利用AMSU-B和GOES-9卫星资料对热带气旋“蒲公英”的分析

利用NOAA-16/AMSU-B三水汽通道微波亮温差和GOES-9红外亮温阈值对热带气旋深厚对流云进行检测,同时利用GOES-9可见光、红外、水汽多光谱通道特征对热带气旋云系进行识别,通过一次台风“蒲公英”个例,对热带气旋在微波和光学遥感图像上的深厚对流云进行分析。结果表明,微波和光学遥感资料均能对热带气旋深厚对流云进行有效识别,检测结果基本一致,但识别出的对流云,微波范围较小,光学遥感范围较大,这可能是由于光学遥感仅能获得云顶信息,将对流云顶部覆盖的卷云错判造成的;即使采用较低亮温阈值,光学遥感也很难将这部分卷云完全分离,而微波对云更具穿透性,在深厚对流云的识别方面具有独特优势;三水汽通道间微波亮温差反映了深厚对流云的发展强度,可间接揭示热带气旋的发展情况。     

应用AMSU-B微波资料识别强对流云区的研究

微波遥感可以穿透云顶直接探测对流云内的冰态粒子分布,受冰晶粒子的强烈散射衰减作用,AMSU-B的3个微波水汽吸收波段亮温随冰粒子的增加而降低.由于探测权重高度不同,辐射传输过程中受冰粒子的散射影响也不尽相同,3个水汽通道之间存在亮温差异,这种差异与对流云的强弱密切相关.利用微波向量辐射传输模式(VDISORT)模拟了云雨粒子对微波水汽通道观测的影响,并利用2005年8月12日华北地区的对流天气过程,分析了AMSU-B通道亮温与对流强弱变化之间的对应关系.在此基础上,建立了一种利用NOAA卫星AMSU-B水汽通道亮温差定量判识深对流云和冲顶对流云的方法.利用该方法对典型对流降水云团进行判识,结果显示,微波识别的对流云区可以较好地表征强降水的分布,其中的冲顶对流区与可见光云系的上冲云顶结构有着很好的对应.     

利用FY-2E红外和水汽波段对强对流云团的识别和演变研究

使用FY-2E静止气象卫星的红外1(10.3~11.3μm)和水汽波段(6.3~7.6μm)时序图像,对强对流云进行识别和短时预测。亮温阈值法是将强对流云和其他高云区分开的常用方法,但是合适的亮温阈值是随着时间和空间而变化的,过高的阈值会将许多卷云包括进来,太低的阈值会排除掉云顶发展还不是很高的强对流云。水汽波段所在的位置是水汽的一个强吸收带,而高度在400 h Pa上下的大气层是水汽波段的一个强吸收层,大气在垂直方向上的对水汽波段辐射吸收的分布模式使得卫星接收到的水汽波段辐射主要来自于400 h Pa以上的大气中高层,而卫星接收到的红外波段辐射主要来自于大气中低层,两个波段间辐射来源的差异使得不同光学厚度的高云的辐射观测值在红外—水汽光谱空间中的分布具有明显差别,并且这种差异具有时空的稳定性。本文将一定范围内的云团的象元测值在红外—水汽光谱空间中的分布的拟合直线斜率作为强对流云识别的依据,结果表明相对于亮温阈值法,本文的识别方法不仅能够较好地区别卷云和强对流云,同时也能更有效地识别未达到旺盛阶段的对流云。在对强对流云进行识别后,根据相邻时间段的卫星图像,利用交叉相关法反演得到强对流云团顶部的位移矢量场,并根据后向轨迹法对强对流云团位置形状进行短时预测,预报结果在短时间内(0~1 h)较好,并且对面积较大的云团的预报效果要优于较小的对流云团。此外文中还利用逐半小时的云顶黑体温度(Temperature of Black Body,TBB)资料分析了云顶亮温的分布变化,得到了整个强对流过程的演变特征。     

FY-2C高时间分辨率扫描数据在强对流云团监测中的应用研究

利用我国首次获取的静止气象卫星平均10 分钟观测间隔的高时间分辨率数据对2011 年6 月28 日～29日发生的一次强对流云团特征进行分析。Hovm?ller 分析图清晰地展示出在高时间分辨率观测条件下云团中心冷核的演变特征。高时间分辨率卫星资料与地面降水量进行联合分析,可推知对流云团中冷核的演变与地面小时降水量大值的落区间有很好的一致性;10 分钟雨量资料联合前推1 小时内7 次平均10 分钟观测间隔的卫星红外1通道亮温,分析可知地面雨量较大时,云顶像元亮温具有持续降低或维持低温状态的特点。反映出在对流性强降水中,冷且具有一定稳定性的云顶是产生大降水的主要特征。研究结果显示,静止气象卫星的高时间分辨率观测可很好地捕捉到强对流云团发展的演变特征,利用FY-2C 静止气象卫星红外1 通道亮温、红外1 通道与水汽通道亮温差在高时间分辨率观测中的时差特征变量,可实现对强对流云团初生的有效监测,为强对流云团的预测预报提供支撑。     

