AIRS impact on analysis and forecast of an extreme rainfall event (Indus River Valley, Pakistan, 2010) with a global data assimilation and forecast system

Abstract

A set of data assimilation and forecast experiments is performed with the NASA Global data assimilation and forecast system GEOS‐5, to compare the impact of different approaches toward assimilation of Atmospheric Infrared Sounder (AIRS) data. The impact is first assessed globally on a sample of more than forty forecasts per experiment, through the standard 500 hPa anomaly correlation metrics. Next, the focus is on precipitation analysis and precipitation forecast skill relative to one particular event: an extreme rainfall episode which occurred in late July 2010 in Pakistan, causing massive floods along the Indus River Valley. Results show that, in addition to improving the global forecast skill, the assimilation of quality‐controlled AIRS temperature retrievals obtained under partly cloudy conditions produce better precipitation analyses, and substantially better 7‐day forecasts, than assimilation of clear‐sky radiances. The improvement of precipitation forecast skill up to 7 days is very significant in the tropics, and is caused by an improved representation, attributed to cloudy retrieval assimilation, of two contributing mechanisms: the low‐level moisture advection, and the concentration of moisture over the area in the days preceding the precipitation peak.