PCT-SI综合指数法反演陆面雨强

利用TRMM微波成像仪(TMI)数据,采用极化订正温度及散射指数综合指数法(PCT-SI),反演赣州及周边地区2011年11月9日雨强,与同时期的PR星载雷达测雨资料进行对比分析,并用赣州地区47个地面站点的小时降雨对反演结果进行了验证。结果表明:采用K-均值法分类并判别降雨区,可以较好地确定降雨区的范围;TMI各通道亮温与对应时空匹配PR雨强的相关系数不同,利用低频组合拟合85 Hz通道亮温来求得大气散射指数,散射指数越大,雨强越大;PCT-SI综合指数法反演的降水中心的降水强度明显偏小,降水范围扩大,但其反演的雨强与PR反演的雨强基本一致,与地面实际雨量站点雨强相关系数为0.784,表明采用PCT-SI综合指数法反演陆面雨强是合理可信的。     

利用TRMM/TMI资料反演青藏高原中部土壤湿度

用辐射传输理论提出的地表微波辐射极化指数PI的定义,分别指出了PI对土壤湿度、地面粗糙度、植被层和大气层的影响。用热带降水测量(TRMM—Tropical Rainfall Measuring Mission)卫星上携带的微波辐射仪(TMI—TRMM Microwave Imager)的1B11的6年亮温数据,统计得到青藏高原中部地区PI值月平均分布。并用归一化距平,反演得到了该区域年、季以及干湿季土壤湿度变化的空间图像。结果表明,PI距平分布图可以很好地表征土壤湿度的变化,从而为大尺度评估高原土壤湿度变化提供了理论依据。另外,在同一时间段内,在已知区域平均PI值与平均土壤湿度的条件下,用归一化距平的方法可以定量反演该区域的土壤湿度。     

用TRMM资料研究江淮、华南降水的微波特性

热带测雨卫星TRMM (Tropical Rainfall Measuring Mission) 于1997年11月发射成功, 其首次携带了空载雷达, 有关资料已在网上对公众发布。利用热带测雨卫星上的微波成像仪TMI (TRMM Microw ave Imager) 资料以及其和测雨雷达TRMM/PR (Precipitation Radar) 资料联合反演的地面瞬时降水产品, 采用散射指数 (Is) 法从理论上探讨了我国江淮、华南降水尤其是暴雨的微波特性, 其中I_s表达式通过江淮、华南晴空TMI资料统计回归得到。以联合反演的地面瞬时降水产品为真值, 用面积相当法对14个降水个例求I_s降水阈值, 研究了阈值和降水面积以及85.5 GHz垂直通道最低亮温的关系, 并寻求了I_s和降水的相关特征。研究表明:I_s降水阈值随降水面积的增大或85.5 GHz垂直通道最低亮温的降低有增高的趋势; I_s与强对流性降水瞬时雨强对应很好, I_s≥60 K是一个好的暴雨指标。最后进行了初步的雨强反演试验研究, 由于TMI资料分辨率的提高以及时空配合较好的真值, 反演的地面瞬时降水与真值相关效果大大提高。     

校正热带测雨卫星轨道抬升对微波成像仪亮温的影响

热带测雨卫星(Tropical Rainfall Measuring Mission,TRMM)在2001年8月轨道高度从350 km升高至402 km,搭载于其上的微波成像仪(TRMM Microwave Imager,TMI)的入射角随之发生变化,进而使得相应探测结果(亮温)发生改变,从而导致由此反演的大气参数出现虚假的突变。为保证轨道抬升前后TMI亮温资料的连致性,以便更好地用于气候研究,本研究首先分析了洋面轨道抬升前后亮温的差异及变化原因,然后结合微波辐射传输模式,分析了不同环境参数对亮温变化的影响,在此基础上用线性变换的方式对轨道抬升后的亮温进行了修正,并从不同角度检验修正效果。结果表明,轨道抬升前后亮温呈线性关系,低频垂直极化通道亮温轨道抬升后升高了0.8-1.6 K,其他通道亮温变化不大。经过修正,轨道抬升前后的亮温趋于一致,月平均亮温偏差明显减小,低频垂直极化通道亮温在轨道抬升期间的突变被消除,亮温变得连续平稳,可用于气候研究。     

基于葵花-8卫星宁夏暴雨监测预警指标研究

基于葵花-8卫星红外通道资料和地面降水数据,对2017年5—9月宁夏暴雨过程进行云团识别、特征参数(云顶平均亮温、最低亮温、亮温梯度、冷云面积和降温率)计算及监测预警指标分析.结果表明:所选云团特征参数在不同类暴雨过程中有较明显的表现特征.暴雨发生时,云顶平均亮温和最低亮温分别介于213～228 K和199～227 K...     

