[M-GI26] Data-driven approaches for weather and hydrological predictions

[MGI26-08] 台風予測改善のための衛星観測を用いた大気モデルのパラメータ推定

*廣瀬 郁希¹、冨澤 風翔¹、Le Duc^1,2、澤田 洋平^1,2 (1.東京大学大学院、2.気象庁気象研究所)

キーワード：台風、パラメータ推定、衛星観測

One of the major uncertainties in atmospheric models is the parametric uncertainty. It is important to infer appropriate parameters in various parameterizations from observation. Despite previous efforts on the calibration of parameters in atmospheric models, there is no existing work that calibrates parameters based on satellite data, which is the most important source of observation for tropical cyclones. In this study, we estimate the posterior distribution of parameters using brightness temperature observation from a geostationary satellite. By adopting the Structural Similarity Index (SSIM), an image similarity metric, as the evaluation metric, two parameters from cloud microphysics scheme and one from the boundary layer scheme demonstrated high sensitivity and were successfully estimated. The estimated posterior distribution of parameters not only improves the accuracy of the prediction of satellite images but also partly reduces errors in the prediction of tropical cyclone intensity. We demonstrate the potential of adjusting multiple parameters based on satellite data and the implications of model development to improve the accuracy of tropical cyclone simulations.