EFFECTS OF A CLOUD FILTERING METHOD FOR FENGYUN-3C MICROWAVE HUMIDITY AND TEMPERATURE SOUNDER MEASUREMENTS OVER OCEAN ON RETRIEVALS OF TEMPERATURE AND HUMIDITY

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.     

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

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/m~3;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。     

A COMPARISON OF THE RETRIEVAL OF ATMOSPHERIC TEMPERATURE PROFILES USING OBSERVATIONS OF THE 60 GHZ AND 118.75 GHZ ABSORPTION LINES

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.     

云对地基微波辐射计反演湿度廓线的影响

利用中国气象局大气探测试验基地的L波段探空数据和微波辐射计观测数据,采用MonoRTM辐射传输模型作为正演亮温模型,BP (back propagation) 神经网络作为反演工具,在由亮温反演大气湿度廓线的过程中,添加与样本匹配的云底高度和云厚度信息,建立新的反演模型,使新反演模型得到的反演湿度廓线和未添加云信息的反演湿度廓线分别与探空数据进行对比,获取两种反演方法各高度层的均方根误差,分析云信息对反演大气湿度廓线的影响。对比结果表明:未添加云信息时,测试样本的反演湿度廓线与探空廓线的相关系数平均值为0.685,而添加云信息后,相关系数平均值为0.805。相比未添加云信息的反演廓线,添加云信息之后多数高度层的均方根误差均有不同程度减小,而在有云以上高度层表现尤为明显。     

Retrieval of Upper Tropospheric Relative Humidity by the GMS-5 Water Vapor Channel： A Study of the Technique

This paper presents an analysis of a technique for retrieving upper tropospheric relative humidity through the GMS-5 satellite‘s 6.7-micron water vapor channel brightness temperature. NCEP analysis shows that a critical assumption of the retrieval theory, namely the constant temperature lapse rate,matches only in the tropical atmosphere. By statistical analyses of brightness temperature simulated by a radiative transfer model and of relative humidity, we examine the effect of lapse rate on this retrieval method and obtain retrieval parameters and error estimates applicable to the GMS-5 satellite over East Asia. If the retrieval parameters are properly chosen, the relative error of retrieving the upper tropospheric relative humidity in this region is less than 10%, and if applied to the low-latitude summer atmosphere, it is less than 5%.     

0-10 KM TEMPERATURE AND HUMIDITY PROFILES RETRIEVAL FROM GROUND-BASED MICROWAVE RADIOMETER

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 are inputted into the MonoRTM Radiative transfer model to simulate atmospheric downwelling radiance at the 22 spectral channels from 22.234GHz to 58.8GHz, 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 1K and 2.0K; the water vapor density’s RMS error is between 0.2 g/m3 and 1.93g/m3; and the relative humidity’s RMS error is between 2.5% and 18.6%.     

微波辐射计温湿廓线反演方法改进试验

为提升地基微波辐射计在不同天气条件下, 特别是云天条件下温湿廓线的反演精度, 利用2011年1月—2016年12月中国气象局北京国家综合气象观测试验基地探空数据, 在微波辐射计反演温湿度廓线的过程中通过区分晴天和云天条件并引入全固态Ka波段测云仪云高及云厚信息, 对反演输入亮温进行质量控制和偏差订正, 建立BP神经网络模型, 采用2017年1月—2018年3月微波辐射计探测数据评估检验, 结果表明:在亮温订正前提下, 晴天温度模型、云天温度模型、晴天相对湿度模型和云天相对湿度模型反演结果与探空的相关系数分别为0.99, 0.99, 0.80和0.78, 均方根误差为2.3℃, 2.3℃, 9%和16%, 较微波辐射计自带产品（LV2产品）减小约0.4℃, 0.3℃, 11%和9%, 准确性提升约30%, 28%, 64%和45%;温度模型偏差在±2℃以内、湿度模型偏差在±20%以内的占比分别为68%, 70%和95%, 78%, 较LV2产品分别提高了7%, 5%和27%, 23%, 其中相对湿度改善明显。可见亮温订正、区分天气类型训练反演模型有利于改善地基微波辐射温湿廓线反演精度。     

A Study on Retrieving Atmospheric Profiles from EOS/AIRS Observations

1. IntroductionThe development of global climate and weathermodels requires accurate monitoring of atmospherictemperature and moisture profiles, as well as the con-tents of trace gases and aerosols. It is quite difficultto monitor continuously these parameters on a globalscale.Until recently. AIRS (Atmospheric InfraredSounder) offers a new opportunity to improve globalmonitoring of temperature, moisture, and ozone distri-butions and changes therein. The high spectral resolu-tion (v/Δv ? 12…     

