王云,王振会,李青,朱雅毓. 2014. 基于一维变分算法的地基微波辐射计遥感大气温湿廓线研究[J]. 气象学报, 72(3):570-582, doi:10.11676/qxxb2014.036
基于一维变分算法的地基微波辐射计遥感大气温湿廓线研究
Research of the one-dimensional variational algorithm for retrieving temperature and humidity profiles from the ground-based microwave radiometer
投稿时间:2013-07-17  修订日期:2014-02-24
DOI:10.11676/qxxb2014.036
中文关键词:  地基微波辐射计  无云样本  质量控制  一维变分  温湿廓线
英文关键词:The ground-based microwave radiometer  Cloudless samples  Quality control  1DVAR  Temperature and humidity profiles
基金项目:国家自然科学基金项目(41275043、41005005)、城市气象科学研究基金(IUMKY&UMRF201101)、江苏省研究生创新项目(CXLX12-0499)。
作者单位
王云 南京信息工程大学气象灾害预报预警与评估协同创新中心, 中国气象局重点实验室, 南京, 210044
南京信息工程大学大气物理学院, 南京, 210044 
王振会 南京信息工程大学气象灾害预报预警与评估协同创新中心, 中国气象局重点实验室, 南京, 210044
南京信息工程大学大气物理学院, 南京, 210044 
李青 南京信息工程大学大气物理学院, 南京, 210044 
朱雅毓 南京信息工程大学大气物理学院, 南京, 210044 
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中文摘要:
      为研究地基微波辐射计遥感温、湿度廓线的一维变分算法的反演能力,用北京地区2010—2011年00和12时(世界时)的多通道地基微波辐射计亮温资料进行试验。首先,利用同时次的地面观测资料、红外亮温(由地基微波辐射计自带红外传感器测得)及探空观测数据,给出提取无云样本的方案,得到432个无云样本;再以辐射传输模式计算得到的模拟亮温为参考,对无云条件下的观测亮温进行质量控制;然后利用探空数据进行模拟试验,结果发现,一维变分算法对3 km以下的温度廓线有较大调整,使反演结果更加接近探空,而对湿度廓线在0—10 km都有不同程度的优化;最后利用一维变分算法对地基微波辐射计观测亮温进行大气温湿廓线反演,将结果与探空对比可以看出,温度廓线的均方根误差小于2.9 K,绝对湿度的均方根误差小于0.47 g/m3;进一步与地基微波辐射计自带神经网络的反演结果比较表明,一维变分的反演结果更接近实际大气。
英文摘要:
      In order to estimate the retrieval ability of the one-dimensional variational (1DVAR) algorithm which was applied to obtaining temperature and humidity profiles from observations of the ground-based microwave radiometer, brightness temperature data observed by a multi-channel ground-based microwave radiometer at 00:00 and 12:00 UTC in Beijing of 2010 and 2011. First of all, the 432 cloudless samples have been obtained by the procuring cloudless sample method based on the simultaneous surface-based observing data, infrared brightness temperature (observed by the infrared sensor installed on the ground-based microwave radiometer) and radiosonde observations. Then, quality control over observed brightness temperature has been made according to the brightness temperature calculated by the radiative transfer model. After that, the simulated experiments using the radiosonde observations have been done, and we achieved that the accuracy of the retrieved temperature profiles are statistically better than this of background profiles below 3 km, and 1DVAR retrievals improved the background humidity profile from the ground surface up to 10 km. At last, the temperature and humidity profiles retrieved by the 1DVAR algorithm are compared with radiosonde observations. The result showed that the retrieved profiles achieved a root mean square error (RMSE) with respect to radiosonde observations less than 2.9 K for temperature profiles and less than 0.47 g/m3 for absolute humidity profiles below 10 km in height. Comparing with the neural network (NN) algorithm of the microwave radiometer, the retrieval results of the 1DVAR algorithm were closer to the real atmosphere.
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