基于DVB-S数据共享平台的NOAA/ATOVS资料获取、处理与显示系统

充分利用国家科技部大力推行的DVB-S共享数据平台, 加强气象卫星遥感数据的广泛应用, 特别是发挥卫星垂直探测器 (ATOVS) 资料在我国数值天气预报以及监测重大灾害性天气系统中的作用。该文介绍了基于DVB-S系统的NOAA/ATOVS资料的处理、分析与显示系统的概况及主要功能, 并以2005年7月人们关注的台风“海棠”为个例, 展示了利用该系统在监测和分析台风或强对流天气时的独特优势。该系统的建立, 将解决省、地气象部门不能实时获取ATOVS资料的问题, 并将推动ATOVS资料在气象以及相关部门的实际应用。     

基于DVB-S数据共享平台的NOAA/ATOVS资料获取、处理与显示系统

充分利用国家科技部大力推行的DVB-S共享数据平台,加强气象卫星遥感数据的广泛应用,特别是发挥卫星垂直探测器(ATOVS)资料在我国数值天气预报以及监测重大灾害性天气系统中的作用。该文介绍了基于DVB-S系统的NOAA/ATOVS资料的处理、分析与显示系统的概况及主要功能,并以2005年7月人们关注的台风“海棠”为个例,展示了利用该系统在监测和分析台风或强对流天气时的独特优势。该系统的建立,将解决省、地气象部门不能实时获取ATOVS资料的问题,并将推动ATOVS资料在气象以及相关部门的实际应用。     

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%.     

利用神经网络从118.75 GHz附近通道亮温反演大气温度

为了准确快速地从118.75 GHz附近六通道亮温计算大气温度,作者开展了利用人工神经网络技术反演大气温度的数值模拟研究。与线性统计反演算法比较,海面上大气温度反演的总体均方根误差减小17%,陆面上大气温度反演的总体均方根误差减小15%。两种下垫面条件下的温度反演结果表明,近陆面的温度反演结果优于近海面的温度反演结果。另外,对温度廓线垂直结构反演性能的分析结果表明,对于具有较厚逆温层结构的温度廓线,神经网络反演对廓线的复现能力优于线性统计反演。     

Uncertainty of AMSU-A derived temperature trends in relationship with clouds and precipitation over ocean

Microwave Sounding Unit (MSU) and Advanced Microwave Sounding Unit-A (AMSU-A) observations from a series of National Oceanic and Atmospheric Administration satellites have been extensively utilized for estimating the atmospheric temperature trend. For a given atmospheric temperature condition, the emission and scattering of clouds and precipitation modulate MSU and AMSU-A brightness temperatures. In this study, the effects of the radiation from clouds and precipitation on AMSU-A derived atmospheric temperature trend are assessed using the information from AMSU-A window channels. It is shown that the global mean temperature in the low and middle troposphere has a larger warming rate (about 20–30 % higher) when the cloud-affected radiances are removed from AMSU-A data. It is also shown that the inclusion of cloud-affected radiances in the trend analysis can significantly offset the stratospheric cooling represented by AMSU-A channel 9 over the middle and high latitudes of Northern Hemisphere.     

Influence of Surface Temperature and Emissivity on AMSU-A Assimilation over Land

AMSU-A (Advanced Microwave Sounding Unit-A) measurements for channels that are sensitive to the surface over land have not been widely assimilated into numerical weather prediction (NWP) models due to complicated land surface features. In this paper, the impact of AMSU-A assimilation over land in Southwest Asia is investigated with the Weather Research and Forecasting (WRF) model. Four radiance assimilation experiments with different land-surface schemes are designed, then compared and verified against radiosonde observations and global analyses. Besides the surface emissivity calculated from the emissivity model and surface temperature from the background field in current WRF variational data assimilation (WRF-VAR) system, the surface parameters from the operational Microwave Surface and Precipitation Products System (MSPPS) are introduced to understand the influence of surface parameters on AMSU-A assimilation over land. The sensitivity of simulated brightness temperatures to different surface configurations shows that using MSPPS surface alternatives significantly improves the simulation with reduced root mean square error (RMSE) and allows more observations to be assimilated. Verifications of 24-h temperature forecasts from experiments against radiosonde observations and National Centers for Environmental Prediction (NCEP) global analyses show that the experiments using MSPPS surface alternatives generate positive impact on forecast temperatures at lower atmospheric layers, especially at 850 hPa. The spatial distribution of RMSE for forecast temperature validation indicates that the experiments using MSPPS surface temperature obviously improve forecast temperatures in the mountain areas. The preliminary study indicates that using proper surface temperature is important when assimilating lower sounding channels of AMSU-A over land.     