内蒙古中西部地区一次沙尘暴天气的动力诊断

用多普勒天气雷达探测资料、地面沙尘暴探测资料、卫星图像和常规气象资料对2009年4月23日发生在内蒙古中西部地区一次大风、扬沙及沙尘暴天气过程进行了连续监测和分析。结果表明：高空槽发展加强,强锋区南压,地面冷锋影响,气压梯度大是造成沙尘暴的天气形势。红外图像形成＂沙尘羽＂,结构均匀有纹理,云顶亮温出现〈-60℃区域是卫...     

基于TRMM的热带气旋降水云系多谱段辐射特征分析

利用TRMM卫星测雨雷达、微波成像仪和可见光/红外扫描仪的探测资料,以2006年第8号台风"桑美"为例,研究了热带气旋在海面上强烈发展时期的降水云系多谱段辐射特征,研究结果表明:可见光资料反映了云的厚度信息,红外(亮温)资料反映云顶高度信息,可以较好地反映台风云系所表现台风的外观,但对台风云系覆盖下的细节特征的探测能力则有限.微波有很强的穿透性,能揭示台风云系中各种粒子的三维分布特征.微波通道亮温与PR降水的相关远大于可见光和红外遥感的效果.不论是可见光、红外还是微波亮温,与高层降水的相关系数绝对值总大于其与低层降水的相关系数.可见光和近红外波段的辐射率与降水正相关,相关系数随频率减小而减小;中红外和两个红外分裂窗区的亮温与降水呈负相关,相关系数的绝对值随频率减小略有增大.TMI低频通道的亮温与降水正相关,相关程度随频率减小略有减小;TMI高频通道的亮温与降水负相关,相关系数的绝对值随频率减小而减小.     

河南省非降水云中液态水的卫星微波反演试验研究

云中液态水分布对全球气候和局地天气变化有重要影响, 是判别人工影响天气作业潜力区的重要依据。利用TRMM卫星微波成像仪 (TMI) 85.5 GHz通道垂直极化亮温资料与NCEP再分析资料, 结合VDISORT模式采用逐步逼近方法反演了河南地区地表比辐射率; 再利用TRMM/TMI 85.5 GHz通道垂直极化亮温资料、TRMM/VIRS红外辐射资料及NCEP再分析资料, 结合VDISORT模式采用迭代的方法反演了河南地区云中液态水的垂直积分总含量。与红外卫星云图、TRMM卫星2A12产品及NCEP资料对比分析表明:该研究提出的反演陆地上空非降水云中液态水方法是可行的, 且对云中液态水垂直积分总含量水平分布的反演结果较对比产品结果更好。     

用TRMM卫星微波成像仪遥感云中液态水

应用热带降雨测量卫星微波成像仪(TRMM/TMI)的被动遥感资料,选用对云中液态水变化非常敏感的85.5 GHz垂直极化通道的亮温信息,通过离散纵坐标矢量辐射传输模式,采取逐步逼近的方法确定出地表的微波比辐射率,并运用迭代方法有效地反演出云中液态水含量及其分布.与对应的卫星红外云图对比结果表明,反演的云中液态水分布是合理和可信的.     

用华南暴雨试验雨量资料对TRMM/TMI-85.5GHz测雨能力的考察

利用华南暴雨试验期间稠密的雨量资料对热带测雨卫星（TRMM）微波成像仪（TMI）频率为85．5GHz（波长0．35cm）的测雨能力进行了考察。通过TMI－85．5GHz亮温分布和一小时雨量分布的对比发现，对流性降水的强雨量中心与TMI－85．5GHz亮温的低值区有很的对应关系，雨带分布和低亮温分布的位置及形状都很相似，雨强的大小和亮温的关系也相当密切：雨强越大，亮温值越低。亮温数值和雨强（指每小时雨量，下同）的相关统计进一步表明：雨强的大小与亮温呈明显的负相关，特别是当雨强达到或超达7mm/h时的相关程度非常显著，从而确认了TMI－85．5GHz的微波遥感对对流性强降水有较好的测雨能力。     

SATELLITE MICROWAVE REMOTE SENSING OF FLOODING

The ability of the Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI) for flooding and soil wetness detection has been demonstrated in this study.On the basis of TMI measurements,four methods,the classification method,the soil wetness index (SWI) method. the polarization difference index (PDI) method,and the polarization ratio index (PRI) method, were brought out to monitor flooding and study soil wetness in the Changjiang and Huaihe River Basins during the summer 1998.Compared with the images provided by L-band Synthetic Aperture Radar (L-SAR) and Radar Satellite (Radarsat) and the figures derived from daily rainfall data based on the Z-index method,the detection of flooding and soil wetness by TMI was proved to be feasible.     

