reference data. These findings suggest that the WGAN framework not only improves perceptual realism in precipitation downscaling but also offers a new perspective for evaluating and quality-controlling precipitation datasets.

cs.LG↗

arXiv2024

Ensemble data assimilation to diagnose AI-based weather prediction model: A case with ClimaX version 0.3.1

Artificial intelligence (AI)-based weather prediction research is growing rapidly and has shown to be competitive with the advanced dynamic numerical weather prediction models. However, research combining AI-based weather prediction models with data assimilation remains limited partially because long-term sequential data assimilation cycles are required to evaluate data assimilation systems. This study proposes using ensemble data assimilation for diagnosing AI-based weather prediction models, and marked the first successful implementation of ensemble Kalman filter with AI-based weather prediction models. Our experiments with an AI-based model ClimaX demonstrated that the ensemble data assimilation cycled stably for the AI-based weather prediction model using covariance inflation and localization techniques within the ensemble Kalman filter. While ClimaX showed some limitations in capturing flow-dependent error covariance compared to dynamical models, the AI-based ensemble forecasts provided reasonable and beneficial error covariance in sparsely observed regions. In addition, ensemble data assimilation revealed that error growth based on ensemble ClimaX predictions was weaker than that of dynamical NWP models, leading to higher inflation factors. A series of experiments demonstrated that ensemble data assimilation can be used to diagnose properties of AI weather prediction models such as physical consistency and accurate error growth representation.

cs.LG↗