MODIS brightness temperature data assimilation under cloudy conditions Ⅱ: Impacts on rainstorm forecasting

DING Wei-yu; WAN Qi-lin; HUANG Yan-yan; CHEN Zi-tong; ZHANG Cheng-zhong

Abstract:

Although satellite observations provide large amount of information of clouds and precipitation and play an important role in the forecast of heavy rainfall, they have not been fully taken advantage of in data assimilation of numerical weather predictions, especially those in infrared channels. Assimilating radiances is common under clear-sky conditions since it is extremely difficult to simulate infrared transmittance in cloudy sky. Based on the Global and Regional Assimilation and Prediction Enhanced System 3-dimensional variance (GRAPES-3DVar), cloud liquid water content, ice-water content and cloud cover are employed as governing variables in the assimilation system. This scheme can improve the simulation of infrared transmittance by a fast radiative transfer model for TOVS (RTTOV) and adjust the atmospheric and cloud parameters based on infrared radiance observations. In this paper, we investigate a heavy rainfall over Guangdong province on May 26, 2007, which is right after the onset of a South China Sea monsoon. In this case, channels of the Moderate Resolution Imaging Spectroradiometer (MODIS) for observing water vapor (Channel 27) and cloud top altitude (Channel 36) are selected for the assimilation. The process of heavy rainfall is simulated by the Weather Research and Forecasting (WRF) model. Our results show that the assimilated MODIS data can improve the distribution of water vapor and temperature in the first guess field and indirectly adjust the upper-level wind field. The tendency of adjustment agrees well with the satellite observations. The assimilation scheme has positive impacts on the short-range forecasting of rainstorm.

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DING Wei-yu, WAN Qi-lin, HUANG Yan-yan, et al. MODIS brightness temperature data assimilation under cloudy conditions Ⅱ: Impacts on rainstorm forecasting [J]. Journal of Tropical Meteorology, 2011, 17(3): 221-230.

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MODIS brightness temperature data assimilation under cloudy conditions Ⅱ: Impacts on rainstorm forecasting

Guangzhou Institute of Tropical and Marine Meteorology, CMA

Abstract: Although satellite observations provide large amount of information of clouds and precipitation and play an important role in the forecast of heavy rainfall, they have not been fully taken advantage of in data assimilation of numerical weather predictions, especially those in infrared channels. Assimilating radiances is common under clear-sky conditions since it is extremely difficult to simulate infrared transmittance in cloudy sky. Based on the Global and Regional Assimilation and Prediction Enhanced System 3-dimensional variance (GRAPES-3DVar), cloud liquid water content, ice-water content and cloud cover are employed as governing variables in the assimilation system. This scheme can improve the simulation of infrared transmittance by a fast radiative transfer model for TOVS (RTTOV) and adjust the atmospheric and cloud parameters based on infrared radiance observations. In this paper, we investigate a heavy rainfall over Guangdong province on May 26, 2007, which is right after the onset of a South China Sea monsoon. In this case, channels of the Moderate Resolution Imaging Spectroradiometer (MODIS) for observing water vapor (Channel 27) and cloud top altitude (Channel 36) are selected for the assimilation. The process of heavy rainfall is simulated by the Weather Research and Forecasting (WRF) model. Our results show that the assimilated MODIS data can improve the distribution of water vapor and temperature in the first guess field and indirectly adjust the upper-level wind field. The tendency of adjustment agrees well with the satellite observations. The assimilation scheme has positive impacts on the short-range forecasting of rainstorm.

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MODIS brightness temperature data assimilation under cloudy conditions Ⅱ: Impacts on rainstorm forecasting

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