我国月晴空指数模型探讨

运用统计方法,建立了不同时空尺度的数字月晴空指数估算模式DCIM。利用克立克(Kriging)插值法,制作了我国1 km×1 km全年各月气候平均晴空指数空间分布图。结果表明:分布图能较好反映我国晴空指数的分布特征,同时为实际复杂地形下太阳辐射及其各分量的估算提供了基础数据,也为其他大气参数的空间扩展提供了有益的尝试。     

利用遥感估算区域气温评价站点代表性——以藏东南林芝站点为例

遥感地表温度(Land Surface Temperature,LST)产品时序数据可用于分析基准气候站点的环境代表性,但LST空间变异性明显高于气温,容易低估站点代表性,因此提出首先建立面上气温的遥感估算模型,再用模型估算气温(和LST产品具有相同时空分辨率)评估站点代表性的方案。以藏东南林芝国家基准气候站点为例,首先提取气象站点2000-2011年8 d平均日最高气温(T-air)和MODIS地表温度产品(MOD11A2,8 d合成,1 km空间分辨率),分析LST与T-air之间的相关性以及其他因素对相关性的影响;而后利用Cubist回归树算法,建立了基于LST、日序、晴空日数的T-air估算模型(RMSE=1.4718℃,r~2=0.95);最终将模型用于林芝站周围附近区域计算T-air,并对林芝站点代表性加以评估,结果表明,新方案得到的结果更加可信,林芝站点代表范围为15 km×15 km,而基于LST计算得到的林芝站代表范围仅为3 km×3 km。     

基于GIS的巴西陆稻IAPAR-9种植气候区划研究

根据巴西陆稻IAPAR-9生长发育对气候条件的要求, 结合其在广西的多年引种试验结果, 分析确定了影响广西种植巴西陆稻的关键气候因子和气候区划指标, 采用GIS技术对区划指标进行小网格推算, 得出广西不同地理背景下1 km×1 km网格点上的有关气候要素值, 通过GIS的空间分析和多层复合方法, 对广西种植巴西陆稻进行气候区划, 并对区划结果评述和建议, 为广西发展巴西陆稻生产进行合理布局提供科学依据。     

基于FY-3D卫星的微波与光学陆表温度融合研究

目前还没有基于国产卫星的1 km分辨率的全天候陆表温度(LST)产品,FY-3D卫星提供了中分辨率成像仪(MERSI)Ⅱ型1 km分辨率晴空LST产品与微波成像仪(MWRI)25 km全天候LST产品,因此可结合两者优势开展全天候1 km分辨率LST的融合研究。基于地理加权回归(GWR)方法,选择海拔、FY-3D归一化植被指数和归一化建筑指数等建立GWR模型对FY-3D/MWRI 25 km LST降尺度到1 km,并与MERSI 1 km LST进行融合;同时针对MWRI轨道间隙,利用前后1天融合后的云覆盖像元1 km LST进行补值,可以得到接近全天候下的1 km LST。基于以上融合算法,选择了中国区域多个典型日期FY-3D/MERSI和MWRI LST官网产品进行了融合试验,并利用公开发布的全天候1 km LST产品(TPDC LST)对FY-3D1 km LST融合结果进行了评估。研究结果表明,基于GWR法的LST降尺度方法,可以有效避免传统微波LST降尺度方法中存在的“斑块”效应和局地温度偏低等问题;LST融合结果有值率从融合前的22.4%～36.9%可提高到融合后69.3...     

起伏地形下我国太阳散射辐射分布式模拟

基于1km×1km分辨率的数字高程模型(DEM)数据,考虑了地形因子对太阳散射辐射的影响,改进了开阔度的计算模型,确定了我国气候平均情况下月散射系数的空间分布,实现了实际起伏地形下我国太阳散射辐射的分布式模拟,计算了我国范围内1km×1km分辨率1-12月气候平均太阳散射辐射的空间分布.结果表明:局地地形对太阳散射辐射空间分布的影响比较明显;模拟结果可靠,可进行大数据量处理,适用于遥感图像处理、地理信息系统等数据处理平台.     

