Satellite retrieval of atmospheric water vapor is intended to further understand the role played by the energy and water cycle to determine the Earth's weather and climate.The algorithm for operational retrieval o...Satellite retrieval of atmospheric water vapor is intended to further understand the role played by the energy and water cycle to determine the Earth's weather and climate.The algorithm for operational retrieval of total precipitable water (TPW) from the visible and infrared radiometer (VIRR) onboard Fengyun 3A (FY-3A) employs a split window technique for clear sky radiances over land and oceans during both day and night.The retrieved TPW is compared with that from the moderate resolution imaging spectroradiometer (MODIS) onboard the Terra satellite and data from radiosonde observations (RAOB).During the study period,comparisons show that the FY-3A TPW is in general agreement with the gradients and distributions from the Terra TPW.Their zonal mean difference over East Asia is smaller in the daytime than at night,and the main difference occurs in the complex terrain at mid latitude near 30°N.Compared with RAOB,the zonal FY-3A and the Terra TPW have a moist bias at low latitudes and a dry bias at mid and high latitudes;in addition,the FY-3A TPW performs slightly better in zonal mean biases and the diurnal cycle.The temporal variation of the FY-3A and the Terra TPW generally fits the RAOB TPW with the FY-3A more accurate at night while Terra TPW more accurate during the daytime.Comparisons of correlations,root mean square differences and standard deviations indicate that the FY-3A TPW series is more consistent with the RAOB TPW at selected stations.As a result,the FY-3A TPW has some advantages over East Asia in both spatial and temporal dimensions.展开更多
风云三号A星(FY-3A)是中国第一颗携带微波湿度垂直探测仪(MicroWave Humidity Sounder,MWHS)的气象卫星,可以为全球及区域数值天气预报提供湿度信息。本文结合中尺度数值预报模式(Weather Research and Forecasting Model,WRF),利用改...风云三号A星(FY-3A)是中国第一颗携带微波湿度垂直探测仪(MicroWave Humidity Sounder,MWHS)的气象卫星,可以为全球及区域数值天气预报提供湿度信息。本文结合中尺度数值预报模式(Weather Research and Forecasting Model,WRF),利用改进后的中尺度数值预报同化系统(Weather Research and Forecasting Model Data Assimilation System,WRFDA),研究FY-3A/MWHS资料同化方法,并与美国国家海洋和大气管理局微波湿度计资料(National Oceanic and Atmospheric Administration/Advanced Microwave Sounding Unit-B,NOAA/AMSU-B)进行同化对比,分析FY-3A/MWHS资料同化对湿度场分析和预报的影响。结果表明:MWHS资料同化可以有效改进湿度分析场,且对分析场的改进明显优于NOAA/AMSUB资料同化,MWHS资料同化对湿度场预报的调整总体优于NOAA/AMSU-B资料同化,MWHS资料同化试验18 h内的湿度场预报精度明显优于NOAA/AMSU-B同化试验。由试验结果可知,中国FY-3A气象卫星MWHS资料在区域尺度数值预报中具有较好的应用前景。展开更多
The Lightning Mapping Imager(LMI)equipped on the FY-4 A(Feng Yun-4 A)geostationary satellite achieves lightning positioning through optical imaging and has the advantages of high temporal resolution,high stability,and...The Lightning Mapping Imager(LMI)equipped on the FY-4 A(Feng Yun-4 A)geostationary satellite achieves lightning positioning through optical imaging and has the advantages of high temporal resolution,high stability,and continuous observation.In this study,FY-4 A LMI lightning event,group and flash data from April to August 2018 are selected,and their quality are assessed through qualitative and quantitative comparison with the ground-based Advanced Time of Arrival and Direction system(ADTD)lightning observation network data and the American International Space Station(ISS)lightning imaging sensor(LIS)data.The results show that the spatial distributions of FY-4 A lightning are consistent with those of the ground-based ADTD and ISS LIS.The temporal variation in FY-4 A lightning group frequency is consistent with that of ADTD stroke,which reflects that FY-4 A LMI can capture the lightning occurrence in inland China.Quantitative statistics show that the consistency rate of FY-4 A LMI and ISS LIS events is relatively high but their consistency rate is lower in terms of lightning group and flash data.Compared with the lightning observations by the ISS LIS and the ground-based ADTD,FY-4 A LMI reports fewer lightning events in the Tibetan Plateau.The application of Tibetan Plateau lightning data requires further processing and consideration.展开更多
