基于Himawari-8卫星的云参数和降水关系研究

基于日本Himawari-8卫星的云产品,对中国中东部地区2017年夏季(6—8月)每日08—17时的降水资料进行了分析,重点讨论了云光学厚度(COD)、云顶粒子平均尺度(CPS)、云顶温度(CTT)三个云参数与降水的关系。试验表明,降水概率与云参数相关性较高,存在随着COD增加、CPS增加、CTT减小而增加的明显趋势。但是,单个云参数与降水强度相关性则较低;COD、CPS、CTT与小时降雨率的相关系数分别为0.2315、0.1823、-0.2235,均为弱相关。如果综合考虑联合两个或三个云参数形成小时降雨率分布矩阵,则降水过程能得到更为清晰的体现。2017年8月28日的个例表明,相比纯粹基于红外的算法,三参数方法可以明显提高小时降雨率的估计精度。     

Assessment of FY-4A and Himawari-8 Cloud Top Height Retrievalthrough Comparison with Ground-Based MillimeterRadar at Sites in Tibet and Beijing

正1Key Laboratory of Middle Atmosphere and Global Environment Observation, Institute of Atmospheric Physics,Chinese Academy of Sciences, Beijing 100029, China2University of Chinese Academy of Sciences, Beijing 100049, China     

基于Himawari-8气象卫星的东亚夏季冰云云顶特征分布

利用Advanced Himawari Imager(AHI)/Himawari-8(HW8)云产品分析2017 年夏季东亚地区不同下垫面条件对冰云云顶特征的影响,针对不同下垫面条件选择了9 个研究区域.结果表明,冰云最常出现在青藏高原和南亚季风区.冰云的发生频率存在南北递减的趋势,并在日本附近有高值中心.冰云云顶的垂...     

Himawari-8气象卫星黄海浒苔动态监测

基于Himawari-8气象卫星数据,研究了利用归一化植被指数提取浒苔信息方法,并提出了浒苔强度和移动速度估算方法。对2016年5-7月黄海海域浒苔信息进行监测,获得了浒苔暴发的时间、地点、面积、强度、影响范围、漂移路径及移动速度。结果表明:2016年5月19日Himawari-8气象卫星首次监测到黄海海域出现浒苔信息;6月中下旬进入暴发期,浒苔面积、影响范围及强度达到最大值;7月上旬,伴随着浒苔大面积登陆青岛、烟台、威海等地,浒苔进入缓慢消亡阶段。多时次浒苔强度合成产品显示:2016年浒苔在黄海中部海域、烟台以东海域覆盖强度较大,在初始位置一带覆盖强度较小。浒苔漂移路径整体为从东南外海逐渐开始向西北近海海域靠近,日移动速度不断变化。浒苔的动态变化与水文气象环境密切相关,适宜的温度是浒苔生长和发展的基础,浒苔出现后,盛行风向是浒苔漂移方向的主要驱动力,2016年5-7月强劲的南风使浒苔一直向北漂移,并最终抵达威海,浒苔的移动与风向大致相同。     

基于葵花8号卫星资料的沈阳两次暴雨过程中对流云特征对比分析

应用葵花8号卫星资料,结合NCEP FNL再分析、GNSS遥感水汽、风廓线雷达、全国智能网格实况融合分析资料,对2017年7月14日和2018年8月7日沈阳两次暴雨过程(分别简称过程Ⅰ和过程Ⅱ)中对流云特征进行了比较分析,重点探讨了对流云的触发维持机制与影响降水特征差异的因素。结果表明:(1)两次过程分别为局地突发暴雨和区域性极端暴雨,沈阳市区暴雨均由两个对流云团引发,对流云团合并使得降水持续。过程Ⅱ云团合并发生在其移动方向的后侧,具有后向传播特征,合并云团沿其长轴方向移动影响沈阳市,使降水时间延长。(2)在降水前至降水初期,过程Ⅰ对流云顶和水汽层顶快速上升且云顶迅速超过水汽层顶,而过程Ⅱ亮温下降缓慢。短时强降水发生前红外和水汽亮温同步快速降至-60℃,可作为提前预判对流云团产生短时强降水的参考指标。10 min雨量大于10 mm的对流云云顶集中分布在红外亮温低于-55℃、亮温差为-5~0℃的范围。(3)两次过程中,沈阳市分别位于东北冷涡后部和副热带高压北缘。过程Ⅰ,探空曲线呈“X”型,CAPE高达2584 J·kg^-1,造成对流云深厚,云底以下干层导致雨滴蒸发,使降水强度减弱,该过程高强度降水仅发生在对流云团合并加强阶段。过程Ⅱ,云底到地面湿层明显,保证了雨滴降至地面,产生相同量级降水的云团的TBB比过程Ⅰ高。(4)强降水发生前,地面风场存在明显辐合,当大气可降水量2 h内跃增8 mm时,站点出现强降水;局地水汽跃增可能是低空西南气流偏南分量增大或偏北冷空气侵入到暖湿空气中所致。     

