COSMIC-2掩星反演数据质量分析

为评价COSMIC-2掩星反演数据精度,利用探空站点资料,对比验证了基于COSMIC-2附加相位数据由无线电掩星数据处理软件ROPP计算得到的大气折射率及温湿廓线。实验结果表明:COSMIC-2数据经反演后的大气廓线质量较高,大气折射率相对偏差标准差为1.5%~4.3%,大气温度和相对湿度均方根误差分别为1.3~2.2 K、10%~15%;低纬地区样本精度总体优于中纬地区; 季节性差异不明显,但冬春季样本精度总体略优于夏秋季。反演数据与COSMIC-2二级产品相比,折射率与温度在部分高度层精度更优,相对湿度在总体上精度更优。      

红外高光谱资料模拟大气廓线反演对云的敏感性

用不同云顶高度和不同有效云量时的星载红外高光谱观测值,模拟大气温湿垂直廓线反演对云参数的敏感性。用特征向量统计反演法按照云顶高度和有效云量分类反演了大气温湿廓线,统计了不同云高（200,300,500,700和850hPa）及云高有50hPa误差时温度、水汽混合比反演的均方根误差随有效云量的变化。结果表明,随着有效云量的增大,云顶以下各高度层上的温度、水汽混合比反演误差都明显增大。云高有50hPa误差较准确已知而言,温度和水汽混合比的反演误差增大,但温度反演对云高误差的敏感性比水汽反演要高。     

浙北沿岸海域海面风场反演方法的研究

高质量的海面实况风场是海洋气象监测和预报的基础,卫星反演的大陆沿岸海域海面风场的准确率不高。文章基于反映近地面风速廓线变化的指数律公式,利用浙江北部沿岸海岛或滩涂上布设的中尺度自动站来反演附近海面风场,并使用客观分析方法将反演的离散风场值转换到中尺度网格上,从而获得完整的高分辨率海面风场。指数律中参数α值对于反演风场的准确率至关重要,它主要受到下垫面状况以及大气层结状态的影响,而后者的影响较前者更大。文章使用多个风塔站的风廓线率值进行了反演风场的误差试验,结果表明:目前单一风速廓线还无法取得最优的反演效果,有必要分季节使用多站风速廓线。使用混合风速廓线得到的总体样本的平均偏差为0.04 m·s~(-1),平均绝对误差为1.51 m·s~(-1),均方根误差为2.01 m·s~(-1)。对海面反演风场的优化可以将总体样本的平均绝对误差和均方根误差分别降低到1.28和1.68 m·s~(-1)。     

云雷达和微波辐射计联合反演大气湿度廓线的初步研究

利用中国气象局大气探测试验基地的微波辐射计亮温值、对应的云雷达测得的反射率因子和L波段探空数据,采用BP(back propagation)神经网络作为反演工具,反演得到大气垂直湿度廓线。将天气情况分为晴天、低云、中云和高云四种情况,对比不加入反射率因子的反演湿度廓线,分析两种反演方法在各高度层的均方根误差。对比结果表明,加入反射率因子的反演湿度廓线与探空廓线的相关系数平均值为0.862,均方根误差为14. 9%,而不添加反射率因子的相关系数平均值为0.763,均方根误差为19.2%。     

大气红外探测器(AIRS)温、湿廓线反演产品及边界层高度在黄土高原的验证

利用黄土高原地区加强观测期探空资料和大气红外探测器(Atmospheric Infrared Sounder,AIRS)反演的温度、相对湿度廓线资料,评估了AIRS反演产品在黄土高原的适用性,以及利用AIRS温度廓线计算边界层高度的可行性。结果表明,各种边界层高度计算方法相关性显著,平均高度差异一般不超过200 m;Richardson数临界值的选取对确定边界层高度的影响不大。AIRS反演的大气温度和相对湿度均能很好地反映环境温湿的变化;温度平均偏差在±1 K以内,均方根误差一般不超过2 K;AIRS反演的地面气温误差相对较大,平均偏差和均方根误差分别为-1.68 K和3.32 K,并且会影响边界层高度的确定;AIRS相对湿度平均偏差在±10%以内,均方根误差不超过20%。利用Parcel法估计的边界层高度结果显示,根据AIRS反演的温度廓线确定的AIRS边界层高度低于利用探空观测资料确定的边界层高度,但能较好地再现边界层高度的变化,在没有探空数据时,可以用AIRS反演的温度廓线确定边界层高度。     

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.     

