New Cloud Assimilation System Boosts Accuracy of Weather Forecast over Tibetan Plateau
Jan 19, 2024
Cloud optical thickness (COT), a crucial parameter influencing cloud-solar radiation interactions, is directly incorporated into the cloud assimilation system. This integration significantly improves the precision of solar shortwave radiation (SSR) predictions and plays a crucial role for improving the precision of weather forecasts and understanding the broader climate system.
Researchers from the Aerospace Information Research Institute (AIR) under the Chinese Academy of Sciences (CAS) have unveiled a new cloud assimilation system aimed at minimizing uncertainty in cloud estimation and enhancing the accuracy of SSR forecasts over the Tibetan Plateau. This approach marks the first application of the four-dimensional local ensemble transform Kalman filter method to directly assimilate COT.
The study was published in the Geoscience Letters on Jan.4, 2024.
The system leverages high-resolution spatial and temporal data from the next-generation geostationary satellite, Himawari-8, ensuring precise estimation of clouds and improved radiation forecasting. Independent verification post-assimilation indicates significant enhancements in both COT and SSR. The correlation coefficient (CORR) for SSR increased by 11.3%, while the root-mean-square error (RMSE) and mean bias error (MBE) decreased by 28.5% and 58.9%, respectively.
Results from the 2-hour cycle assimilation forecasts demonstrate a noteworthy reduction in SSR overestimation, showcasing the effectiveness of the assimilation system. These findings underscore the high potential of this assimilation technique in numerical weather prediction, specifically in forecasting SSR.
While the study acknowledges the progress made in enhancing model forecasts through cloud properties assimilation, it underscores the need for further research and exploration. This innovative approach marks a significant step towards more accurate weather predictions, particularly in challenging geographic regions like the Tibetan Plateau.