中国区域云特性分析及在FY-2云检测中的应用

云检测中所使用的云检测阈值正确与否是关系到云检测精度的重要因素。该文利用1983年7月-2007年6月ISCCP数据对覆盖我国及周边区域不同云类云顶温度的年、日变化特征进行分析, 得到云顶温度的分布特征; 对晴空下垫面与最暖云云顶温度差随纬度分布特征、不同区域晴空下垫面及云顶温度与晴空地表温度差日变化特征进行分析, 这些特征及每3 h的晴空下垫面24年平均亮温作为云检测算法的背景场, 用以判识实时动态提取云检测阈值的合理性。个例分析表明:利用多年平均晴空下垫面温度及最暖云云顶温度与晴空下垫面温度之差, 可有效识别云检测阈值的合理性, 合理的阈值有助于提高连续多日被云覆盖及冰雪下垫面条件下的云检测精度。     

起伏地形下我国太阳直接辐射的分布式模拟

运用数据集群技术,建立了我国不同时空尺度直接透射率的估算模式,对比分析了不同模式的拟合精度。基于1km×1km分辨率的数字高程模型(DEM)数据,全面考虑了地形因子对太阳直接辐射的影响,实现了实际起伏地形下我国太阳直接辐射的分布式模拟,计算了我国范围内1km×1km分辨率1—12月气候平均太阳直接辐射的空间分布。结果表明:局地地形对太阳直接辐射空间分布的影响非常强烈,尤其是在太阳高度角较低的冬季和秋季;模拟结果可靠,可进行大数据量处理,适用于遥感图像处理、地理信息系统等数据处理平台。     

贵溪市植被资源遥感调查和综合气候区划

应用中巴资源一号卫星遥感数据、地理信息系统和全球定位系统技术，结合常规资料、野外考察等手段，采用数字化图像处理和分类技术，进行了贵溪市土地利用类型、水资源分布和森林植被等环境资源遥感解译。在通用的遥感图像分类方法的基础上，将GPS采样数据参与到各类资源遥感解译标志的建立和分类中，并留出样本做精度检验，提高了环境资源遥感解译的客观性和效率。根据中国亚热带东部丘陵山区气候考察资料和贵溪所处武夷山区17年的垂直气候观测资料，应用统计学方法建立了12种气候要素和资源随海拔高度变化模型。应用1:25万数字化地理数据，经三角网插值，生成20 m网格精度的数字高程图（DEM），通过地学模型，将气候要素（资源）计算到同样的网格点上。最后，应用GIS中栅格图层的空间分析方法，将环境资源遥感解译结果、气候资源、区划指标等计算到20 m分辨率的网格点上，应用逻辑算子进行气候分区判断，得到了优质双季晚稻适宜播种期等7种作物的综合区划，并对区划结果进行了分析。     

基于MODIS云产品的AIRS云相态的识别和云量产品的检验

选用中国中东部(95～130 °E, 20～60 °N)午后同一时刻相对应的AIRS与MODIS各15景样本数据,将MODIS在红外波段上四种基于亮温的云识别算法对AIRS晴空像元和云像元中不同的云相态进行区分,分别统计出AIRS不同类型像元中这四种云识别算法的亮温差异,根据AIRS在晴空、水云和冰云三种不同相态下亮温的差异,确定出其阈值,实现AIRS晴空和云相态识别。在此基础上,对AIRS不同云相态下反演的有效云量进行检验和改进。结果表明:运用亮温阈值法对AIRS进行云相态识别能够较好地反映AIRS的晴空和云相态特征,云相态分布与MODIS云相态产品对应效果较好,特别在云边缘区域。对AIRS不同云相态下反演的有效云量进行对比和误差分析后发现:当水云有效云量较高、冰云有效云量较低时,AIRS反演的有效云量误差较大。在误差分析的基础上,提出了AIRS有效云量的偏差订正算法,对AIRS反演的有效云量具有一定改进,为AIRS云产品的应用提供参考依据。      

基于晴空阈值法的全天空红外图像云量计算

为提高新疆戈壁地区云的自动化观测水平,基于全天空红外成像仪(WSIRCMS)获取的红外辐射图像,利用辐射传输模式SBDART分析了仪器测量波段对有云无云状况的敏感性并构建了拟合方程,同时利用典型季节的晴空辐射样本拟合了晴空曲线并统计形成了晴空阈值,最后利用统计晴空阈值对全天空红外辐射图像进行云像素识别和总云量计算。将不同季节总云量计算结果同人工观测结果对比验证表明：观测时段算法计算总云量和人工观测总云量差值在±2成以下的概率均在80%以上,说明该方法具有较高的准确度和较强的实用性,在观测业务中具有较好的应用前景。     