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。     

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。     

激光雷达和微波辐射计温湿度廓线与探空数据的对比分析

基于西安泾河国家气象站2021年微波辐射计、西安理工大学研制的大气温湿度廓线激光雷达等设备观测数据反演计算0～10 km 以下的温湿度，通过与探空资料的对比分别评估了微波辐射计和激光雷达的探测性能。结果表明，二者反演的温度均与探空数据具有较好的一致性，激光雷达反演的温度和探空温度的相关系数为0997，均方根误差在15～25 K之间，微波辐射计反演的温度和探空温度的相关系数为0973，均方根误差在15～50 K之间；在反演相对湿度方面激光雷达优于微波辐射计，激光雷达反演的相对湿度和探空相对湿度的相关系数为0964，均方根误差在5%～15%之间，微波辐射计反演的相对湿度和探空相对湿度的相关系数为0632，均方根误差在20%左右。     

利用气象卫星资料反演大气温度廓线的初步试验

气象卫星垂直探测资料在国外已广泛用于数值天气预报中,本文介绍了由气象卫星垂直探测资料反演大气温度廓线的方法及回归反演系数的计算。最后给出了利用NOAA-7 TOVS资料反演大气温度廓线的试验结果。试验结果与常规探测温度的比较表明:绝对平均偏差和均方根误差在各等压面上大致为2—3℃。     

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.     

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.     

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.     

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%.     

全球大气温度廓线的统计特性分析

利用欧洲数值预报中心发布的第三代ERA-Interim月平均再分析资料对1979—2016年全球大气温度廓线进行统计,分别探讨了南、北半球大气温度廓线平均值和标准差随季节、纬度和海陆的时空分布特征,并与国内外常用的AFGL标准大气廓线进行了对比。研究结果表明:南、北半球各高度层温度的平均值、频数最大温度区间和最大值垂直分布均随高度先减小后增加;在大气低层,多年的温度波动较大,200 hPa处波动达到最小;不论是北半球还是南半球,大气温度平均值廓线均具有典型的纬度差异,其中低纬度季节性差异较小,中纬度次之,高纬季节性差异最大;各季节大气温度廓线的海陆差异不同,且南半球海陆差异比北半球大。大气温度标准差廓线同样存在纬度、海陆和季节分布差异。根据ECMWF再分析资料构建的温度廓线较AFGL标准大气廓线而言具有更加丰富的时空分布等细节特征,并且代表了气候变化后最新的大气温度状况。有关结果可作为现有标准大气廓线的更新和完善,为新型卫星仪器应用性能评估、辐射传输算法和大气反演方法评价和对比分析提供支持。      

GPCP和TRMM PR热带月平均降水的差异分析

文中利用GPCP(Global Precipitation Climatology Project)和TRMM(Tropical Rainfall Measuring Missio) PR(Precipitation Radar)资料,分析了1998～2002年热带地区月平均降水的差异及其主要原因.结果表明,GPCP和TRMM PR资料能一致地反映热带降水的主要分布特征,但降水的强度和范围存在着差异;两种资料的差异与雨强有密切关系;平均而言,洋面上的降水差异(0.5 mm/d)大于陆地上的差异(0.1 mm/d).微波发射信号(SSM/I E)的反演结果对洋面降水的高估和地面雨量计的缺乏,是造成两种资料间差异的主要原因.分析结果还表明,洋面上GPCP降水相对于PR降水的最大概率差异随雨强增大呈线性增大;陆地上这种差异则呈非线性关系.文中最后还利用最大概率函数对1979～1997年GPCP气候平均降水的误差进行了分析.     

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

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登陆台风边界层风廓线特征的地基雷达观测

为了分析登陆台风边界层风廓线特征,利用2004—2013年中国东南沿海新一代多普勒天气雷达收集的17个登陆台风资料,采用飓风速度体积分析方法,反演登陆台风的边界层风场结构特征。与探空观测对比表明,利用雷达径向风场可以准确地反演登陆台风的边界层风场结构,其风速误差小于2 m/s,风向误差小于5°。所有登陆台风合成的边界层风廓线显示,在近地层(100 m)以上,边界层风廓线存在类似急流的最大切向风,其高度均在1 km以上,显著高于大西洋观测到的飓风边界层急流高度(低于1 km)。陆地边界层内低层入流强度也明显大于过去海上观测,这主要是由陆地上摩擦增大引起。越靠近台风中心,边界层风廓线离散度越大,其中,径向风廓线比全风速以及切向风廓线离散度更大。将风廓线相对台风移动方向分为4个象限,分析边界层风廓线非对称特征显示,台风移动前侧入流层明显高于移动后侧。最大切向风位于台风移动左后侧,而台风右后侧没有显著的急流特征,与过去理想模拟的海陆差异导致的台风非对称分布特征一致。