Development and initial assessment of a new land index for microwave humidity sounder cloud detection

This paper describes a new quality control (QC) scheme for microwave humidity sounder (MHS) data assimilation. It consists of a cloud detection step and an O–B (i.e., differences of brightness temperatures between observations and model simulations) check. Over ocean, cloud detection can be carried out based on two MHS window channels and two Advanced Microwave Sounding Unit-A (AMSU-A) window channels, which can be used for obtaining cloud ice water path (IWP) and liquid water path (LWP), respectively. Over land, cloud detection of microwave data becomes much more challenging due to a much larger emission contribution from land surface than that from cloud. The current MHS cloud detection over land employs an O–B based method, which could fail to identify cloudy radiances when there is mismatch between actual clouds and model clouds. In this study, a new MHS observation based index is developed for identifying MHS cloudy radiances over land. The new land index for cloud detection exploits the large variability of brightness temperature observations among MHS channels over different clouds. It is shown that those MHS cloudy radiances that were otherwise missed by the current O–B based QC method can be successfully identified by the new land index. An O–B check can then be employed to the remaining data after cloud detection to remove additional outliers with model simulations deviated greatly from observations. It is shown that MHS channel correlations are significantly reduced by the newly proposed QC scheme.     

Comparison between linear and nonlinear trends in NOAA-15 AMSU-A brightness temperatures during 1998–2010

Brightness temperature observations from Microwave Sounding Unit and Advanced Microwave Sounding Unit-A (AMSU-A) on board National Oceanic and Atmospheric Administration (NOAA) satellites have been widely utilized for estimating the global climate trend in the troposphere and stratosphere. A common approach for deriving the trend is?linear regression, which implicitly assumes the trend being a straight line over the whole length of a time series and is often highly sensitive to the data record length. This study explores a new adaptive and temporally local data analysis method—Ensemble Empirical Mode Decomposition (EEMD)—for estimating the global trends. In EEMD, a non-stationary time series is decomposed adaptively and locally into a sequence of amplitude-frequency modulated oscillatory components and a time-varying trend. The AMSU-A data from the NOAA-15 satellite over the time period from October 26, 1998 to August 7, 2010 are employed for this study. Using data over Amazon rainforest areas, it is shown that channel 3 is least sensitive to the orbital drift among four AMSU-A surface sensitive channels. The decadal trends of AMSU-A channel 3 and other eight channels in the troposphere and stratosphere are deduced and compared using both methods. It is shown that the decadal climate trends of most AMSU-A channels are nonlinear except for channels 3–4 in Northern Hemisphere only and channels 12–13. Although the decadal trend variation of the global average brightness temperature is no more than 0.2?K, the regional decadal trend variation could be more (less) than 3?K (?3?K) in high latitudes and over high terrains.     

利用AMSU分析热带气旋结构特征

搭载在美国新一代极轨业务系列气象卫星上的先进的微波探测器 (Advanced Microwave Sounding Unit , AMSU) 提供了对于大气中温度、湿度以及云雨分布特征的探测能力。 研究选择 2003 年发生在西北太平洋上的多个热带气旋个例, 利用 NOAA16/17 卫星的 AMSU 数据分析热带气旋热力及云雨结构特征, 结果显示: 热带气旋中心的增暖在 AMSU-A 微波温度观测表现显著, 特别是在对流层上层通道尤其明显; AMSU 观测热带气旋中心增暖与强度相关性统计分析显示, 两者相关性达 0.778; AMSU-B 高频通道可以揭示热带气旋的云雨结构分布和对流发展旺盛情况, 分析显示热带气旋云雨结构变化与气旋强度密切相关, 气旋强度滞后于系统对流过程的发展 。     

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。      

应用先进微波探测器AMSU资料遥感反演春季陆地表层湿度

利用先进微波探测器(AMSU)正演快速微波辐射传输模型,完成了AMSU 窗区通道亮温与地表温湿特征的相关性分析,根据模拟计算结果改进了现有业务上反演地表微波辐射率的统计分析方法,得到适用于我国非冻土积雪覆盖地区地表微波辐射率反演的指数分析模型;进一步通过地表微波辐射传输模型迭代反演得到土壤体积含水量信息.2001年相互匹配的AMSU资料和地面农业气象观测站地表相对湿度观测结果的对比分析表明二者间具有一定的相关性.利用2002年3月和2003年3月的AMSU资料,反演了我国陆地区域地表湿度;连续两年春季地表湿度反演结果的对比分析表明,与2002年春季相比,2003年我国北方沙尘暴发生源区地表湿度反演值普遍偏高,潮湿的下垫面特征与沙尘暴发生频次的减少之间有一定的对应关系.试验结果表明,利用AMSU遥感资料可以获取大范围陆地表层湿度信息,进行区域尺度陆地表层湿度特征的动态分析,为我国沙尘暴监测分析提供陆地表层湿度基础信息.     