GMS 5反演中国几类典型下垫面晴空地表温度的日变化及季节变化

文中利用单时相双光谱分裂窗算法以GMS 5/VISSR红外资料反演地表温度,揭示了中国几类典型下垫面晴空地表温度的日变化及季节变化特征.塔里木盆地、青藏高原、浑善达克沙地、华北平原北部、华南部分地区因地表反射率、土壤含水量、受太阳辐射影响程度不同等地表温度季节变化差异很大,月平均地表温度日较差一年内基本上呈双峰双谷型.作为比较,东亚部分陆地的地表温度与台湾海峡南部、黄海的海表温度及其日变化、季节变化一并进行了分析.塔里木盆地、浑善达克沙地不仅具有强烈的日变化,而且季节变化也显著.2000年两地月平均地表温度日较差最大值超过30 K,浑善达克沙地的年较差高达58.50 K.青藏高原地表温度季节变化小于东亚部分陆地、塔里木盆地、浑善达克沙地,但该区日变化幅度在所研究几个区中最大,2000年年平均日较差达28.05 K.文中将研究时段扩充到1998～2000年后揭示了连续三年地表温度及其日变化的年际变化特征.所获得这几类地表温度的变化特征与量值对于气候与辐射收支研究以及推测地表状况会有一定参考价值.     

A comparison of satellite-retrieved and simulated cloud coverage in the Baltic Sea area as part of the BALTIMOS project

A cloud-detection algorithm for METEOSAT first generation data has been developed. The algorithm utilizes solely infrared data from the METEOSAT thermal infrared window channel at around 11.5 μm. The developed algorithm estimates an assumed clear-sky brightness temperature from time series analysis on pixel bases. Land-/sea-depending dynamic thresholds are then utilized discriminating the infrared images in cloudy, undecided, and cloud free pixels. The cloud-detection algorithm has been validated against synoptic observations. The developed cloud-detection scheme has been applied to 10 years (1992–2001) of METEOSAT data, extracting cloud coverage statistics for the Baltic Sea catchment area. These have been compared to corresponding cloud coverage statistics derived from the BALTIMOS coupled model system. Building overall averaged values of the cloud coverage in the period from 1999 to 2001 gives results with very good agreement between simulation and observation: the total METEOSAT-derived cloud coverage amounts to 0.65 compared to 0.63 for BALTIMOS. In contrast, large discrepancies in the phase of the diurnal cycle of cloud coverage have been observed. A significant trend in total cloud amount was observed neither from the model nor from the satellite.     

基于亮温通道变率的FY-3C微波湿度计陆地云检测新方法

云检测是卫星资料同化的重要前处理步骤,无论是晴空资料同化还是有云资料同化,都需要准确地区分有云和晴空资料。由于陆地地表发射率的多变性和微波能穿透部分云类的特点,微波湿度计资料在陆地上空的云检测研究一直是难点。利用快速辐射传输模式（CRTM）分析了不同云类条件下FY-3C微波湿度计（MWHS-Ⅱ）各通道亮温的通道间变率特征,根据MWHS-Ⅱ亮温通道间变率随云高以及云中液态水含量的增大而减小的特点,提出了一个基于亮温通道变率的MWHS-Ⅱ陆地资料云检测方法。与已有的云产品比较结果表明:新的云检测算法能有效地剔除大部分受云影响的资料,剔除后的晴空资料观测和模拟偏差更好地符合高斯分布。新方法对过冷水云、冰云、重叠云的检测能力较强,正确检测率可以达到80%,对卷云以及高度较低的水云的检测能力相对较弱。新方法能有效利用MWHS-Ⅱ观测资料自身完成云检测,在MWHS-Ⅱ资料同化中有很好的应用前景。      

