基于FY-3/IRAS利用非线性模式反演OLR

FY-3系列卫星星载IRAS仪器设有26个通道,其中20个通道用于探测地球大气在红外波段的热辐射,通道辐射率代表了地球大气系统在大气顶的向外辐射光谱信息,与总波段的射出长波辐射（OLR）通量相关性高。该文基于逐线辐射传输模式计算软件LBLRTM对全球2521条大气廓线的大气顶射出辐射率模拟数据,计算了每条廓线的OLR和FY-3B/IRAS,FY-3C/IRAS通道辐射率,用统计回归方法建立了利用IRAS的多通道辐射率计算OLR的非线性理论回归模式;应用模式和FY-3B/IRAS,FY-3C/IRAS的L1级数据,处理得到2016年4月1-30日的全球日平均、月平均OLR格点产品。与Aqua/CERES,Terra/CERES仪器宽波段观测OLR产品对比表明:对于水平分辨率为1°×1°的全球月平均OLR格点产品,均方根误差为2.22 W·m-2,相关系数为0.9982 W·m-2,平均偏差为-0.2 W·m-2,表明FY-3/IRAS仪器定标及反演模式均达到较高水平。文中还回顾了历史上不同气象卫星的多种OLR反演算法模式,并对不同模式精度进行了比较。     

高分五号热红外传感器多通道SST反演

2018-05高分五号（GF-5）卫星发射升空,其上搭载的全谱段成像仪在热红外8—13 μm谱段范围内具有4个温度反演通道（B09,B10,B11,B12）,空间分辨率设计优于40 m,在国内民用传感器领域实现了由单通道向多通道、中空间分辨率向高空间分辨率的跨越式突破,使得GF-5卫星热红外数据在地表热环境遥感领域具有极其重要应用价值。本研究基于GF-5的4个热红外通道的通道响应函数,利用全球742条TIGR（Thermodynamic Initial Guess Retrieval）探空廓线数据,进行不同观测角度、水汽含量和海表发射率条件下的MODTRAN4.0（Moderate resolution atmospheric Transmittance and Radiance code4.0)辐射传输过程模拟,基于模拟结果分别对两通道、三通道和四通道劈窗算法海表温度SST（ Sea Surface Temperature）反演系数进行修订,并分析观测角度、水汽含量和海表发射率对不同通道组合的精度影响,并通过GF-5卫星实际反演的SST结果进行验证。GF-5全谱段成像仪SST反演两通道劈窗算法组合共有6种,即B09-B10、B09-B11、B09-B12、B10-B11、B10-B12、B11-B12;三通道劈窗算法组合共有4种,即B09-B10-B11、B09-B10-B12、B09-B11-B12、B10-B11-B12;四通道劈窗算法组合1种,即B09-B10-B11-B12。通过对不同通道组合形式研究发现,水汽含量对SST反演精度有较大的影响,且温度反演的精度随着水汽含量的增加而降低;其次是观测角度,SST反演精度随着观测天顶角的增大而降低;最后是发射率的影响,两通道、三通道和四通道劈窗算法SST反演精度随着发射率的变化总体在0.1 K以内变化。最后以大亚湾核电站周围海域为验证区,用GF-5热红外遥感影像进行SST的反演并做误差分析,结果表明B09-B10通道SST反演实际误差为0.57 K,反演精度较高,实际误差与理论模拟误差相差0.24 K,差异的来源主要包括辐射定标和传感器噪声等要素影响,其他通道形式反演精度有待于传感器响应稳定后进一步验证。     

风云三号 (02) 批卫星微波氧气吸收通道降水特性

我国新一代极轨业务气象卫星风云三号 (02) 批计划2012年发射。该文利用UWNMS模拟2005年Katrina飓风的结果作为基础数据集,借助VDISORT微波辐射传输模式对风云三号 (02) 批计划装载的微波探测仪器中50~60 GHz和新增的118.75 GHz频点的降水特性进行初步研究。首先通过晴空权重函数匹配,选择出50~60 GHz与118.75 GHz频点匹配关系较好的4对通道。敏感性分析表明:各通道对各种水凝物粒子均很敏感,可用于改进现有业务降水反演算法。分别选取50~60 GHz 4个通道、118.75 GHz 4个通道、50~60 GHz及118.75 GHz全部通道3种不同的通道组合进行反演试验。结果表明:将50~60 GHz及118.75 GHz通道联合起来进行降水反演可提高降水反演的精度,并可以更好地区分降水区与非降水区。     

RPG-HATPRO-G3地基微波辐射计反演产品评估

RPG-HATPRO-G3地基微波辐射计采用“多通道并行测量技术”,性能稳定,反演精度高。应用该微波辐射计和常规L波段探空数据,比较了微波辐射计反演数据与探空测值的差异。结果表明：温度、水汽密度与常规探空资料比较有很好的线性相关性,对仪器维护后可以提高数据质量;相对湿度数据离散度较高。比较无降水时不同高度下微波辐射计反演数据的精度,温度在1 000 m以下低层平均误差和均方根误差较小;水汽密度的平均误差和均方根误差均为近地面较大,随高度而减小;相对湿度的平均误差和均方根误差都明显较大,温度和水汽密度的准确性高于相对湿度。降水时不同高度的温度、水汽密度和相对湿度的平均误差和均方根误差变化趋势均与无降水时相似,但是误差值明显偏大;降水时反演温度在2 000 m以下误差较小,水汽密度在3 000 m以下反演值较探空测值大,相对湿度在降水时的误差较大。     