Deviation exists between measured and simulated microwave radiometer sounding data. The bias results in low-accuracy atmospheric temperature and humidity profiles simulated by Back Propagation artificial neural networ...Deviation exists between measured and simulated microwave radiometer sounding data. The bias results in low-accuracy atmospheric temperature and humidity profiles simulated by Back Propagation artificial neural network models. This paper evaluated a retrieving atmospheric temperature and humidity profiles method by adopting an input data adjustment-based Back Propagation artificial neural networks model. First, the sounding data acquired at a Nanjing meteorological site in June 2014 were inputted into the Mono RTM Radiative transfer model to simulate atmospheric downwelling radiance at the 22 spectral channels from 22.234 GHz to 58.8 GHz, and we performed a comparison and analysis of the real observed data; an adjustment model for the measured microwave radiometer sounding data was built. Second, we simulated the sounding data of the 22 channels using the sounding data acquired at the site from 2011 to 2013. Based on the simulated rightness temperature data and the sounding data, BP neural network-based models were trained for the retrieval of atmospheric temperature, water vapor density and relative humidity profiles. Finally, we applied the adjustment model to the microwave radiometer sounding data collected in July 2014, generating the corrected data. After that, we inputted the corrected data into the BP neural network regression model to predict the atmospheric temperature, vapor density and relative humidity profile at 58 high levels from 0 to 10 km. We evaluated our model's effect by comparing its output with the real measured data and the microwave radiometer's own second-level product. The experiments showed that the inversion model improves atmospheric temperature and humidity profile retrieval accuracy; the atmospheric temperature RMS error is between 1 K and 2.0 K; the water vapor density's RMS error is between 0.2 g/m^3 and 1.93 g/m3; and the relative humidity's RMS error is between 2.5% and 18.6%.展开更多
基金supported by the National High Technology Research and Development Program of China(Grant No. 2007AA12Z144)the Professional Projects (Grant Nos.GYHY200706005 and GYHY200906036)the China Meteoro-logical Administration New Technology Promotion Project (GrantNo. CMATG2008Z04)
文摘Satellite retrieval of atmospheric water vapor is intended to further understand the role played by the energy and water cycle to determine the Earth's weather and climate.The algorithm for operational retrieval of total precipitable water (TPW) from the visible and infrared radiometer (VIRR) onboard Fengyun 3A (FY-3A) employs a split window technique for clear sky radiances over land and oceans during both day and night.The retrieved TPW is compared with that from the moderate resolution imaging spectroradiometer (MODIS) onboard the Terra satellite and data from radiosonde observations (RAOB).During the study period,comparisons show that the FY-3A TPW is in general agreement with the gradients and distributions from the Terra TPW.Their zonal mean difference over East Asia is smaller in the daytime than at night,and the main difference occurs in the complex terrain at mid latitude near 30°N.Compared with RAOB,the zonal FY-3A and the Terra TPW have a moist bias at low latitudes and a dry bias at mid and high latitudes;in addition,the FY-3A TPW performs slightly better in zonal mean biases and the diurnal cycle.The temporal variation of the FY-3A and the Terra TPW generally fits the RAOB TPW with the FY-3A more accurate at night while Terra TPW more accurate during the daytime.Comparisons of correlations,root mean square differences and standard deviations indicate that the FY-3A TPW series is more consistent with the RAOB TPW at selected stations.As a result,the FY-3A TPW has some advantages over East Asia in both spatial and temporal dimensions.