FY-3A陆表温度反演及高温天气过程动态监测

采用FY-3A/VIRR数据,利用Becker局地分裂窗改进算法反演得到逐日陆表温度 (LST), 对2009年一次高温天气过程进行动态监测, 并分析不同下垫面的热环境变化。结果显示:此过程中可见光红外扫描辐射计 (VIRR) 陆表温度产品在敦煌辐射校正场地两次验证的误差为-0.17 K和1.77 K,与同时间过境的MODIS产品均方根误差为2.64 K,直方图对比陆表温度的频数分布基本一致;对高温天气过程监测发现,此次出现以华北的石家庄、郑州、北京等地和西北地区东部的西安等地为中心的两个陆表温度高值区, 部分地区达到了320.2 K以上;城市剖面资料证实城市热岛现象存在,并发现工矿用地的热岛效应不容忽视,主要是大面积的工矿用地周围植被破坏严重,地表增温更为显著。     

基于AHI观测的全天气条件海表温度反演

基于搭载在日本新一代静止气象卫星Himawari-8上的先进葵花成像仪(Advanced Himawari Imager,AHI)观测资料,研究了高时空分辨率的、全天气条件的海表温度(Sea Surface Temperature,SST)反演算法。本算法包括两步:第一步,根据云检测算法划分晴空和云区,然后利用非线性SST(NLSST)方程由红外亮温估计晴空SST;第二步,在有云区,先由前5 d同一时刻的晴空SST进行初步补缺,然后再利用Barnes插值完善云区SST估计和进行异常点平滑。最终得到时间分辨率为10 min、空间分辨率为0.05°的全天气条件海温分布。利用移动浮标的观测SST验证,晴空区SST估计的均方根误差(Root Mean Square Error,RMSE)和平均误差(Mean Error,ME)分别为0.857 K和0.017 K。全天气条件SST估计的RMSE和ME分别为0.872 K和-0.005 K。     

水物质对云雨区卫星微波观测模拟影响

受云和降水影响卫星资料在数值天气预报中的同化应用对于进一步改善数值预报效果具有重要作用, 这部分工作的开展要求快速辐射传输模式中能够较好地考虑各种水物质的辐射效应。使用美国卫星资料同化联合中心新近开发的快速辐射传输模式CRTM, 通过中尺度数值模式WRF的预报输出提供水物质输入, 分析水物质辐射效应对云雨区卫星微波观测模拟计算的影响。在WRF模式预报水物质的分布和天气系统配置合理并符合云物理基本特征的前提下, 水物质辐射效应的考虑极大改善了卫星观测模拟的效果。结合卫星各通道探测特性, 进一步分析各种水物质粒子对NOAA-16 AMSU A/B各通道卫星亮温模拟的影响和物理机理, 定量统计各类水物质对各卫星观测通道亮温计算偏差和偏差贡献的权重大小。分析结果表明:快速辐射传输模式中, 考虑水物质辐射效应为数值天气预报中云雨区卫星资料的同化应用提供了必需的基础条件。     

Assessing Drought Conditions in Cloudy Regions Using Reconstructed Land Surface Temperature

Temperature vegetation dryness index (TVDI) in a triangular or trapezoidal feature space can be calculated from the land surface temperature (LST) and normalized difference vegetation index (NDVI), and has been widely applied to regional drought monitoring. However, thermal infrared sensors cannot penetrate clouds to detect surface information of sub-cloud pixels. In cloudy areas, LST data include a large number of cloudy pixels, seriously degrading the spatial and temporal continuity of drought monitoring. In this paper, the Remotely Sensed Daily Land Surface Temperature Reconstruction model (RSDAST) is combined with the LST reconstructed (RLST) by the RSDAST and applied to drought monitoring in a cloudy area. The drought monitoring capability of the reconstructed temperature vegetation drought index (RTVDI) under cloudy conditions is evaluated by comparing the correlation between land surface observations for soil moisture and the TVDI before and after surface temperature reconstruction. Results show that the effective duration and area of the RTVDI in the study area were larger than those of the original TVDI (OTVDI) in 2011. In addition, RLST/NDVI scatter plots cover a wide range of values, with the fitted dry-wet boundaries more representative of real soil moisture conditions. Under continuously cloudy conditions, the OTVDI inverted from the original LST (OLST) loses its drought monitoring capability, whereas RTVDI can completely and accurately reconstruct surface moisture conditions across the entire study area. The correlation between TVDI and soil moisture is stronger for RTVDI (R = −0.45) than that for OTVDI (R = −0.33). In terms of the spatial and temporal distributions, the R value for correlation between RTVDI and soil moisture was higher than that for OTVDI. Hence, in continuously cloudy areas, RTVDI not only expands drought monitoring capability in time and space, but also improves the accuracy of surface soil moisture monitoring and enhances the applicability and reliability of thermal infrared data under extreme conditions.     