卫星遥感反演土壤水分研究综述

土壤水分是影响地表过程的核心变量之一。精准地测量土壤水分及其时空分布,长期以来是定量遥感研究领域的难点问题。简要回顾基于光学、被动微波、主动微波和多传感器联合反演等卫星遥感反演土壤水分的主要反演算法、存在的难点和前沿性研究问题,介绍了应用土壤水分反演算法所形成的3种主要全球土壤水分数据集,包括欧洲气象业务卫星（ERS/MetOp）数据集、高级微波扫描辐射计（AMSR-E）数据集、土壤湿度与海洋盐分卫星（SMOS）数据集,并结合目前存在的问题探讨卫星遥感反演土壤水分研究的发展趋势。     

用多角度遥感数据反演混合像元组分温度的可行性分析

利用多通道信息反演陆面温度的传统方法,由于通道间信息的高度相关使反演结果的精度难以提高,并且无法得到混合像元中的组分温度。文中以非同温混合像元热红外辐射方向性规律为基础,建立了连续植被类型非同温混合像元热红外辐射模型,用蒙特卡罗方法模拟分析了连续植被的组分有效比辐射率与变量的关系。结果表明:连续植被热辐射亮度值是组分温度T_v、T_s,叶面积指数LAI,叶倾角分布LAD,以及单叶面比辐射率ε_v(θ)和土壤表面比辐射率ε_v(θ)的函数。利用先验知识对变量进行分析后表明,6个变量中只有T_v、T_s、LAI、ε_v(0)需要反演。由于多角度间组分辐射亮度的相关性低,从理论上讲只需要4个角度的辐射亮度观测值就可以解出4个未知量而达到反演组分温度的目的。这4个角度数据除选择垂直方向上辐射亮度数据外,应在30°～50°视角范围内选择另外3个热辐射亮度数据。     

从NOAA卫星AVHRR资料反演中国区域地表反照率

由NOAA卫星AVHRR短波通道1、2反射率反演地表反照率需要3个反演模式,分别是窄-宽波段反射率转换模式、大气顶双向反射模式、大气订正模式.基于模式和国家卫星气象中心接收处理的NOAA-18 AVHRR1B数据,处理了2006年1月至2010年12月的中国区域地表反照率,由于云的影响,15天合成技术用来形成周期为15天的地表反照率数据文件.2006年、2010年2年的处理结果与MODIS同类产品对比,RMS为0.028～0.074、相关系数为0.76～0.93,误差较大出现在冬季,原因是两者15天合成方法不同;5年的日平均地表反照率与21个中国地面气象一级辐射站的观测测值作对比,结果是：RMS为0.053、相关系数为0.88.反演模式系统误差以及云和气溶胶影响是卫星反演地表反照率的主要误差来源.     

Ensemble retrieval of atmospheric temperature profiles from AIRS

ABSTRACT Satellite-based observations provide great opportunities for improving weather forecasting. Physical retrieval of atmo spheric profiles from satellite observations is sensitive to the uncertainty of the first guess and other factors. In order to improve the accuracy of the physical retrieval, an ensemble methodology was developed with an emphasis on perturbing the first guess. In the methodology, a normal probability density function （PDF） is used to select the optimal profile from the ensemble retrievals. The ensemble retrieval algorithm contains four steps： （1） regression retrieval for original first guess; （2） perturbation of the original first guess to generate new first guesses （ensemble first guesses）; （3） using the ensemble first guesses and nonlinear iterative physical retrieval to generate ensemble physical results; and （4） the final optimal profile is selected from the ensemble physical results by using PDE Temperature eigenvectors （EVs） were used to generate the pertur- bation and generate the ensemble first guess. Compared with the regular temperature profile retrievals from the Atmospheric InfraRed Sounder （AIRS）, the ensemble retrievals RMSE of temperature profiles selected by the PDF was reduced between 150 and 320 hPa and below 400 hPa, with a maximum improvement of 0.3 K at 400 hPa. The bias was also reduced in many layers, with a maximum improvement of 0.69 K at 460 hPa. The combined optimal （CombOpt） profile and a mean optimal （MeanOpt） profile of all ensemble physical results were improved below 150 hPa. The MeanOpt profile was better than the CombOpt profile, and was regarded as the final optimal （FinOpt） profile. This study lays the foundation for improving temperature retrievals from hyper-spectral infrared radiance measurements.     

Raindrop Size Distribution Parameters Retrieved from Xinfeng C-Band Polarimetric Radar Observations

The objective of this research was to acquire a raindrop size distribution (DSDs) retrieved from C-band polarimetric radar observations scheme for the first time in south China.An observation period of the precipitation process was selected,and the shape-slope (μ-Λ) relationship of this region was statistically analyzed using the raindrop sample observations from the two-dimensional video disdrometer (2DVD) at Xinfeng Station,Guangdong Province.Simulated data of the C-band polarimetric radar reflectivity Z_(HH)and differential reflectivity Z_(DR)were obtained through scattering simulation.The simulation data were combined with DSD fitting to determine the Z_(DR)-Λand log_(10)(Z_(HH)/N_0)-Λrelationships.Using Xinfeng C-band polarimetric radar observations Z_(DR)and Z_(HH),the raindrop Gamma size distribution parametersμ,Λ,and N_0were retrieved.A scheme for using C-band polarimetric radar to retrieve the DSDs was developed.This research revealed that during precipitation process,the DSDs obtained using the C-band polarimetric radar retrieval scheme are similar to the 2DVD observations,the precipitation characteristics of rainfall intensity (R),mass-weighted mean diameter (D_m) and intercept parameter (N_w) with time obtained by radar retrieval are basically consistent with the observational results of the 2DVD.This scheme establishes the relationship between the observations of the C-band polarimetric radar and the physical quantities of the numerical model.This method not only can test the prediction of the model data assimilation system on the convective scale and determine error sources,but also can improve the microphysical precipitation processes analysis and radar quantitative precipitation estimation.The present research will facilitate radar data assimilation in the future.     

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.