Use of total precipitable water classification of a priori error and quality control in atmospheric temperature and water vapor sounding retrieval

This study investigates the use of dynamic a priori error information according to atmospheric moistness and the use of quality controls in temperature and water vapor profile retrievals from hyperspectral infrared (IR) sounders. Temperature and water vapor profiles are retrieved from Atmospheric InfraRed Sounder (AIRS) radiance measurements by applying a physical iterative method using regression retrieval as the first guess. Based on the dependency of first-guess errors on the degree of atmospheric moistness, the a priori first-guess errors classified by total precipitable water (TPW) are applied in the AIRS physical retrieval procedure. Compared to the retrieval results from a fixed a priori error, boundary layer moisture retrievals appear to be improved via TPW classification of a priori first-guess errors. Six quality control (QC) tests, which check non-converged or bad retrievals, large residuals, high terrain and desert areas, and large temperature and moisture deviations from the first guess regression retrieval, are also applied in the AIRS physical retrievals. Significantly large errors are found for the retrievals rejected by these six QCs, and the retrieval errors are substantially reduced via QC over land, which suggest the usefulness and high impact of the QCs, especially over land. In conclusion, the use of dynamic a priori error information according to atmospheric moistness, and the use of appropriate QCs dealing with the geographical information and the deviation from the first-guess as well as the conventional inverse performance are suggested to improve temperature and moisture retrievals and their applications.     

INVERSION OF MOISTURE PROFILES BY A NONLINEAR ITERATIVE PHYSICAL RETRIEVAL*

The Advanced TIROS-N Operational Vertical Sounder(ATOVS) measurements are used to generate the atmospheric parameters,such as temperature and moisture profiles,under both clear and cloudy situations.This paper describes briefly the nonlinear iterative physical retrieval method.By using this retrieval scheme,an experiment has been carried out to retrieve the moisture profiles from ATOVS measurements on the NOAA-16 satellite for July of 2002.ATOVS profile retrieval results are evaluated by root mean square(RMS) differences with respect to RAdiosonde OBservation(RAOB) profiles.The accuracy of the retrieval is about 15%-23% for the relative humidity profile in this study.     

基于一维变分算法的红外高光谱(IASI)卫星遥感大气温湿廓线研究

大气温湿度廓线是大气重要参数,在数值天气预报及天气预警中具有重要的应用价值。为获得高精度的大气温度与水汽混合比廓线数据,研究了基于Metop-A/IASI红外高光谱资料的大气温度与水汽混合比廓线变分反演方法。利用IASI高光谱传感器温度和水汽探测通道资料,结合CRTM模式和WRF模式预报技术,使用一维变分方法,研究了卫星资料质量控制、背景误差协方差本地化、观测误差协方差计算等方法,构建了大气温度及水汽混合比廓线变分反演系统,并在北京、青岛、沈阳3个地区开展了反演试验。以探空为标准的反演结果对比显示,使用WRF模式预报值作为背景场,温度的平均误差绝对值小于0.6 K,均方根误差为0.89 K;水汽混合比的平均误差绝对值小于0.021 g/kg,均方根误差为0.02 g/kg。试验结果表明:基于一维变分方法,可以利用Metop-A/IASI红外高光谱资料进行大气温度与水汽混合比廓线高精度探测。     

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.     

Calculation of updated coefficients for atmospheric temperature retrieval

In older to calculate updated coefficients for atmospheric temperature retrieval from satellite sounding data and radiosonde data, it is necessary to form statistical samples of real radiance and radiosonde data match-ups. A procedure is presented here for the data matchups. And a method of eigenvectors of statistical covariance matrices is used to produce updated coefficients for atmospheric temperature retrieval. The updated coefficients produced are tested using radiance observations from NOAA-7 satellite. Comparisons of these real-time retrieved data with radiosonde data show that the atmospheric temperature profiles retrieved have an accu-racy of RMS 2－3 degrees (oC). In addition, the error sources are also discussed.     

基于加密探空资料的上海不同云天下AIRS/Aqua相对湿度廓线反演精度研究

目前云对卫星相对湿度廓线反演精度的影响研究大多是针对云量,对其他云属性的影响研究尚少,云高也是影响卫星相对湿度廓线反演精度的重要因素。利用上海宝山站L波段(1型)加密探空资料,分析了上海地区7—9月不同质量控制标识、云量和云顶高度条件下大气红外探测器AIRS/Aqua (Atmospheric Infrared Sounder) 相对湿度廓线的反演精度,以期为今后开展AIRS等卫星资料的同化研究提供科学依据。结果表明:(1)AIRS相对湿度廓线反演误差随着云量的增加而逐渐增大,并且随着气压值的升高,少云与多云时的均方根误差(Root Mean Squared Error, RMSE)之差有逐渐增大的趋势;(2)云顶高度越高,AIRS相对湿度廓线反演精度越差,云顶以上湿度廓线反演精度更高,而云顶以下高度的反演误差较大;(3)高云且多云时,AIRS相对湿度廓线的反演精度最差,850 hPa处,AIRS相对湿度反演数据与探空资料绝对误差的下限达到了[-63.51%];(4)虽然质量控制标识为0时,AIRS湿度廓线在对流层范围内的反演精度仍达不到无线电探空的水平,但是相对于质量控制标识1时,反演精度明显提高。      

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。     

Research on the Application of the Radiative Transfer Model Based on Deep Neural Network in One-dimensional Variational Algorithm

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.     

