基于TMI产品资料对数值模式水凝物模拟能力的检验分析

本文利用热带测雨卫星(Tropical Rainfall Measuring Mission,TRMM)搭载微波成像仪(TRMM Microwave Imager,TMI)的探测及反演结果,结合微波辐射传输模式,就2004年17号台风暹芭(Chaba)过程,对AREM(Advanced Regional Eta-coo...     

基于FY-3C微波成像仪的沙漠地表发射率反演

地表发射率是地表的固有属性,也是反演地表信息和大气温湿度廓线的重要参数.为了获取准确且具有具体物理含义的沙漠地区微波地表发射率,首先选取塔克拉玛干沙漠部分地区为反演区域,根据二元函数泰勒定理,推导了该地区的微波地表发射率与地表温度、地表湿度的线性、非线性函数关系.其次,利用最优控制原理,结合FY-3C微波成像仪的观测亮温资料与辐射传输模式(CRTM)模拟亮温数据,构建了沙漠地区微波地表发射率的线性与非线性反演模型.通过对比发现,利用线性和非线性反演模型得到的地表发射率不仅提高了反演区域亮温的模拟精度,而且模拟亮温的变化趋势也与观测更吻合.最后,对地表发射率的线性和非线性反演模型进行了不同时间与空间上的独立性检验,结果表明:除了反演区域外,在整个塔克拉玛干沙漠地区,两种模型反演的地表发射率仍比原地表发射率模拟亮温更接近观测.总的来说,线性和非线性反演模型对沙漠地区的微波地表发射率反演均具有一定的有效性和普适性,且非线性反演模型优于线性反演模型.     

格点尺度对TRMM微波成像仪云水数据的影响

选取热带测雨卫星(Tropical Rainfall Measuring Mission,TRMM)微波成像仪(TRMM Microwave Imager,TMI)液态水路径(liquid water path,LWP)轨道像元数据为研究对象,探讨了将瞬时探测以及逐月的像元数据进行格点化(0.1°、0.25°、0.5°、1.0°和2.5°五种格点分辨率)时,格点数据的失真情况。对TMI瞬时探测的个例分析结果表明,细分辨率(0.1°、0.25°和0.5°)格点能保留原始像元数据的细节;而随着网格变粗,细节受到较大的平滑。因此对于中尺度到天气尺度的天气系统分析而言,将卫星轨道数据处理到网格尺度不大于0.5°的格点更合适。对逐月LWP像元资料格点化处理的分析表明,细分辨率格点能保留LWP空间分布细节,尽管5种分辨率下LWP的概率密度分布(probability density function,PDF)均相近。因此,对月尺度及以上的气候分析研究而言,格点尺度大小对卫星像元数据格点化的影响不显著。最后利用本实验室计算的TMI/LWP格点数据与欧洲中期数值预报中心再分析资料(European Centre for Medium-range Weather Forecasts Interim reanalysis,ERA-Interim)和NCEP再分析资料(NCEP Climate Forecast System Reanalysis,NCEP CFSR)进行了对比,发现两种再分析资料都高估了LWP;TMI/LWP格点数据与两种再分析资料LWP的多年变化趋势大致相同。     

Microwave Remote Sensing of Tropical Cyclones from Space

This article reviews several microwave instruments employed in research and analysis of tropical cyclones (TCs), typhoons, and hurricanes. The instruments discussed include scatterometers, microwave radiometers, synthetic aperture radars (SARs), and rain radar from space. Examples of the particular contribution by one or more of these instruments in analysis of several storms illustrate the comprehensive new views provided by the SeaWinds scatterometers, the detailed high-resolution wind field provided by RADARSAT-1 SAR, particularly inside and in the vicinity of hurricane “eyes,” and the presence of secondary flows in the region between rainbands in TCs. The high spatial resolution of precipitation data from the Tropical Rainfall Measuring Mission's rain radar, combined with scatterometer or SAR data, give a significant improvement in the details that can be seen from space, at the surface, and in the precipitating areas of TCs. The microwave instruments provide a penetrating view below the upper level cirrus clouds. This revised version was published online in August 2006 with corrections to the Cover Date.     

