Abstract
Clouds affect the assimilation of microwave data from satellites and therefore the detection of clouds is important under both clear sky and cloudy conditions. We introduce a new cloud detection method based on the assimilation of data from the advanced microwave sounder unit A (AMSU-A) and the microwave humidity sounder (MHS) into the global and regional assimilation and prediction system (GRAPES) and use forecast experiments to evaluate its impact. The new cloud detection method can retain more observational data than the current method in GRAPES, thereby improving the assimilation of AMSU-A data. Verification of the method showed that, by improving the forecast of the lower-level air temperature and geopotential height, the new cloud detection method improved the forecast of the track of two typhoons. The forecast of a large-scale weather system in GRAPES was also improved by the new method in the later period of the forecast.
Highlights
The application of satellite data in numerical weather forecasting entered a new era when it became possible to directly assimilate satellite radiance data into a variational data assimilation system [1,2]
If the advanced microwave sounder unit A (AMSU-A) and microwave humidity sounder (MHS) data are combined into one stream of data, information about the liquid water path (LWP) and ice water path (IWP) can be obtained at the same time
The AMSU-A and MHS have both been onboard the polar-orbiting NOAA-18 satellite since it was launched, but the data from the two instruments have always been assimilated separately
Summary
The application of satellite data in numerical weather forecasting entered a new era when it became possible to directly assimilate satellite radiance data into a variational data assimilation system [1,2]. A scattering index is used for detecting clouds in AMSU-A data in the global and regional assimilation and prediction system (GRAPES) These two cloud detection methods are empirical methods and lack a solid physical foundation, relying heavily on the background field. This study only evaluated the impact of the one-stream cloud detection method on the assimilation of MHS data and only operated on the regional model covering the USA, so the global applicability of this method needs further research. Based on the latest version of the GRAPES global model, we introduced the AMSU-A and MHS one-stream cloud detection method into the GRAPES assimilation system and evaluated the impact of the new cloud detection method on the assimilation of AMSU-A data with the aim of improving the forecasting ability of GRAPES.
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