日本地球惑星科学連合2018年大会

講演情報

[EE] ポスター発表

セッション記号 M (領域外・複数領域) » M-GI 地球科学一般・情報地球科学

[M-GI22] Data assimilation: A fundamental approach in geosciences

2018年5月20日(日) 13:45 〜 15:15 ポスター会場 (幕張メッセ国際展示場 7ホール)

コンビーナ:中野 慎也(情報・システム研究機構 統計数理研究所)、藤井 陽介(気象庁気象研究所)、宮崎 真一(京都大学理学研究科、共同)、三好 建正(理化学研究所計算科学研究機構)

[MGI22-P04] Applying data assimilation to precipitation nowcasting

*大塚 成徳1三好 建正1 (1.国立研究開発法人理化学研究所計算科学研究機構)

キーワード:降水ナウキャスト、LETKF

Data assimilation (DA) has been developed in numerical weather prediction (NWP) for decades. Various types of observations are combined with physically-based numerical simulations in a sophisticated way. In a similar manner, we applied DA to precipitation nowcasting with space-time extrapolation. Although the basic workflow is the same as NWP, there are two major differences. The model we use is a simple advection equation without complex physics, and the "observations" are motion vectors computed from consecutive two bitmap images by a cross-correlation method. These differences require additional developments of DA techniques for nowcasting such as covariance inflation in an ensemble Kalman filter. We have been running two nowcasting systems at different scales: 1) global precipitation nowcasting based on satellite observations for 12 hours, and 2) phased-array weather radar three-dimensional nowcasting for 10 minutes. The local ensemble transform Kalman filter (LETKF) is applied in the global precipitation nowcasting to achieve high accuracy, whereas a simple grid-by-grid time filtering is used in the phased-array radar nowcasting to achieve high stability and low computational cost. In this presentation, we will discuss differences in these DA methods for nowcasting at different scales.