One-Dimensional Variational Retrieval of Temperature and Humidity Profiles from the FY4A GIIRS

A physical retrieval approach based on the one-dimensional variational(1 D-Var) algorithm is applied in this paper to simultaneously retrieve atmospheric temperature and humidity profiles under both clear-sky and partly cloudy conditions from FY-4 A GIIRS(geostationary interferometric infrared sounder) observations. Radiosonde observations from upper-air stations in China and level-2 operational products from the Chinese National Satellite Meteorological Center(NSMC)during the periods from December 2019 to January 2020(winter) and from July 2020 to August 2020(summer) are used to validate the accuracies of the retrieved temperature and humidity profiles. Comparing the 1 D-Var-retrieved profiles to radiosonde data, the accuracy of the temperature retrievals at each vertical level of the troposphere is characterized by a root mean square error(RMSE) within 2 K, except for at the bottom level of the atmosphere under clear conditions. The RMSE increases slightly for the higher atmospheric layers, owing to the lack of temperature sounding channels there.Under partly cloudy conditions, the temperature at each vertical level can be obtained, while the level-2 operational products obtain values only at altitudes above the cloud top. In addition, the accuracy of the retrieved temperature profiles is greatly improved compared with the accuracies of the operational products. For the humidity retrievals, the mean RMSEs in the troposphere in winter and summer are both within 2 g kg^–1. Moreover, the retrievals performed better compared with the ERA5 reanalysis data between 800 h Pa and 300 h Pa both in summer and winter in terms of RMSE.     

基于BP神经网络与遗传算法反演大气温湿廓线

为提高地基微波辐射计大气探测精度,融合BP神经网络与遗传算法,研究0~10 km大气温湿度廓线。首先,结合数据特征,基于数值模拟技术,建立一套TP/WVP-3000型号地基微波辐射计的一级数据质量控制和订正模型。然后,为减小训练样本代表性误差对模型反演精度的影响,利用遗传算法优化训练样本数据,建立一套精度更高的神经网络大气温湿度反演模型。最后,利用构建的反演模型,开展大气温湿度反演试验,结合探空资料和微波辐射计二级产品,评价反演模型精度。研究结果表明:(1)经过质量控制后的实测数据与模拟数据之间的相关性有显著提升;(2)经过质量控制与订正后建立的神经网络模型对比原微波辐射计二级产品的反演精度有一定提升,温度提升6.77%,湿度提升20.11%;(3)经过遗传算法优化后的训练样本所建立的神经网络反演模型对比原微波辐射计二级产品反演精度有进一步的提升,温度提升10.21%,湿度提升23.75%,反演结果与该地区同类型研究结果相比有着较大提升。      

FY-3A微波资料偏差订正及台风路径预报应用

我国极轨气象卫星FY-3A大大增强了对地球系统的综合探测能力,而偏差订正对卫星资料的应用非常必要。试验中FY-3A卫星微波资料的偏差订正方案是在Harris等的TOVS辐射资料偏差订正经验方法的基础上结合WRF-3DVAR系统发展的,偏差订正后微波资料各通道拟合结果基本位于主对角线上,大多数卫星观测数据与观测算子利用背景场计算的亮温值分布趋于合理,偏差得到很大程度的降低。偏差订正后,利用数值模式直接同化FY-3A气象卫星微波资料,通过对2008年和2009年的4个台风进行预报评估表明:同化FY-3A气象卫星资料后路径预报能力提高明显,尤其是36 h后路径预报结果;同化FY-3A气象卫星微波资料后台风预报路径误差平均降低20%,而只同化常规资料路径误差仅仅降低了4%。     

探测大气温湿廓线的35通道微波辐射计设计原理与特点

总结了自主研制的MWP967KV型地基35通道微波辐射计系统设计原理和主要特点。系统将K、V双频段宽带天线及接收机、宽带调谐本振、温湿压计以及电源模块紧凑集成为一台整机设备。对各传感器的测量输出实时进行一体化综合处理,采用BP神经网络实时反演大气温度、湿度廓线和汽、水总量。廓线的垂直覆盖范围为地表至顶空10km,共划分为58层。辐射计样机于2012年秋冬季节在北京地区开展了为期2个月的观测试验,利用这段时期内的69个探空资料样本对辐射计2个层次的实时观测输出都进行了对比检验,计算了水汽、氧气通道亮温和反演所得大气温湿廓线的平均偏差、均方差以及相关性。结果表明该系统能够满足实时气象监测的需求,达到国际先进水平。     

