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
The sounding data of a multi-channel parallel ground-based microwave radiometer （MWR） in Fuzhou station in July and August in 2016 were compared with the sounding data of a radiosonde in the same position in the sam...The sounding data of a multi-channel parallel ground-based microwave radiometer （MWR） in Fuzhou station in July and August in 2016 were compared with the sounding data of a radiosonde in the same position in the same period. The results showed that the correlations between the two types of temperature or humidity detected by the microwave radiometer and the radiosonde were significant at 0.05 level, indicating that the overall changing trends of temperature or humidity detected by the two devices were similar. The temperature detected by the microwave radiometer and the radiosonde decreased with the increase of height. The difference between the changes in the height of the zero layer detected by the micro- wave radiometer and the radiosonde was not significant, and their trends were basically the same.展开更多
利用常规观测资料、1°×1°NCEP再分析资料、微波辐射计及风廓线雷达等加密观测资料,针对2011年6月23日北京地区发生的一次强对流暴雨过程,对高低层干冷空气活动及其作用进行了分析。结果表明:北京地区高低层配置符合干侵...利用常规观测资料、1°×1°NCEP再分析资料、微波辐射计及风廓线雷达等加密观测资料,针对2011年6月23日北京地区发生的一次强对流暴雨过程,对高低层干冷空气活动及其作用进行了分析。结果表明:北京地区高低层配置符合干侵入三维模型,中层以上的干区与水汽图像的暗区相吻合,同时配合有自高纬向低纬传播的高位涡带,北京地区干侵入特征明显。微波辐射计与风廓线雷达观测能够实时追踪干冷空气造成的温度、湿度和风场变化,6 km高度以上的温度和湿度变化最为显著,两者下降到最低点的时间与强对流的触发时间有较好的一致性,高层干冷空气对强降水有一定激发作用。干侵入降低了大气稳定度,造成高层θse低值中心垂直位于低层θse高值中心之上,探空曲线呈典型的喇叭形开口结构。回流干冷空气与气旋前部暖湿气流形成准静止锋,850 h Pa以下低层温度锋区显著,为对流的发展加强提供了热动力条件,回流冷空气形成的东部高压有利于雷暴系统在北京地区滞留,延长了降水时间。展开更多
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%.
文摘The sounding data of a multi-channel parallel ground-based microwave radiometer （MWR） in Fuzhou station in July and August in 2016 were compared with the sounding data of a radiosonde in the same position in the same period. The results showed that the correlations between the two types of temperature or humidity detected by the microwave radiometer and the radiosonde were significant at 0.05 level, indicating that the overall changing trends of temperature or humidity detected by the two devices were similar. The temperature detected by the microwave radiometer and the radiosonde decreased with the increase of height. The difference between the changes in the height of the zero layer detected by the micro- wave radiometer and the radiosonde was not significant, and their trends were basically the same.
文摘利用常规观测资料、1°×1°NCEP再分析资料、微波辐射计及风廓线雷达等加密观测资料,针对2011年6月23日北京地区发生的一次强对流暴雨过程,对高低层干冷空气活动及其作用进行了分析。结果表明:北京地区高低层配置符合干侵入三维模型,中层以上的干区与水汽图像的暗区相吻合,同时配合有自高纬向低纬传播的高位涡带,北京地区干侵入特征明显。微波辐射计与风廓线雷达观测能够实时追踪干冷空气造成的温度、湿度和风场变化,6 km高度以上的温度和湿度变化最为显著,两者下降到最低点的时间与强对流的触发时间有较好的一致性,高层干冷空气对强降水有一定激发作用。干侵入降低了大气稳定度,造成高层θse低值中心垂直位于低层θse高值中心之上,探空曲线呈典型的喇叭形开口结构。回流干冷空气与气旋前部暖湿气流形成准静止锋,850 h Pa以下低层温度锋区显著,为对流的发展加强提供了热动力条件,回流冷空气形成的东部高压有利于雷暴系统在北京地区滞留,延长了降水时间。