用星载先进微波探测器（AMSU）资料开展区域地表洪涝分类监测

以微波辐射传输模式为基础，正演模拟分析星载先进微波探测器（AMSU）窗区通道微波亮温对地表洪涝信息的敏感性，结果表明中心频点分别为31.4、50.3和89.0 GHz的AMSU窗区通道2、3和15的微波亮温对地表洪涝特征敏感；以正演模拟计算结果为基础，选择对地表洪涝特征敏感的AMSU窗区通道为因子，组合形成AMSU洪涝指数AFI(AMSU Flooding Index)，进行地表洪涝区分类研究；地表洪涝分类结果与经机-地校验过的机载合成孔径雷达L-SAR资料分析结果进行对比分析，分类正确率可达73%。短时监测试验结果表明，应用美国第5代极轨业务环境卫星上装载的先进微波探测器（AMSU）微波资料可以在准全天候条件下，实现区域地表洪涝区分类实时监测，有效弥补红外和可见波段洪涝遥感探测技术在云天条件下无法实施的缺陷。     

Remote sensing land surface wetness by use of TRMM/TMI microwave data

Summary The water cycle analysis is the most important part of the GEWEX project. In the water cycle analysis, the land surface wetness information plays an important role. TRMM/TMI is a new kind of microwave image unit, and has great potential application in land characteristics analysis, especially in remote sensing of land surface wetness information and the monitoring of flood and drought situations. In this study, the wetness index analysis method was used to analysis surface wetness during the summer of 1998 over Boyang and Tongting lake area in China, and we retrieved the land surface emissivity over the same area to estimate the land surface wetness. To accomplish this, we have first studied the TRMM/TMI forward characteristics. By using the VIDSORT model, we developed wetness indexes BWI by combining three window channels of TRMM/TMI. According to our analysis results, the wetness BWI10 are better than the other indexes. So we use the best wetness indexes (BWI10) sensitive to the land surface wetness changes to do our flood classification and monitoring. In our calibration/validation test, the data from the China L-SAR (located on an airplane) and the Canadian Radar-SAR aboard on the Radarsat were used. At the same time we also have tried to retrieve the surface microwave emissivity from the TMI data. We use the emissivity product to estimate the land surface wetness, and we also got a good result. Future work will focus on investigating possible improvements to the algorithm and extending the testing of the algorithm to other regions. Received October 10, 2001 Revised December 18, 2001     

用TRMM/TMI估算HUBEX试验区的云中液态水

文中应用热带降雨测量卫星微波成像仪的微波遥感资料反演云中液态水。由于微波成像仪85.5 GHz通道对云中液态水非常敏感,通过离散纵坐标矢量辐射传输模式,运用迭代的方法可以有效地反演出陆地上空非降水云中的液态水路径。在淮河流域能量与水分循环试验中,分别运用微波成像仪85.5 GHz垂直极化单通道和微波成像仪85.5 GHz极化亮温差两种方法来估算陆地上空的云中液态水路径,反演结果与地基微波辐射计的测量结果是较为一致的。当地表比辐射率或地表温度误差较大时,用极化亮温差法估算云中液态水路径相对较好,尤其是对于低云,因为该方法对地表温度不敏感。     

Estimates of continental-scale soil wetness and comparison with the soil moisture data of Mintz and Serafini

 Soil wetness, in both its global distribution and the seasonal change, has been mainly estimated by the water balance approach using the bucket model which regards the soil wetness as soil moisture. The soil moisture data of Mintz and Serafini is one of the representatives examples, however, this method has problems since it does not incorporate the effects of flooding, snow accumulation on the ground, and so on. In this study, we use the Amazon and Volga river basin to carry out a case study to evaluate these problems. In the Amazon river basin, the annual range of the entire terrestrial water storage, about 400 mm, can be mainly explained by the rising and falling of the water level, and flooding around river channels, although soil moisture data of Mintz and Serafini is almost constant throughout the year. In the Volga river basin, snow accumulates on the ground producing 80 mm of water equivalent during winter, however the soil moisture data of Mintz and Serafini is almost saturated in winter. Received: 30 October 1996 / Accepted: 4 June 1997     