大气红外探测器(AIRS)温、湿度反演产品的有效性检验及在数值模式中的应用研究

利用中国540个地面气象观测站点资料,对1和7月大气红外探测器(AIRS)的反演中国区域地面气温精度做了详细评估,分析了产生误差的原因。同时把AIRS的反演温、湿度廓线产品与T213数值预报产品进行比较,分析了它们之间的差异。为进一步考察AIRS温、湿度产品的有效性,我们把经过订正的AIRS地面气温以及温、湿度廓线产品分析同化到中尺度模式MM5中,用于华北降雪天气过程的对比模拟试验,分析AIRS反演产品对降雪量、水汽场、垂直速度场、散度场以及云物理过程等的影响。     

利用MODIS红外资料反演大气温湿度廓线的研究

概述了利用特征向量统计回归反演算法,从EOS/MODIS的红外通道资料反演大气温湿度垂直分布的过程,并与美国国家环境预报中心NCEP(National Centers for Environmental Prediction)的等压面再分析场资料按照纬度和气压高度进行了真实性检验。结果表明：由MODIS资料反演得到的大气温湿度参数能够揭示大气温湿度的垂直分布。在各个等压面上均方根误差平均值在中纬度地区为3.39K,低纬度地区为1.40K,近地面层、对流层顶附近及下垫面地形复杂的区域误差较大,总体上低纬度地区要好于中纬度地区。反演的水汽误差也为低纬度地区小于中纬度地区,且随高度升高,中、高纬度误差都逐渐减小并逐渐接近。     

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。     

TOVS水汽及演的误差分析及其订正

该文利用同步物理反演法以６小时数值预报场为背景场对１９９２年１月份ＮＯＡＡ－１２卫星垂直探测（ＴＯＶＳ）资料进行了大气参数反演，并对水汽反演误差进行了检验和分析．以常规观测作为检验的标准，检验结果按３条轨道统计，发现反演的相对湿度平均误差与背景场的误差密切相关，均为负值；在高层和高纬度，误差绝对值随高度的增加而增加，随纬度的升高而增加；误差的日际变化在高层比较稳定，低层振动较大．对反演误差进行订正后的结果表明，在５００ｈＰａ以上反演均比背景质量要好，尤其轨道Ａ所有层次上订正后的反演比背景场好．     

TOVS 水汽反演的误差分析及其订正

该文利用同步物理反演法以6小时数值预报场为背景场对1992年1月份NOAA-12卫星垂直探测（TOVS）资料进行了大气参数反演，并对水汽反演误差进行了检验和分析。以常规观测作为检验的标准，检验结果按3条轨道统计，发现反演的相对湿度平均误差与背景场的误差密切相关，均为负值；在高层和高纬度，误差绝对值随高度的增加而增加，随纬度的升高而增加；误差的日际变化在高层比较稳定，低层振动较大。对反演误差进行订正后的结果表明，在500 hPa以上反演均比背景质量要好，尤其轨道A所有层次上订正后的反演比背景场好。     

数值积分过程中截断误差和舍入误差的分离方法及其效果检验

本文讨论数值积分过程中截断误差和舍入误差的分离方法和理论,解析地给出某些数值计算方法的理论截断误差,并以此来分离计算结果中的误差.然后引入参考解的办法,用来分离更为一般的微分方程求解过程中的截断误差和舍入误差.以参考解算法为基础,对一个偏微分方程的数值解进行计算,所得结果与采用理论截断误差得到的结果进行了对比,发现:(...     

GNSS/PWV与风云四号A星GIIRS水汽廓线融合应用研究

中国新一代地球静止气象卫星风云四号A星（FY-4A）搭载的干涉式大气垂直探测仪（Geostationary Interferometric Infrared Sounder, GIIRS）以红外高光谱干涉分光方式探测三维大气温湿结构,取得了在静止轨道上探测大气的突破性进展。地基全球导航卫星系统（Global Navigation Satellite System,GNSS）是一种连续监测大气可降水量（Precipitable Water Vapor,PWV）的有效手段,基于2018年6—8月中国地基GNSS站监测的PWV和FY-4A/GIIRS水汽廓线的业务产品以及常规无线电探空资料,开展GNSS/PWV与FY-4A/GIIRS水汽廓线快速融合应用,以提高卫星资料反演大气水汽廓线的精度。结果表明:与常规无线电探空相比,FY-4A/GIIRS水汽廓线产品在大气低层均方根误差（Root Mean Square Error,RMSE）为4.5 g/kg,700 hPa为2.4 g/kg,500 hPa以上因水汽含量较低RSME小于1.5 g/kg。GNSS/PWV与FY-4A/GIIRS水汽廓线融合后,FY-4A/GIIRS水汽廓线误差整层RMSE减小20%,从近地层到600 hPa RMSE平均减小20%—25%,尤其是850—700 hPa改善最明显,极大改善了卫星水汽反演资料的可用性。对一次多系统影响的暴雨天气过程应用分析表明,GNSS/PWV和FY-4A/GIIRS融合产品可获得高时、空密度的大气水汽廓线,对强降水的临近预报有非常重要的支撑作用。      