Atmospheric profile retrieval with AIRS data and validation at the ARM CART site

The physical retrieval algorithm of atmospheric temperature and moisture distribution from the Atmospheric InfraRed Sounder （AIRS） radiances is presented. The retrieval algorithm is applied to AIRS clear-sky radiance measurements. The algorithm employs a statistical retrieval followed by a subsequent nonlinear physical retrieval. The regression coefficients for the statistical retrieval are derived from a dataset of global radiosonde observations （RAOBs） comprising atmospheric temperature, moisture, and ozone profiles. Evaluation of the retrieved profiles is performed by a comparison with RAOBs from the Atmospheric Radiation Measurement （ARM） Program Cloud And Radiation Testbed （CART） in Oklahoma, U. S. A.. Comparisons show that the physically-based AIRS retrievals agree with the RAOBs from the ARM CART site with a Root Mean Square Error （RMSE） of 1K on average for temperature profiles above 850 hPa, and approximately 10% on average for relative humidity profiles. With its improved spectral resolution, AIRS depicts more detailed structure than the current Geostationary Operational Environmental Satellite （GOES） sounder when comparing AIRS sounding retrievals with the operational GOES sounding products.     

基于MonoRTM模型的微波辐射计反演方法研究

基于辐射传输模型MonoRTM计算天空亮温度,使用多元线性统计回归方法和BP神经网络方法分别对大气温度和水汽密度廓线进行了反演,检验并分析了两种方法的反演精度.结果表明,多元线性回归方法反演的温度偏差总体不大于6K,反演的水汽密度偏差小于4 g/m3;神经网络方法反演的温度偏差小于2K,反演的水汽密度误差总体不大于2 g/m3.与探空数据的对比表明,对于大气温度和水汽密度反演,BP神经网络方法的反演结果都要比多元线性回归方法的反演结果更接近探空资料值.     

RETRIEVING ATMOSPHERIC SOUNDING PROFILES AROUND TYPHOON YUNNA USING INFRARED HYPERSPECTRAL MEASUREMENTS AIRS

In this study,we derived atmospheric profiles of temperature,moisture,and ozone,along with surface emissivity,skin temperature,and surface pressure,from infrared-sounder radiances under clear sky (cloudless) condition.Clouds were detected objectively using the Atmospheric Infrared Sounder under a relatively low spatial resolution and cloud-mask information from the Moderate Resolution Imaging Spectroradiometer under a high horizontal resolution;this detection was conducted using space matching.Newton’s nonlinear physical iterative solution technique is applied to the radiative transfer equation (RTE) to retrieve temperature profiles,relative humidity profiles,and surface variables simultaneously.This technique is carried out by using the results of an eigenvector regression retrieval as the background profile and using corresponding iterative forms for the weighting functions of temperature and water-vapor mixing ratio.The iterative forms are obtained by applying the variational principle to the RTE.We also compared the retrievals obtained with different types of observations.The results show that the retrieved atmospheric sounding profile has great superiority over other observations by accuracy and resolution.Retrieved profiles can be used to improve the initial conditions of numerical models and used in areas where conventional observations are sparse,such as plateaus,deserts,and seas.     

A polarized microwave radiative transfer model for passive remote sensing

The objective of the present work is to develop a general purpose, polarized, microwave radiative transfer forward model, including calculation of interaction parameters for fast, simultaneous and accurate generation of radiances, for use in a wide variety of atmospheric retrieval applications. This complete polarized model includes the generation of atmospheric profiles, calculation of the interaction parameters and the solution of the vector radiative transfer equations. In the present work, the doubling and adding method is used to calculate radiances. Mie theory is used for spherical particles to evaluate the polarized scattering matrix. Two types of sea surface models applicable for specular and diffuse surfaces respectively are implemented. The model has been validated against standard benchmark cases and experimental data available in literature. While the model is itself generic, parametric studies are performed to study the influence of the vertical structure of the atmosphere, for two polarizations and a set of frequencies to be used on the microwave imager aboard the proposed Indo-French climate research satellite MEGHA-TROPIQUES.     