七通道微波辐射计遥感大气温度廓线的性能分析

文中首先分析了大气参数分布的垂直分辨率对模拟七通道微波温度探测器各通道亮温的影响,通过对比计算表明,大气参数的垂直分辨率对微波测温通道亮温计算的影响为0．2K～0．7K;随后分析了各通道亮温测量误差对温度廓线反演性能的影响,当测量误差在0．2K～2．0K范围内变化时,反演结果的总体均方根误差为2．1K～3．1K;同时还分析了单通道缺损、单通道发生漂移及所有通道发生漂移等情况对温度廓线反演性能的影响。当仪器缺损一个通道时,对所缺通道权重函数峰值附近高度上的大气温度反演有不同程度的影响,对其他高度上的大气温度反演的影响可忽略;单通道漂移0．5K时,对反演结果的影响较小;各通道整体漂移达到1．0K时,对温度廓线反演有明显影响;最后,考虑了云雾对微波测温通道亮温的影响,数值计算表明,雾层、浓霾和卷云对各通道亮温几乎没有影响,层云和积云对50．50GHz通道亮温有明显的影响。     

GMS-5 估计可降水量的研究

文章证明了由静止气象卫星GMS-5的分裂窗通道和水汽通道亮温反演可降水量的可行性,探讨了GMS-5红外通道亮温与可降水量的关系,建立了由3个通道亮温反演可降水量的经验公式。用60组大气平均廓线,对公式模拟检验误差为0.18 g/cm2,而用实际124组探空和对应的GMS-5亮温资料进行检验,误差0.40 g/cm2。用得到的经验公式可反演大范围的晴空可降水量分布。     

利用NOAA/AMSU资料和GRAPES同化系统对陆面比辐射率计算的改进研究

地表比辐射率计算的不确定性,直接影响到卫星资料在数值预报中同化应用的效果。本文采用美国NOAA/NESDIS的Weng等[1,2]提出的复杂陆面比辐射率模式,同时用NOAA卫星AMSU-ACh1或Ch2反演的地表比辐射率来调整该模式所需的地表参数,从而在缺少详细地表参数的情况下,改进AMSU-A Ch3和Ch15的地表比辐射率计算精度。在积雪地表情况下,用NOAA卫星AMSU-A资料直接反演各通道的比辐射率,在GRAPES同化系统中的应用表明,结果有明显的改进。     

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。      

Application of ATOVS microwave radiance assimilation to rainfall prediction in Summer 2004

Experiments are performed in this paper to understand the influence of satellite radiance data on the initial field of a numerical prediction system and rainfall prediction. First, Advanced Microwave Sounder Unit A （AMSU-A） and Unit B （AMSU-B） radiance data are directly used by three-dimensional variational data assimilation to improve the background field of the numerical model. Then, the detailed effect of the radiance data on the background field is analyzed. Secondly, the background field, which is formed by application of Advanced Television and Infrared Observation Satellite Operational Vertical Sounder （ATOVS） microwave radiance assimilation, is employed to simulate some heavy rainfall cases. The experiment results show that the assimilation of AMSU-A （B） microwave radiance data has a certain impact on the geopotential height, temperature, relative humidity and flow fields. And the impacts on the background field are mostly similar in the different months in summer. The heavy rainfall experiments reveal that the application of AMSU-A （B） microwave radiance data can improve the rainfall prediction significantly. In particular, the AMSU-A radiance data can significantly enhance the prediction of rainfall above 10 mm within 48 h, and the AMSU-B radiance data can improve the prediction of rainfall above 50 mm within 24 h. The present study confirms that the direct assimilation of satellite radiance data is an effective way to improve the prediction of heavy rainfall in the summer in China.     

A Neural Network Based Single Footprint Temperature Retrieval for AtmosphericInfrared Sounder Measurements and Its Application to Study onStratospheric Gravity Wave

Satellite hyperspectral infrared sounder measurements have better horizontal resolution than other sounding techniques as it boasts the stratospheric gravity wave(GW) analysis. To accurately and efficiently derive the three-dimensional structure of the stratospheric GWs from the single-field-of-view(SFOV) Atmospheric Infra Red Sounder(AIRS) observations, this paper firstly focuses on the retrieval of the atmospheric temperature profiles in the altitude range of 20-60 km with an artificial neural...     

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.     

地基微波辐射计资料在降水分析中的应用

为了提升国产地基微波辐射计反演大气温湿廓线的精度,增强本地部署设备的观测性能,研究实现了地基微波辐射计的神经网络直接样本反演法和观测亮温预处理的神经网络间接样本反演法。将算法应用于武汉华梦科技有限公司研制的HRA002型国产地基微波辐射计,在武汉国家基本气象站开展了与探空以及美国3台MP-3000A微波辐射计的对比观测试验。试验结果显示,HRA002直接样本反演采用改进网络反演水汽密度、相对湿度均方差分别降低约0.94 g·m^-3、5%;观测亮温经过预处理后与模拟亮温的相关性提升明显,预处理前后反演的低层温度、水汽密度和相对湿度与探空观测的均方差分别从2.4 K、3.26 g·m^-3和18.79%改善为1.58 K、2.18 g·m^-3和14.55%,略高于直接样本反演;与3台MP-3000A的反演结果相比,HRA002采用直接样本反演方法的温度廓线总体优于MP-3000A,HRA002采用间接样本反演方法的水汽密度和相对湿度总体上平均偏差占优而均方差稍逊。研究结果表明改进后的直接样本反演法更贴合辐射计硬件性能,反演精度较高;亮温预处理显著提升了间接样本反演精度,在反演精度总体接近的情况下,弥补了直接样本反演法需要长期观测数据的缺陷;综合采用上述两种算法能够提升国产地基微波辐射计本地化、个体化的观测性能,在反演大气参量廓线方面具有可用性。

     

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。