Review and Development on the Studies of Chinese Meteorological Satellite and Satellite Meteorology

Meteorological satellite and satellite meteorology are the fastest developing new branches in the atmospheric sciences. Today the meteorological satellite has become a key element in the global atmospheric sounding system while the satellite meteorology is covering the main components of earth's system science. This article describes the major achievements that China has made in these fields in the past 30 years. The following contents are involved: (1) History and present status of China's meteorological satellites. It covers the development, launch, operation, technical parameters of China's polar and geostationary meteorological satellites. (2) Major achievements on remote sensing principle and method. It describes the retrieval of atmospheric temperature and humidity profiles, cloud character retrieval, aerosol character retrieval, precipitation retrieval as well as the generation of cloud wind. (3) Achievement on the studies of meteorological satellite data application. This part covers the applications of meteorological satellite data to weather analysis and forecast, numerical forecast, climate monitoring, and prediction of short-term climate change. Besides, the new results on data assimilation, climate monitoring, and forecast are also included.     

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。      

Retrieval of Vertical Distribution of Tropospheric Refractivity through Ground-Based GPS Observation简

In the present reported study, the vertical distributions of local atmospheric refractivity were retrieved from ground- based GPS observations at low elevation angles. An improved optimization method was implemented at altitudes of 0-10 km to search for a best-fit refractivity profile that resulted in atmospheric delays most similar to the delays calculated from the observations. A ray-tracing model was used to simulate neutral atmospheric delays corresponding to a given refractivity profile. We initially performed a ＂theoretical retrieval＂, in which no observation data were involved, to verify the optimization method. A statistical relative error of this ＂theoretical retrieval＂ （-2% to 2%） indicated that such a retrieval is effective. In a practical retrieval, observations were obtained using a dual-frequency GPS receiver, and its initial value was provided by CIRA86aQ_UoG data. The statistical relative errors of the practical retrieval range from -3% to 5% were compared with co-located radiosonde measurements, Results clearly revealed diurnal variations in local refractivity prc,files, The results also suggest that the general vertical distribution of refractivity can be derived with a high temporal resolution. However, further study is needed to describe the vertical refractivity gradient clearly.     

The Capability of Atmospheric Profile Retrieval from Satellite High Resolution Infrared Sounder Radiances

ＴｈｅＣａｐａｂｉｌｉｔｙｏｆＡｔｍｏｓｐｈｅｒｉｃＰｒｏｆｉｌｅＲｅｔｒｉｅｖａｌｆｒｏｍＳａｔｅｌｌｉｔｅＨｉｇｈＲｅｓｏｌｕｔｉｏｎＩｎｆｒａｒｅｄＳｏｕｎｄｅｒＲａｄｉａｎｃｅｓＬｉＪｕｎ（李俊）（Ｃｏｏｐｅｒａｔｉｖｅｉｎｓｔｉｔｕｔｅｆｏ...     

Retrieving atmospheric temperature profiles from AMSU-A data with neural networks

Back propagation neural networks are used to retrieve atmospheric temperature profiles from NOAA-16 Advanced Microwave Sounding Unit-A (AMSU-A) measurements over East Asia. The collocated radiosonde observation and AMSU-A data over land in 2002-2003 are used to train the network, and the data over land in 2004 are used to test the network. A comparison with the multi-linear regression method shows that the neural network retrieval method can significantly improve the results in all weather conditions. When an offset of 0.5 K or a noise level of ±0.2 K is added to all channels simultaneously, the increase in the overall root mean square (RMS) error is less than 0.1 K. Furthermore, an experiment is conducted to investigate the effects of the window channels on the retrieval. The results indicate that the brightness temperatures of window channels can provide significantly useful information on the temperature retrieval near the surface. Additionally, the RMS errors of the profiles retrieved with the trained neural network are compared with the errors from the International Advanced TOVS (ATOVS) Processing Package (IAPP). It is shown that the network-based algorithm can provide much better results in the experiment region and comparable results in other regions. It is also noted that the network can yield remarkably better results than IAPP at the low levels and at about the 250-hPa level in summer skies over ocean. Finally, the network-based retrieval algorithm developed herein is applied in retrieving the temperature anomalies of Typhoon Rananim from AMSU-A data.