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As a typical physical retrieval algorithm for retrieving atmospheric parameters, one-dimensional variational (1DVAR) algorithm is widely used in various climate and meteorological communities and enjoys an important position in the field of microwave remote sensing. Among algorithm parameters affecting the performance of the 1DVAR algorithm, the accuracy of the microwave radiative transfer model for calculating the simulated brightness temperature is the fundamental constraint on the retrieval accuracies of the 1DVAR algorithm for retrieving atmospheric parameters. In this study, a deep neural network (DNN) is used to describe the nonlinear relationship between atmospheric parameters and satellite-based microwave radiometer observations, and a DNN-based radiative transfer model is developed and applied to the 1DVAR algorithm to carry out retrieval experiments of the atmospheric temperature and humidity profiles. The retrieval results of the temperature and humidity profiles from the Microwave Humidity and Temperature Sounder (MWHTS) onboard the Feng-Yun-3 (FY-3) satellite show that the DNN-based radiative transfer model can obtain higher accuracy for simulating MWHTS observations than that of the operational radiative transfer model RTTOV, and also enables the 1DVAR algorithm to obtain higher retrieval accuracies of the temperature and humidity profiles. In this study, the DNN-based radiative transfer model applied to the 1DVAR algorithm can fundamentally improve the retrieval accuracies of atmospheric parameters, which may provide important reference for various applied studies in atmospheric sciences.  相似文献   
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FY-3C/MWHTS资料反演陆地晴空大气温湿廓线   总被引:2,自引:0,他引:2       下载免费PDF全文
针对风云三号C星微波湿温探测仪(FY-3C/MWHTS)的陆地晴空观测资料,建立了一维变分反演系统,对大气的温湿廓线进行反演。为了更好地描述温湿廓线的相关性,同时减小温度和湿度在反演过程中相互之间的误差传递,提出了使用背景协方差矩阵的联合矩阵和单独矩阵进行组合反演的方法。对于MWHTS模拟亮温和观测亮温之间的偏差,使用逐扫描点的统计回归方法进行校正。选择中国部分陆地区域的晴空观测亮温进行温湿廓线的反演,并利用欧洲中期天气预报中心(ECMWF)再分析数据、美国国家环境预报中心(NCEP)分析数据以及无线电探空观测(RAOB)数据对反演结果进行验证,温湿廓线的反演结果与ECMWF再分析数据验证的最大均方根误差分别是2.59 K和11.87%,与NCEP分析数据验证的最大均方根误差分别是1.88 K和21.50%,与RAOB数据验证的最大均方根误差分别是3.43 K和25.48%,验证结果表明了反演结果的可靠性。另外与国外同类载荷AMSU观测亮温的物理方法和统计方法反演精度进行了对比,结果表明:MWHTS具有较强的湿度廓线以及高空温度廓线的探测能力,且针对MWHTS的观测亮温建立的一维变分反演系统具有较高的反演精度。与NCEP 6小时预报廓线的验证结果表明:反演的湿度廓线可以提高预报廓线的精度。  相似文献   
3.
The Microwave Temperature Sounder-Ⅱ(MWTS-Ⅱ) and Microwave Humidity and Temperature Sounder(MWHTS) onboard the Fengyun-3 C(FY-3 C) satellite can be used to detect atmospheric temperature profiles. The MWTS-II has 13 temperature sounding channels around the 60 GHz oxygen absorption band and the MWHTS has 8 temperature sounding channels around the 118.75 GHz oxygen absorption line. The data quality of the observed brightness temperatures can be evaluated using atmospheric temperature retrievals from the MWTS-Ⅱ and MWHTS observations. Here, the bias characteristics and corrections of the observed brightness temperatures are described. The information contents of observations are calculated, and the retrieved atmospheric temperature profiles are compared using a neural network(NN) retrieval algorithm and a one-dimensional variational inversion(1 D-var) retrieval algorithm. The retrieval results from the NN algorithm show that the accuracy of the MWTS-Ⅱ retrieval is higher than that of the MWHTS retrieval, which is consistent with the results of the radiometric information analysis. The retrieval results from the 1 D-var algorithm show that the accuracy of MWTS-Ⅱ retrieval is similar to that of the MWHTS retrieval at the levels from 850-1,000 h Pa, is lower than that of the MWHTS retrieval at the levels from 650-850 h Pa and 125-300 h Pa, and is higher than that of MWHTS at the other levels. A comparison of the retrieved atmospheric temperature using these satellite observations provides a reference value for assessing the accuracy of atmospheric temperature detection at the 60 GHz oxygen band and 118.75 GHz oxygen line. In addition, based on the comparison of the retrieval results, an optimized combination method is proposed using a branch and bound algorithm for the NN retrieval algorithm, which combines the observations from both the MWTS-Ⅱand MWHTS instruments to retrieve the atmospheric temperature profiles. The results show that the optimal combination can further improve the accuracy of MWTS-Ⅱ retrieval and enhance the detection accuracy of atmospheric temperatures near the surface.  相似文献   
4.
For Microwave Humidity and Temperature sounder (MWHTS) measurements over the ocean, a cloud filtering method is presented to filter out cloud- and precipitation-affected observations by analyzing the sensitivity of the simulated brightness temperatures of MWHTS to cloud liquid water, and using the root mean square error (RMSE) between observation and simulation in clear sky as a reference standard. The atmospheric temperature and humidity profiles are retrieved using MWHTS measurements with and without filtering by multiple linear regression (MLR), artificial neural networks (ANN) and one- dimensional variational (1DVAR) retrieval methods, respectively, and the effects of the filtering method on the retrieval accuracies are analyzed. The numerical results show that the filtering method can improve the retrieval accuracies of the MLR and the 1DVAR retrieval methods, but have little influence on that of the ANN. In addition, the dependencies of the retrieval methods upon the testing samples of brightness temperature are studied, and the results show that the 1DVAR retrieval method has great stability due to that the testing samples have great impact on the retrieval accuracies of the MLR and the ANN, but have little impact on that of the 1DVAR.  相似文献   
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