利用热带降雨测量卫星的微波成像仪观测资料反演陆地降水

利用热带降雨测量卫星的微波成像仪资料，结合淮河流域试验加密观测期的阜阳地面天 气雷达雨量资料，建立了以散射指数和极化订正温度为主要参数的降水反演算法。对文 中所做反演试验与日本ＮＡＳＤＡ用微波成像仪和星载测雨雷达反演的雨强进行了比较。结果表明 ，文中所用的方法在反演陆地下垫面的降雨强度的分布和降雨区域的确定是比较成功的。     

Temporal and spatial variability of dryness/wetness in China during the last 530 years

Summary The main characteristics of spatial and temporal variability of dryness and wetness during the last 530 years (1470–1999) are classified over five centuries. They have been investigated by using 100-site dryness/wetness index data that has recorded the historical weather conditions that affect agriculture and living conditions in eastern China. A set of principal modes of spatial variability and time coefficient series describing the dominant temporal variability are extracted by a diagnostic method, the rotated empirical orthogonal function (REOF) analysis. The long-term precipitation around Beijing, north China and the long-term runoffs in the middle Yangtze River are used to confirm the dry/wet variability in north China and the mid-low Yangtze River over the last two centuries.When considering the data from the last 530 years as a whole, the first two modes of dryness/wetness variability are found in the mid to low sections of two major valleys in eastern China, the Yellow and Yangtze River valleys. These valleys experienced the largest dryness/wetness variability in the history of eastern China. The third and fourth modes are located in northwest and northeast China. The fifth and sixth modes are situated in south and southwest China. However, over the last 500 years the strength and location of principal modes have experienced significant changes. During the 20th century, the first mode is found in the lower Yangtze River valley, the second mode in south China while the third mode is located in the mid-low Yellow River valley. During the 19^th century, the first three modes are situated in the mid-low Yellow River, the mid-low Yangtze River and south China, respectively. The first two modes in the 18^th century are located in the mid-low Yellow River and the mid-low Yangtze River valleys. The largest change of all modes occurred in the 17^th century with the first mode in northeast China, the second mode in northwest China, and the third mode in the mid-low Yangtze River valley. During the 16^th century, the first two modes are found in the mid-low Yangtze River and the mid-low Yellow River valleys.In each of the last five centuries, some special dryness/wetness processes are characterized in the mid-low Yangtze River and the mid-low Yellow River (north China). During the 20^th century, continuous and severe wetness is experienced in the mid-low Yangtze River in the last two decades. A two-decade wetness period in north China was followed by a severe dry period in the late 19^th century. Inter-annual variability, decade and two-decade oscillations of dryness/wetness are experienced in the series of different modes from one century to another. Dry/wet variations in north China and the middle Yangtze River are confirmed by series of data on local precipitation and runoff.     

Comparison of TRMM and water district rain rates over New Mexico

This paper compares monthly and seasonal rain rates derived from the Version 5 (V5) and Version 6 (V6) TRMM Precipitation Radar (TPR, TSDIS reference 2A25), TRMM Microwave Imager (TMI, 2A12), TRMM Combined Instrument (TCI, 2B31), TRMM calibrated IR rain estimates (3B42) and TRMM merged gauge and satellite analysis (3B43) algorithms over New Mexico (NM) with rain gauge analyses provided by the New Mexico water districts (WD). The average rain rates over the NM region for 1998–2002 are 0.91mmd?1 for WD and 0.75, 1.38, 1.49, 1.27, and 1.07mmd?1 for V5 3B43, 3B42, TMI, PR and TCA; and 0.74, 1.38, 0.87 and 0.97 mm d?1 for V6 3B43, TMI, TPR and TCA, respectively. Comparison of V5 3B43 with WD rain rates and the daily TRMM mission index (TPR and TMI) suggests that the low bias of V5 3B43 for the wet months (summer to early fall) may be due to the non-inclusion of some rain events in the operational gauge analyses that are used in the production of V5 3B43. Correlation analyses show that the WD rain rates vary in phase, with higher correlation between neighboring WDs. High temporal correlations (>0.8) exist between WD and the combined algorithms (3B42, 3B43 and TCA for both V5 and V6) while satellite instrument algorithms (PR, TMI and TCI) are correlated best among themselves at the monthly scale. Paired t-tests of the monthly time series show that V5 3B42 and TMI are statistically different from the WD rain rates while no significant difference exists between WD and the other products. The agreements between the TRMM satellite and WD gauge estimates are best for the spring and fall and worst for winter and summer. The reduction in V6 TMI (?7.4%) and TPR (?31%) rain rates (compared to V5) results in better agreement between WD estimates and TMI in winter and TPR during summer.