Assimilating All-sky Infrared Radiances from Himawari-8 Using the 3DVar Method for the Prediction of a Severe Storm over North China

Although radar observations capture storm structures with high spatiotemporal resolutions, they are limited within the storm region after the precipitation formed. Geostationary satellites data cover the gaps in the radar network prior to the formation of the precipitation for the storms and their environment. The study explores the effects of assimilating the water vapor channel radiances from Himawari-8 data with Weather Research and Forecasting model data assimilation system(WRFDA) for a severe storm case over north China. A fast cloud detection scheme for Advanced Himawari imager(AHI)radiance is enhanced in the framework of the WRFDA system initially in this study. The bias corrections, the cloud detection for the clear-sky AHI radiance, and the observation error modeling for cloudy radiance are conducted before the data assimilation. All AHI radiance observations are fully applied without any quality control for all-sky AHI radiance data assimilation. Results show that the simulated all-sky AHI radiance fits the observations better by using the cloud dependent observation error model, further improving the cloud heights. The all-sky AHI radiance assimilation adjusts all types of hydrometeor variables, especially cloud water and precipitation snow. It is proven that assimilating all-sky AHI data improves hydrometeor specifications when verified against the radar reflectivity. Consequently, the assimilation of AHI observations under the all-sky condition has an overall improved impact on both the precipitation locations and intensity compared to the experiment with only conventional and AHI clear-sky radiance data.     

基于朴素贝叶斯的FY-4A/AGRI云检测方法

光学遥感云检测是定量遥感和遥感应用的基础, 尝试将朴素贝叶斯的机器学习方法应用于风云四号气象卫星A星(FY-4A)搭载的先进静止轨道辐射成像仪(AGRI)红外通道数据云检测。因辐射物理方法的云检测采用可见光通道导致存在日夜不连续现象,仅选取FY-4A/AGRI载荷7个红外通道的光谱数据,构建10种特征分类,利用正交偏振云-气溶胶激光雷达(CALIOP)与FY-4A/AGRI时空匹配数据,对不同地表类型和不同季节的数据集进行分类训练和验证。与CALIOP数据交叉验证显示除积雪上空云识别准确率约为81%,深海、浅水、陆地和荒漠上空的云识别准确率均高于92%,误判率基本低于10%,总体云识别精度达到90%;与2021年10月和2022年1,4,7月MODIS 2级云检测产品比对,深海、浅水云识别准确率均在88%以上,误判率分别低于3%和10%,夏季云识别效果最佳,总体云识别准确率高达90%。云检测结果不仅得到云、可能云、可能晴空和晴空4种分类结果,还得到每种特征和综合特征云检测分类器的不确定性概率值,这为云和地表相关检测产品提供重要参考。     

基于深度语义分割的FY-2E遥感影像云检测方法

本文提出了一种基于深度语义分割技术的全自动云检测算法,可提高FY-2E遥感影像的云检测精度。首先,将FY-2EL1数据与精度较高的云检测结果进行匹配,获得用于训练和评估样本的数据集;其次,设计了深度语义分割网络,并针对训练集中正负样本严重失衡的问题,改进了损失函数,可以有效提取云的边界;最后,分别以FY-2E和MODIS数据作为训练和标签样本训练网络,得到了可用于FY-2EL1影像检测的四分类模型。试验结果表明,在四分类检测中,所提方法的准确率达到了75%,Kappa系数为0.53左右。与现有多通道阈值法相比,采用所提方法进行二分类检测可提高约90%样本的准确率,部分样本的准确率提升20%以上。此外,所提方法对云边缘、破碎云等细节识别能力较强,且具有一定的鲁棒性,受训练样本中的误判类别影响较小。未来通过扩充数据集并优化网络,可提高FY-2全圆盘影像的数据质量。     

利用气温与云顶亮温差值检测云方法研究

利用2005年7-12月卫星资料、地面观测资料、探空资料和地形资料,统计分析了无云条件下气温与云顶亮温的差值,结果表明：无云情况下气温与云顶亮温差值平均为5.04℃,且具有较大的日变化性。由此提出了按时次分别求取阈值用以检测云。通过个例对比分析,初步得出该方法检测云具有以下特点：可用于全天候云检测;不受地形地貌等因素的影响;该云检测方法应用于反演计算,不仅反演的中、高云区域与实测的比较一致,而且反演的低云区域与实测结果也比较一致。     