文摘风云三号A星(FY-3A)是中国第一颗携带微波湿度垂直探测仪(MicroWave Humidity Sounder,MWHS)的气象卫星,可以为全球及区域数值天气预报提供湿度信息。本文结合中尺度数值预报模式(Weather Research and Forecasting Model,WRF),利用改进后的中尺度数值预报同化系统(Weather Research and Forecasting Model Data Assimilation System,WRFDA),研究FY-3A/MWHS资料同化方法,并与美国国家海洋和大气管理局微波湿度计资料(National Oceanic and Atmospheric Administration/Advanced Microwave Sounding Unit-B,NOAA/AMSU-B)进行同化对比,分析FY-3A/MWHS资料同化对湿度场分析和预报的影响。结果表明:MWHS资料同化可以有效改进湿度分析场,且对分析场的改进明显优于NOAA/AMSUB资料同化,MWHS资料同化对湿度场预报的调整总体优于NOAA/AMSU-B资料同化,MWHS资料同化试验18 h内的湿度场预报精度明显优于NOAA/AMSU-B同化试验。由试验结果可知,中国FY-3A气象卫星MWHS资料在区域尺度数值预报中具有较好的应用前景。
基金National Key R&D Program of China(2018YFC1506603)The Second Tibetan Plateau Scientific Expedition and Research(STEP)Program(2019QZKK0105)。
文摘The Lightning Mapping Imager(LMI)equipped on the FY-4 A(Feng Yun-4 A)geostationary satellite achieves lightning positioning through optical imaging and has the advantages of high temporal resolution,high stability,and continuous observation.In this study,FY-4 A LMI lightning event,group and flash data from April to August 2018 are selected,and their quality are assessed through qualitative and quantitative comparison with the ground-based Advanced Time of Arrival and Direction system(ADTD)lightning observation network data and the American International Space Station(ISS)lightning imaging sensor(LIS)data.The results show that the spatial distributions of FY-4 A lightning are consistent with those of the ground-based ADTD and ISS LIS.The temporal variation in FY-4 A lightning group frequency is consistent with that of ADTD stroke,which reflects that FY-4 A LMI can capture the lightning occurrence in inland China.Quantitative statistics show that the consistency rate of FY-4 A LMI and ISS LIS events is relatively high but their consistency rate is lower in terms of lightning group and flash data.Compared with the lightning observations by the ISS LIS and the ground-based ADTD,FY-4 A LMI reports fewer lightning events in the Tibetan Plateau.The application of Tibetan Plateau lightning data requires further processing and consideration.
基金National Key Research and Development Program of China(2017YFC1501704,2016YFA0600703)Projects of International Cooperation and Exchanges NSFC(NSFC-RCUK_STFC)(61661136005)+2 种基金Major State Basic Research Development Program of China(973 Program)(2013CB430101)Six Talent Peaks Project in Jiangsu Province(2015-JY-013)Project Funded by the Priority Academic Program Development of Jiangsu Higher Education Institutions(PAPD),Key Laboratory of Radiometric Calibration and Validation for Environmental Satellites,National Satellite Meteorological Center,China Meteorological Administration
文摘Deviation exists between measured and simulated microwave radiometer sounding data. The bias results in low-accuracy atmospheric temperature and humidity profiles simulated by Back Propagation artificial neural network models. This paper evaluated a retrieving atmospheric temperature and humidity profiles method by adopting an input data adjustment-based Back Propagation artificial neural networks model. First, the sounding data acquired at a Nanjing meteorological site in June 2014 were inputted into the Mono RTM Radiative transfer model to simulate atmospheric downwelling radiance at the 22 spectral channels from 22.234 GHz to 58.8 GHz, and we performed a comparison and analysis of the real observed data; an adjustment model for the measured microwave radiometer sounding data was built. Second, we simulated the sounding data of the 22 channels using the sounding data acquired at the site from 2011 to 2013. Based on the simulated rightness temperature data and the sounding data, BP neural network-based models were trained for the retrieval of atmospheric temperature, water vapor density and relative humidity profiles. Finally, we applied the adjustment model to the microwave radiometer sounding data collected in July 2014, generating the corrected data. After that, we inputted the corrected data into the BP neural network regression model to predict the atmospheric temperature, vapor density and relative humidity profile at 58 high levels from 0 to 10 km. We evaluated our model's effect by comparing its output with the real measured data and the microwave radiometer's own second-level product. The experiments showed that the inversion model improves atmospheric temperature and humidity profile retrieval accuracy; the atmospheric temperature RMS error is between 1 K and 2.0 K; the water vapor density's RMS error is between 0.2 g/m^3 and 1.93 g/m3; and the relative humidity's RMS error is between 2.5% and 18.6%.
