Heavy rainfall prediction applying satellite‐based cloud data assimilation over land

To optimize flood management, it is crucial to determine whether rain will fall within a river basin. This requires very fine precision in prediction of rainfall areas. Cloud data assimilation has great potential to improve the prediction of precipitation area because it can directly obtain informat...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:Journal of geophysical research. Atmospheres 2016-08, Vol.121 (16), p.9737-9755
Hauptverfasser: Seto, Rie, Koike, Toshio, Rasmy, Mohamed
Format: Artikel
Sprache:eng
Schlagworte:
Online-Zugang:Volltext
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
Beschreibung
Zusammenfassung:To optimize flood management, it is crucial to determine whether rain will fall within a river basin. This requires very fine precision in prediction of rainfall areas. Cloud data assimilation has great potential to improve the prediction of precipitation area because it can directly obtain information on locations of rain systems. Clouds can be observed globally by satellite‐based microwave remote sensing. Microwave observation also includes information of latent heat and water vapor associated with cloud amount, which enables the assimilation of not only cloud itself but also the cloud‐affected atmosphere. However, it is difficult to observe clouds over land using satellite microwave remote sensing, because their emissivity is much lower than that of the land surface. To overcome this challenge, we need appropriate representation of heterogeneous land emissivity. We developed a coupled atmosphere and land data assimilation system with the Weather Research and Forecasting Model (CALDAS‐WRF), which can assimilate soil moisture, vertically integrated cloud water content over land, and heat and moisture within clouds simultaneously. We applied this system to heavy rain events in Japan. Results show that the system effectively assimilated cloud signals and produced very accurate cloud and precipitation distributions. The system also accurately formed a consistent atmospheric field around the cloud. Precipitation intensity was also substantially improved by appropriately representing the local atmospheric field. Furthermore, combination of the method and operationally analyzed dynamical and moisture fields improved prediction of precipitation duration. The results demonstrate the method's promise in dramatically improving predictions of heavy rain and consequent flooding. Key Points Cloud and cloud‐affected atmosphere assimilation using satellite microwave radiance is proposed for heavy rain prediction Coupled atmosphere and land data assimilation system enables satellite‐based cloud assimilation over land Microwave radiance alone improves heavy rain representation, and there is further improvement by operationally analyzed fields
ISSN:2169-897X
2169-8996
DOI:10.1002/2016JD025291