导致区域性雷暴大风天气的云型分类及统计特征分析

利用2005—2011年的静止卫星、常规探空和重要天气报资料,文章选取了18次典型区域性雷暴大风过程,在分析500 hPa天气形势基础上对导致雷暴大风的强对流云型进行了分类分析,其发展过程可划分为初始、发展、成熟和消亡四个阶段。对静止卫星观测的定量特征分析表明,对流云团中IR1通道和水汽(WV)通道的亮温差基本为负值,其值的不断减小预示着强对流在持续发展;在监测和预报雷暴大风天气时,需要特别关注长椭圆形强对流云带的右侧和其右侧的孤立对流云团,尤其是TBB(红外亮度温度)低负值区、TBB高梯度区、IR1和WV通道亮温差值区及大梯度区均配合的区域。在定性分析的基础上对静止卫星IR1与WV通道的亮温特征进行了定量统计分析,获得了雷暴大风出现站点附近的红外亮温、水汽亮温、IR1与WV通道亮温差和红外亮温梯度的分布情况,结果发现大部分站点的雷暴大风天气出现在以下时段:红外亮温由急剧下降到平缓下降之间的过渡期;IR1与WV通道亮温差由迅速下降转为缓慢下降或稳定少变的时间点前后,且多数处于IR1和WV通道亮温差由正转负临近的时间段内;红外亮温梯度达到最大的时间点附近或开始下降的时候。     

FY3极轨卫星资料在暴雨短时预报中的应用分析

利用常规观测、地面加密自动站降水资料以及FY3B极轨卫星资料,对2014年5月18日广西一次暴雨过程中暴雨云团的微波湿度计特征进行分析,得出以下结论:(1)广西中东部处受高空槽前西南气流影响,中低层水汽条件较好,低层有切变辐合,为中尺度对流系统的发生、发展提供了有利背景。(2)250m分辨率可见光通道云图上对流发展旺盛的云团有明显不均匀纹理结构,其中镶嵌着多个圆形的暗影,预示着云中伴有强降雨、雷暴大风等灾害性天气。(3)微波湿度计资料反映出暴雨云团从低层到中高层都有较高的水汽含量,50mm以上的强降雨区主要位于3个亮温低值中心的过渡区域,即对流云团合并处。高低层通道亮温差能反映云团对流的强弱程度,强降雨发生在微波亮温差的正值区,通道3与通道5的亮温差对6h强降雨落区有较好的指示意义。     

基于卫星雷达等多源资料的短时暴雨预警

利用2017—2018年葵花卫星(Himawari)TBB亮温资料,计算最低亮温、亮温梯度、红外与水汽亮温差和低亮温区面积及其随时间变化率等特征参量,确定短时暴雨的卫星参数阈值,并融合了雷达参数阈值及过去1 h地面加密降水实况资料,采用指标叠加法判定监测区域内某一云团未来2 h能否产生区域性短时暴雨天气,并采用交叉相关法外推云团的移动,进而对强降水云团进行预警。对2019年几次暴雨过程预报检验结果是:预警命中率(POD)为80.6%~97.1%,平均为91.0%,临界成功指数(CSI)为77.2%~79.2%,平均为77.9%,所预警的云团未来2 h影响区域出现≥30 mm/h短时暴雨站数占全省短时暴雨站数的76.4%~96.2%,平均为85.2%,整体预警效果较好。     

台风"杜鹃"的AMSU卫星微波探测资料分析

由于微波具有穿透云的能力,AMSU卫星探测资料得到了越来越广泛的应用。利用AMSU多个通道的资料,对2003年台风“杜鹃”过程进行了分析。通过比较AMSU-B（89GHz和150GHz）与GOES-9的台风结构图像可以发现微波具有较强的垂直探测能力;在AMSU-B的5个通道中,通道2（Ch2）亮温值的大小能够最好地反映热带气旋螺旋雨带冰晶层的深厚程度,从而判定螺旋雨带的强度;Ch2的亮温分布与雷达的强降水回波有较好的对应关系;利用AMSU-A温度反演资料能够清晰地揭示热带气旋的暖心结构、地面风速和中心气压与250hPa温度距平的关系,以及强降水的落区等。结果表明：AMSU资料作为一种新的卫星微波探测资料,在热带气旋的结构、螺旋雨带的强度及其降水强度的分析预报中具有一定的应用价值。     