Forecast of wheat yield throughout the agricultural season using optical and radar satellite images

The aim of this study is to estimate the capabilities of forecasting the yield of wheat using an artificial neural network combined with multi-temporal satellite data acquired at high spatial resolution throughout the agricultural season in the optical and/or microwave domains. Reflectance (acquired by Formosat-2, and Spot 4–5 in the green, red, and near infrared wavelength) and multi-configuration backscattering coefficients (acquired by TerraSAR-X and Radarsat-2 in the X- and C-bands, at co- (abbreviated HH and VV) and cross-polarization states (abbreviated HV and VH)) constitute the input variable of the artificial neural networks, which are trained and validated on the successively acquired images, providing yield forecast in near real-time conditions. The study is based on data collected over 32 fields of wheat distributed over a study area located in southwestern France, near Toulouse. Among the tested sensor configurations, several satellite data appear useful for the yield forecasting throughout the agricultural season (showing coefficient of determination (R²) larger than 0.60 and a root mean square error (RMSE) lower than 9.1 quintals by hectare (q ha⁻¹)): C_VH, C_HV, or the combined used of X_HH and C_HH, C_HH and C_HV, or green reflectance and C_HH. Nevertheless, the best accurate forecast (R² = 0.76 and RMSE = 7.0 q ha⁻¹) is obtained longtime before the harvest (on day 98, during the elongation of stems) using the combination of co- and cross-polarized backscattering coefficients acquired in the C-band (C_VV and C_VH). These results highlight the high interest of using synthetic aperture radar (SAR) data instead of optical ones to early forecast the yield before the harvest of wheat.     

RETRIEVING OVER-OCEAN PRECIPITABLE WATER FROM THE SSM/I MEASUREMENTS WITH SEVERAL REGRESSION ALGORITHMS*

A collocated SSM/I and radiosonde measurement data set provided by the NASDA(Japan) was used to retrieve the total precipitable water(PW) over oceans.The retrieval results obtained with several regression algorithms were compared against the radiosonde measurements.It is shown that:(a) the routinely operational algorithm of Alishouse et al.(1990) yields significant underestimation in high PW regime and overestimation in low PW regime;(b) a cubic correction by Colton and Poe(1994) is not sufficient and globally improves slightly the retrieval results;and(c) the regression algorithm with the form of brightness temperature(T_b) function In(280-T_b) gives a little largely scattered retrievals in whole PW range but without considerable over-and underestimates in low and high PW regimes.To improve the estimation of the oceanic precipitable water from the SSM/I measurements,a composite algorithm with different forms of T_b function in low.medium and high PW regimes is proposed and tested.     

Evaluation of the Community Microwave Emission Model Coupled with the Community Land Model over East Asia

The Community Microwave Emission Model (CMEM) developed by the European Centre for Me-dium-Range Weather Forecasts (ECMWF) can provide a link between surface states and satellite observations and simulate the passive microwave brightness temperature of the surface at low frequencies (from 1 GHz to 20 GHz).This study evaluated the performance of the CMEM cou-pled with the Community Land Model (CLM) (CMEM-CLM) using C-band (6.9 GHz) microwave brightness temperatures from the Advanced Microwave Scanning Radiometer on Earth Observing System (AMSR-E) over East Asia.Preliminary results support the argument that the simulated brightness temperatures of CMEM-CLM from July 2005 to June 2010 are comparable to AMSR-E observational data.CMEM-CLM performed better for vertical polarization,for which the root mean square error was approximately 15 K,compared to over 30 K for horizontal polarization.An evaluation performed over seven sub-regions in China indicated that CMEM-CLM was able to capture the temporal evolution of C-band brightness temperatures well,and the best correlation with AMSR-E appeared over western Northwest China (over 0.9 for vertical polarization).However,larger biases were found over southern North China and the middle and lower reaches of the Yangtze River.     

航空遥感测量海面油膜厚度的研究

本文根据3层介质微波辐射的基本原理,分析计算了各介质和环境参数对油膜厚度的影响大小,并以此为依据探讨了确定各参数的方法。通过露天水池试验给予验证。提出了在单波段(8mm)固定天线微波辐射计获取海水区与油膜区天线亮(度)温(度)两参数条件下的航空遥感南海海面油膜厚度计算模式,海上航空遥感测量实验与模式计算结果基本符合。     

环境样品中有机物分析的前处理技术

综述了顶空法、吹脱－捕集法、固相萃取、微波萃取、超临界流体萃取技术在环境样品前处理中的应用现状并比较了这种几样品处理方法的优缺点。     

Determination of the Turbulent Temperature–Humidity Correlation from Scintillometric Measurements

We report on the investigation and successful application of the bichromatic correlation of optical and microwave signals for determining the area-averaged correlation of temperature–humidity fluctuations. The additional technical effort is marginal compared to the common ‘two-wavelength method’, which has (in contrast) the restriction that only two of the three relevant meteorological structure parameters can be deduced. Therefore, in the past, it was often assumed that the turbulent humidity and temperature fluctuations are perfectly positively or negatively correlated. However, as shown in this study, over non-homogeneous terrain when the flow conditions are not ideal, this assumption is questionable. The measurements were analysed statistically, and were compared to in situ measurements of the Bowen ratio Bo and the correlation of temperature–humidity fluctuations using eddy-covariance techniques. The latter is in good agreement to that derived by scintillometry. We found that the correlation is not ±1 but as low as −0.6 for Bo smaller than −2, and up to 0.8 for Bo larger than 1.