Characteristics of Pre-summer Daytime Cloud Regimes over Coastal South China from the Himawari-8 Satellite

Using the high spatiotemporal resolution (2 km-and-10 min) data from the Advanced Himawari Imager onboard the Himawari-8 satellite, this study documents the fine-scale characteristics of daytime cloud regimes (CRs) over coastal South China during the pre-summer rainy season (April–June). Six CRs (CR1–CR6) are identified based on the joint frequency distribution of cloud top brightness temperature and cloud optical thickness, namely, the optically thin-to-moderate cloud mixture, optically thin warm clouds with cirrus, optically thick warm clouds, weak convective cloud mixture, strong convective clouds, and extreme, deep convective clouds. The optically thick warm clouds are the major CR during April and May, with higher frequencies over land, especially along the urban agglomeration, rather than the offshore which may be an indicator of the higher aerosol concentrations being a contributing factor over the cities. The CRs with weak convective cloud mixtures and strong convective clouds appear more frequently over the land, while the two CRs with optically thinner clouds occur mainly offshore. Synoptic flow patterns (SPs) are objectively identified and examined focusing on those favoring the two major rain-producing CRs (CR5 and CR6) and the highly reflective CR with optically thick warm clouds (CR3). The two SPs favoring CR5 and CR6 are characterized by abundant moisture with low-level jets after monsoon onset, and a northwest high-southeast low pattern with strong dynamic convergence along the coastline, respectively. The non-convective CR3 with high reflectance is related to a SP that features the western North Pacific subtropical high extending more westward, leading to a moderate moisture supply and a wide range of convective available potential energy, but also, large convective inhibition.     

A Cloud Detection Scheme for the Chinese Carbon Dioxide Observation Satellite(TANSAT)

Cloud detection is an essential preprocessing step for retrieving carbon dioxide from satellite observations of reflected sunlight.During the pre-launch study of the Chinese Carbon Dioxide Observation Satellite(TANSAT),a cloud-screening scheme was presented for the Cloud and Aerosol Polarization Imager(CAPI),which only performs measurements in five channels located in the visible to near-infrared regions of the spectrum.The scheme for CAPI,based on previous cloudscreening algorithms,defines a method to regroup individual threshold tests for each pixel in a scene according to the derived clear confidence level.This scheme is proven to be more effective for sensors with few channels.The work relies upon the radiance data from the Visible and Infrared Radiometer(VIRR)onboard the Chinese Feng Yun-3A Polar-orbiting Meteorological Satellite(FY-3A),which uses four wavebands similar to that of CAPI and can serve as a proxy for its measurements.The scheme has been applied to a number of the VIRR scenes over four target areas(desert,snow,ocean,forest)for all seasons.To assess the screening results,comparisons against the cloud-screening product from MODIS are made.The evaluation suggests that the proposed scheme inherits the advantages of schemes described in previous publications and shows improved cloud-screening results.A seasonal analysis reveals that this scheme provides better performance during warmer seasons,except for observations over oceans,where results are much better in colder seasons.     

北京香山\

应用葵花8号卫星资料,结合NCEP FNL再分析、GNSS遥感水汽、风廓线雷达、全国智能网格实况融合分析资料,对2017年7月14日和2018年8月7日沈阳两次暴雨过程（分别简称过程Ⅰ和过程Ⅱ）中对流云特征进行了比较分析,重点探讨了对流云的触发维持机制与影响降水特征差异的因素。结果表明：（1）两次过程分别为局地突发暴雨和区域性极端暴雨,沈阳市区暴雨均由两个对流云团引发,对流云团合并使得降水持续。过程Ⅱ云团合并发生在其移动方向的后侧,具有后向传播特征,合并云团沿其长轴方向移动影响沈阳市,使降水时间延长。（2）在降水前至降水初期,过程Ⅰ对流云顶和水汽层顶快速上升且云顶迅速超过水汽层顶,而过程Ⅱ亮温下降缓慢。短时强降水发生前红外和水汽亮温同步快速降至-60℃,可作为提前预判对流云团产生短时强降水的参考指标。10 min雨量大于10 mm的对流云云顶集中分布在红外亮温低于-55℃、亮温差为-5~0℃的范围。（3）两次过程中,沈阳市分别位于东北冷涡后部和副热带高压北缘。过程Ⅰ,探空曲线呈“X”型,CAPE高达2 584 J·kg^-1,造成对流云深厚,云底以下干层导致雨滴蒸发,使降水强度减弱,该过程高强度降水仅发生在对流云团合并加强阶段。过程Ⅱ,云底到地面湿层明显,保证了雨滴降至地面,产生相同量级降水的云团的TBB比过程Ⅰ高。（4）强降水发生前,地面风场存在明显辐合,当大气可降水量2 h内跃增8 mm时,站点出现强降水;局地水汽跃增可能是低空西南气流偏南分量增大或偏北冷空气侵入到暖湿空气中所致。