应用AMSU B微波资料分析0509号Matsa台风水汽场分布特征

热带气旋的形成、发展、移动以及消亡与大范围水汽输送和集中息息相关.此研究以2006年7-8月的AMSU-B的183.3±1、183.3±3、183.3±7GHz三个水汽通道亮温为主要资料源,利用三个通道所在的权重高度差异,分别建立了对流层高、中、低层水汽量与三个通道亮温的e指数统计回归关系.在此基础上,选取西北太平洋2005年09号麦莎台风,利用AMSU-B亮温通过回归关系式计算出的高、中、低层水汽量,就其水汽输送与强度变化关系进行了分析.结果表明,其一,微波水汽通道亮温能够很好地表征热带气旋环境场不同高度的水汽分布和水汽输送,尤其是对流层中层亮温的变化显示出的水汽量的变化与热带气旋强度密切相关;其二,热带气旋发展过程中,除了西南季风形成的西南水汽通道是水汽维持的重要输送带外,东南水汽通道也经常成为登陆热带气旋强度维持的主要水汽供给.     

基于“葵花8号”气象卫星的陇东南地区强对流识别跟踪技术研究

利用常规观测资料、区域自动站资料和“葵花8号”气象卫星资料,对2016年4—9月甘肃省陇东南地区出现的43次强对流天气过程进行分析,确立了强对流云团识别指标、追踪方法及预报指标,并对2018年部分个例进行效果检验。结果表明：（1）利用卫星B13通道（10.4μm）亮温值TBB≤238 K或B08通道（6.2μm）与B13通道亮温差△TBB<0 K双阈值作为强对流云团识别指标,可以准确识别出陇东南地区的强对流天气云团;（2）利用“逆向搜索法”、“面积重叠法”及对云团重心的计算,可以对强对流云团进行准确定位、追踪及移动路径外推预报;（3）建立的强对流天气落区判别指标对该地区短时强降水及冰雹落区具有一定的预报能力。     

西南低涡对流云团及其降水的一些特征

基于FY-2C静止卫星红外和水汽通道资料,简单分析了发生在四川盆地的西南低涡暴雨云团生消过程,给出了一些有意义的云团生命特征。同时,结合相应的地面自动站降水资料,详细分析了卫星红外和水汽通道云顶亮温与对流云团降水之间的关系特征,结果表明:对于一完整对流降水过程,1小时内最低水汽亮温和水汽亮温增量能很好地描述地面1小时累计降水特征。然而,用静止卫星红外或水汽通道亮温来表征的云团降水特征是非常复杂的。尽管具有相同的最低云顶红外或水汽亮温,但对不同的对流过程其总体降水量级趋势不一样。而且,对于同一对流过程的不同发展阶段,即使出现云顶红外或水汽亮温一样,但其地面降水特征也是不一致的。甚至是对于同一时刻具有相同最低红外或最低水汽亮温特征的云,其降水落区与量级都不尽相同。正是这些复杂的降水特征,使得西南低涡对流云团的降水估算具有很大的难度。      

半透明云风矢量高度算法中云下背景辐射的估计

半透明云风矢量高度指定是卫星风矢量算法的重要部分,需要来自半透明云体的辐射和云下背景辐射两个变量。云下背景辐射发生在云层下面,未被卫星直接观测到,为了在半透明云风矢量高度指定算法中更精确地获得云下背景辐射,使用风矢量附近无云区的红外/水汽散点图,估计云下背景辐射。分析表明:在追踪区域里存在无云区的情况下,追踪区的最高红外亮温可代表红外通道的背景辐射;而水汽通道的背景辐射,却在红外亮温高值区段内水汽亮温相对较低区段。追踪区内找不到无云区时应扩大搜索范围,找到无云区后可估计云下背景辐射。在半透明云风矢量高度指定算法中使用云下背景辐射估计的改进算法前后,计算FY-2气象卫星进行风矢量,并将结果与欧洲中期天气预报中心（ECMWF）分析场进行对比表明,在半透明风矢量高度指定算法中使用云下背景辐射估计,FY-2气象卫星风矢量误差明显降低。     

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.     

