MODIS BRIGHTNESS TEMPERATURE DATA ASSIMILATION UNDER CLOUDY CONDITIONS Ⅱ: IMPACTS ON RAINSTORM FORECASTING

Satellite observations provide large amount of information of clouds and precipitation and play an important role in the forecast of heavy rainfall.However,we have not fully taken advantage of satellite observations in the data assimilation of numerical weather predictions,especially those in infrar...

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Published inJournal of Tropical Meteorology Vol. 17; no. 3; pp. 221 - 230
Main Author 丁伟钰 万齐林 黄燕燕 陈子通 张诚忠
Format Journal Article
LanguageEnglish
Published Guangzhou Guangzhou Institute of Tropical & Marine Meteorology 01.09.2011
Guangzhou Institute of Tropical and Marine Meteorology,CMA,Guangzhou 510080 China
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ISSN1006-8775
DOI10.3969/j.issn.1006-8775.2011.03.004

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Abstract Satellite observations provide large amount of information of clouds and precipitation and play an important role in the forecast of heavy rainfall.However,we have not fully taken advantage of satellite observations in the data assimilation of numerical weather predictions,especially those in infrared channels. It is common to only assimilate radiances under clear-sky conditions since it is extremely difficult to simulate infrared transmittance in cloudy sky.On the basis of 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.
AbstractList Satellite observations provide large amount of information of clouds and precipitation and play an important role in the forecast of heavy rainfall.However,we have not fully taken advantage of satellite observations in the data assimilation of numerical weather predictions,especially those in infrared channels. It is common to only assimilate radiances under clear-sky conditions since it is extremely difficult to simulate infrared transmittance in cloudy sky.On the basis of 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.
P412.27; Satellite observations provide large amount of information of clouds and precipitation and play an important role in the forecast of heavy rainfall.However,we have not fully taken advantage of satellite observations in the data assimilation of numerical weather predictions,especially those in infrared channels.It is common to only assimilate radiances under clear-sky conditions since it is extremely difficult to simulate infrared transmittance in cloudy sky.On the basis of 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.
Author 丁伟钰 万齐林 黄燕燕 陈子通 张诚忠
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MODIS brightness temperature data
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Notes Satellite observations provide large amount of information of clouds and precipitation and play an important role in the forecast of heavy rainfall.However,we have not fully taken advantage of satellite observations in the data assimilation of numerical weather predictions,especially those in infrared channels. It is common to only assimilate radiances under clear-sky conditions since it is extremely difficult to simulate infrared transmittance in cloudy sky.On the basis of 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.
MODIS brightness temperature data; assimilation; rainstorm
DING Wei-yu,WAN Qi-lin,HUANG Yan-yan,CHEN Zi-tong,ZHANG Cheng-zhong(Guangzhou Institute of Tropical and Marine Meteorology,CMA,Guangzhou 510080 China)
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PublicationTitle Journal of Tropical Meteorology
PublicationTitleAlternate Journal of Tropical Meteorology
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P412.27; Satellite observations provide large amount of information of clouds and precipitation and play an important role in the forecast of heavy...
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SubjectTerms Data collection
MODIS数据
中分辨率成像光谱仪
亮度温度
卫星观测
数据同化
暴雨预报
液态水含量
阴天
Title MODIS BRIGHTNESS TEMPERATURE DATA ASSIMILATION UNDER CLOUDY CONDITIONS Ⅱ: IMPACTS ON RAINSTORM FORECASTING
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