Diurnal and Seasonal Variation of Clear-Sky Land Surface Temperature of Several Representative Land Surface Types in China Retrieved by GMS-5

The retrieved results in this paper by GMS-5/VISSR thermal infrared data with single time/dual channel Split-Window Algorithm reveal the characteristics of diurnal and seasonal variation of clear-sky land surface temperature (LST) of several representative land surface types in China,including Tarim Basin,Qinghai- Tibetan Plateau,Hunshandake Sands,North China Plain,and South China.The seasonal variation of clear-sky LST in above areas varies distinctly for the different surface albedo,soil water content,and the extent of influence by solar radiation.The monthly average diurnal ranges of LST have two peaks and two valleys in one year.The characteristics of LST in most land of East Asia and that of sea surface temperature (SST) in the south of Taiwan Strait and the Yellow Sea are also analyzed as comparison.Tarim Basin and Hunshandake Sands have not only considerable LST diurnal cycle but also remarkable seasonal variation. In 2000,the maximum monthly average diurnal ranges of LST in both areas are over 30 K,and the annual range in Hunshadake Sands reaches 58.50 K.Seasonal variation of LST in the Qinghai-Tibetan Plateau is less than those in East Asia,Tarim Basin,and Hunshandake Sands.However,the maximum diurnal range exists in this area.The yearly average diurnal range is 28.05 K in the Qinghai-Tibetan Plateau in 2000.The characteristics of diurnal,seasonal,and annual variation from 1998 to 2000 are also shown in this research. All the results will be valuable to the research of climate change,radiation balance,and estimation for the change of land surface types.     

A modified Becker’s split-window approach for retrieving land surface temperature from AVHRR and VIRR

In order to provide a long time-series,high spatial resolution,and high accuracy dataset of land surface temperature(LST) for climatic change research,a modified Becker and Li’s split-window approach is proposed in this paper to retrieve LST from the measurements of Advanced Very High Resolution Radiometer(AVHRR) onboard National Oceanic and Atmospheric Administration(NOAA)-7 to-18 and the Visible and InfraRed Radiometer(VIRR) onboard FY-3A.For this purpose,the Moderate Resolution Transmittance Model(MODTRAN) 4.1 was first employed to compute the spectral radiance at the top of atmosphere(TOA) under a variety of surface and atmosphere conditions.Then,a temperature dataset consists of boundary temperature T s(which is one of the input parameters to MODTRAN),and channels 4 and 5 brightness temperatures(T 4 and T 5) were constructed.Note that channels 4 and 5 brightness temperatures were simulated from the MODTRAN output spectral radiance by convolving them with the spectral response functions(SRFs) of channels 4 and 5 of AVHRRs and VIRR.The coefficients of modified Becker and Li’s split-window approach for various AVHRRs and VIRR were subsequently regressed based on this temperature dataset using the least square method.As an example of validation,one AVHRR satellite image over Beijing acquired at 0312 UTC 27 April 2008 by AVHRR onboard NOAA-17 was selected to retrieve the LST image using the modified Becker and Li’s approach.The comparison between this LST image and that from the MODIS level-2 LST product provided by the University of Tokyo in Japan indicates that the correlation coefficient is 0.88,the bias is 0.6 K,and the root mean square deviation(RMSD) is 2.1 K.Furthermore,about 70% and 37% pixels in the LST difference image,which is the result of retrieved LST image from AVHRR minus the corresponding MODIS LST image,have the values within ± 2 and ± 1 K,respectively.     

Evaluation of All-Sky Assimilation of FY-3C/MWHS-2 on Mei-yuPrecipitation Forecasts over the Yangtze-Huaihe River Basin

正1School of Atmospheric Sciences, Chengdu University of Information Technology, Chengdu 610025, China2International Center for Climate and Environment Sciences, Institute of Atmospheric Physics,Chinese Academy of Sciences, Beijing 100029, China3University of Chinese Academy of Sciences, Beijing 100049, China