Latitudinal and Scan-dependent Biases of Microwave Humidity Sounder Measurements and Their Dependences on Cloud Ice Water Path

The relationship between differences in microwave humidity sounder(MHS)–channel biases which represent measured brightness temperatures and model-simulated brightness temperatures, and cloud ice water path(IWP) as well as the influence of the cloud liquid water path(LWP) on the relationship is examined. Seven years(2011–17) of NOAA-18 MHS-derived measured brightness temperatures and IWP/LWP data generated by the NOAA Comprehensive Large Array-data Stewardship System Microwave Surface and Precipitation Products System are used. The Community Radiative Transfer Model, version2.2.4, is used to simulate model-simulated brightness temperatures using European Center for Medium-Range Weather Forecasts reanalysis data as background fields. Scan-angle deviations of the MHS window channel biases range from-1.7 K to1.0 K. The relationships between channels 2, 4, and 5 biases and scan angle are symmetrical about the nadir. The latitudedependent deviations of MHS window channel biases are positive and range from 0–7 K. For MHS non-window channels,the latitudinal deviations between measured brightness temperatures and model-simulated brightness temperatures are larger when the detection height is higher. No systematic warm or cold deviations are found in the global spatial distribution of difference between measured brightness temperatures and model-simulated brightness temperatures over oceans after removing scan-angle and latitudinal deviations. The corrected biases of five different MHS channels decrease differently with respect to the increase in IWP. This decrease is stronger when LWP values are higher.     

Local Initiation of Deep Convection on the Canadian Prairie Provinces

Our study found that local mesoscale circulations associated with highlandareas, and transient evapotranspiration discontinuities, are likely to influencethe timing and location of the initiation of deep convection across the Canadianprairie provinces when synoptic-scale forcing is weak (e.g., weak low level windsand no frontal boundaries). The cumulus congestus and cumulonimbus clouds thatformed over the highland areas were initiated by anabatic wind induced mesoscalecirculations. These deep convective clouds generally formed relatively early in theday (about 1030 Central Standard Time (CST)). In the relatively flat cropped grasslandregion, transient evapotranspiration gradients influenced the location of deep convectioninitiation, and the average soil moisture in the root zone had a direct impact on theirtiming. As root zone soil moisture declined from greater than 70% to less than 30%,convection initiation was delayed from about 0930 to 1630 CST. Cumulus congestusand cumulonimbus clouds that formed over the ephemeral evapotranspiration gradientswere initiated by land-land circulations. The study has improved the understanding ofthe influence of local surface forcing on the development of deep convective cloud onthe Canadian prairie provinces. The identification of areas where deep convection islikely to be initiated with weak synoptic forcing will also aid in the forecasting ofthunderstorms in this region.     

Using longwave HIRS radiances to test climate models

A ‘model-to-radiance’ comparison of simulated brightness temperatures from the Hadley Centre Global Environmental Model 2 with measurements from the High Resolution Infrared Radiation Sounder/4 (HIRS/4) instrument onboard the MetOp-A satellite is presented. For the all-sky, the model overestimates brightness temperatures in the atmospheric window region with the greatest biases over areas associated with deep convective cloud. In contrast to many global climate models, much smaller clear-sky biases are found indicating that model clouds are the dominating source of error. Simulated values in upper atmospheric CO₂ channels approximate observations better as a result of compensating cold biases at the poles and warm biases at lower latitudes, due to a poor representation of the Brewer Dobson circulation in the 38 level ‘low-top’ configuration of the model. Simulated all and clear-sky outgoing longwave radiation (OLR) evaluated against the Clouds and the Earth’s Radiant Energy System (CERES) and HIRS OLR products reveal good agreement, in part due to cancellation of positive and negative biases. Through physical arguments relating to the spectral energy balance within a cloud, it is suggested that broadband agreement could be the result of a balance between positive window biases and unseen negative biases originating from the water vapour rotational band in the far infrared (not sampled by HIRS).     

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。      

Subpixel fractional cloud cover and inhomogeneity effects on microwave beam-filling error

Using synthetic geometrical clouds and radiative microwave model, we examine the possibility to correct the estimations of liquid water path (LWP) or rain rate with cloud cover measurement. This information may be gotten by co-localized measurements of microwave and infrared/visible measurements on new satellites (TRMM, ADEOS 2, …). In a first step, the effects of fractional cloud cover on microwave brightness temperatures (TB) are investigated in three typical cases of nonprecipitating and precipitating (stratiform and convective) clouds. The beam-filling error (BFE) on brightness temperatures may be analyzed with the known spatial variability using 1D or 3D radiative transfer model. Relationships between BFE and subpixel cloud fraction (CF) are discussed according to the cloud type. We tested several parameters that characterize the horizontal cloud inhomogeneity within a radiometer field of view. BFE was found very sensitive to cloud type and inhomogeneity and is maximum for raining cloud with open spatial structure. In order to account for the uncertainty introduced by the spatial distribution, dependence of BFE on textural-based parameters is also discussed using homogeneity, entropy and an indicator of CF horizontal gradient.