地基微波辐射计探测大气边界层高度方法

采用2013年中国科学院大气物理研究所香河大气综合观测试验站的地基微波辐射计和激光雷达观测数据,以激光雷达探测的大气边界层高度为参考,分别利用非线性神经网络和多元线性回归方法建立微波亮温直接反演大气边界层高度的算法,并对比两种方法的反演能力, 同时分析非线性神经网络算法在不同时段及不同天气状况下反演结果的差异。结果表明:非线性神经网络算法的反演能力优于多元线性回归算法,其反演结果与激光雷达探测的大气边界层高度有较好一致性,冬、春季的相关系数达到0.83,反演精度比线性回归算法约高26%;对于不同时段和不同天气条件,春季的反演结果最好,晴空的反演结果好于云天; 四季和不同天气状况的划分也有利于提高反演精度。     

利用AIRS卫星资料反演大气廓线Ⅰ.特征向量统计反演法

引进美国威斯康星大学的IMAPP（International MODIS／AIRS Preprocessing Package）软件包，介绍了利用高光谱分辨率大气红外探测器AIRS（Atmospheric Infrared Sounder）观测辐射值，用特征向量统计法反演大气温度、湿度等垂直廓线的算法，采用亮度温度分类和扫描角分类回归后，减小了反演误差。并将其应用到中国地区，通过与无线电探空值及欧洲中期天气预报中心ECMWF（European Center of Medium-range Weather Forecasts）客观分析场的比较，结果表明：该方法所获得的温度、水汽反演结果与探空观测及ECMWF大气廓线分布一致，且AIRS因其高光谱分辨率（即高垂直空间分辨率）显示了精细的大气结构。     

机载微波大气温度探测仪多高度飞行观测试验结果分析

机载微波大气温度探测仪可以机动灵活地获取大气温度廓线信息。针对一次机载微波大气温度探测仪的多高度飞行观测试验,基于逐线积分模式和大气参数廓线库,建立用于不同飞行高度的快速辐射传输模式,分析了仪器观测亮温的质量并对仪器观测进行了订正;建立了基于神经网络的微波大气温度廓线反演算式,分析了不同高度、不同通道选择对于大气温度廓线反演性能的影响。研究结果表明:（1）较低飞行高度计算得到的各地表敏感通道地表比辐射率之间具有较好的一致性;（2）采用订正算式订正后,不同飞行高度的模拟亮温与观测亮温具有较好的一致性;（3）机载微波大气温度反演最优通道组合依赖于平台飞行高度;（4）采用最优的通道组合,4 200 m、3 200 m和2 500 m高度层温度反演均方根误差范围分别为0.5~1.8 K、0.5~1.3 K和0.4~1.0 K。      

Remote sensing of cloud liquid water

Summary A method is presented to infer cloud liquid water path (LWP in kg/m²) over the ocean from passive microwave measurements of SSM/I. The algorithm to retrieve LWP is based on simulated satellite observations. They are calculated with a radiative transfer model applied to about 3000 radiosonde ascents over the Atlantic Ocean. Since radiosonde observations do not contain direct information about cloud water and ice, these parameters are parameterized based on relative humidity and temperature using modified adiabatic liquid water density profiles. A multiple linear regression is applied to the simulated radiances and the calculated LWP to derive the algorithm. The retrieval accuracy based on the regression analysis including instrumental noise is 0.03 kg/m². Validation of the LWP-algorithm was pursued through a comparison with measurements of a ground-based 33 GHzmicrowave radiometer on board of R.V. Poseidon during the International Cirrus Experiment 1989 at the North Sea (ICE'89). The LWP values agree within the range of uncertainty caused by the different sampling characteristics of the observing systems. The retrieval accuracy for clear-sky cases determined using colocated METEOSAT data over the North Sea is 0.037 kg/m² and confirms the accuracy estimated from regression analysis for the low liquid water cases.The algorithm was used to derive maps of monthly mean LWP over the Atlantic Ocean. As an example the Octobers of the 5 years 1987–1991 were selected to demonstrate the interannual variability of LWP. The results were compared with the cloud water content produced by the climate model ECHAM-T2 from the Max-Planck-Institut Hamburg.Observations during ICE'89 were used to check the accuracy of the applied radiative transfer model. Brightness temperatures were calculated from radiosonde ascents launched during the overpass of DMSP-F8 in cloud-free situations. The channel-dependent differences range from about –2 to 3 K.The possibility to identify different cloud types using microwave and infrared observations was examined. The main conclusion is that simultaneous microwave and infrared measurements enable the separation of dense cirrus and cirrus with underlying water clouds. A classification of clouds with respect to their top heights and LWP was carried out using a combination of SSM/I derived LWP and simultaneously recorded Meteosat IR-data during ICE'89.With 11 Figures