     

一次对流降水过程增雨催化时机的模拟分析和雷达识别

为加深对对流云增雨催化机制的理解,避免催化指标的随意性,通过三维积云数值模拟和雷达加密探测资料分析相结合的方法,对发生在湖北省西北部多山地区的一次对流降水过程进行了增雨催化时机的分析和识别.首先运用三维积云模式,通过催化模拟敏感试验,得出对流云增雨催化存在时间窗,最佳的催化时机应该为对流云发展的早期,催化后关键是增加雨和冰晶冻结后碰并霰增长这一冰相微物理过程的产量.根据模式分析结果,结合雷达观测资料分析,发现对流回波在生成及发展上表现出一些规律性,可以作为通过雷达观测识别增雨催化的时机和潜力区域的指标.     

基于迁移学习的卫星云图云分类

针对在基于机器学习的云图识别中,由于不存在公认的云分类样本库的现实条件下,带来的训练样本数量不足和不平衡,从而难以获得可靠的分类模型的问题,利用迁移学习中的多源加权Tradaboost算法(内部采用极限学习机作为分类器)来进行卫星云图云的检测。利用多人(多源)标注的大量厚云的样本,构成多源辅助样本集;利用少量标注的薄云样板构成目标样本集。使用迁移学习和辅助样本集,对仅在薄云样本集下的训练获得的极限学习机分类器进行辅助训练,提高其薄云识别率。基于国家卫星气象中心的HJ-1A/B的卫星数据实验结果表明,迁移学习可以充分利用容易获得的大样本厚云辅助样本知识,对同类型有关联的小样本薄云分类器进行识别提高。实验表明,迁移学习算法可以进一步用于更多多源样本和其他云分类的任务。     

基于FY-2C红外资料进行夜间云检测的方法研究

应用2007年4月30日20时至5月1日03时的FY-2C红外亮温资料、地面云观测资料以及地理海拔高度资料,详细分析了无云条件下红外亮温与地理海拔高度之间的关系特征。通过建立夜间无云条件下红外亮温与地理海拔高度之间对应关系的标准数据库,以此为判据实现了有云区和无云区的分离,云检测个例结果表明正确率可达88%;将无云区误判为有云区的百分比为4%,同时将有云区误判为晴空区的百分比是8%。在云区的云检测正确率为82%;在无云区的云检测正确率达到92%。在业务化应用和适用性分析中,以5月1日00时资料建立的判据,分别对5月2日至5日00时整个区域进行云检测试验,分析结果表明其总体准确率与5月1日00时的基本接近但稍高,达到92%。在云区的判识正确率提高到93%,从而导致总体准确率稍微提高;而在无云区的判识效果与5月1日00时的一致为92%。     

A Validation of CloudSat and CALIPSO's Temperature,Humidity, Cloud Detection,and Cloud Base Height over the Arctic Marine Cryosphere

Abstract

This study presents a semi‐analytic non‐iterative solution for the Monin‐Obukhov similarity equations under unstable surface conditions. The solution is represented in terms of the non‐dimensional Monin‐Obukhov stability parameter z/L. This parameter is given as a function of the bulk Richardson number and other surface parameters including the heat and momentum roughness lengths which are generally assumed to be different in this formulation. The proposed formulations give results that are both quantitatively and qualitatively consistent with the fully iterated numerical solution for a wide range of surface parameters.     

冻滴微物理过程的分档数值模拟试验研究

霰和冻滴是深对流降水的主要来源。由于二者密度差异造成的不同下落末速度必然会导致云微物理过程的变化以及降水时空分布的改变。我们在以色列特拉维夫大学二维轴对称对流云全分档模式的基础上,将水成物粒子从34档增加到40档,修改了霰和雪的密度,加入冻滴分档处理的微物理过程,发展了一个包括液滴、冰晶、雪、霰和冻滴更为详细的云微物理分档模式。利用改进后的模式模拟了一次理想的强对流天气过程,分析了改进模式与原模式模拟的云微物理量场以及水成物粒子的时空分布特征,模拟结果表明:（1）由于冻滴的产生,较大的下落末速度导致在云内-3℃至-8℃较早地出现了冻滴,并造成了大量的冰晶繁生。（2）冻滴形成前期,液态水中心区域位于垂直上升速度大值中心上方,形成液态水累积区;冻滴形成期,液态水累积区位于0℃层以上,雨滴冻结生成冻滴,霰与半径大于100 μm的液滴碰并生成冻滴;冻滴增长期,在垂直上升气流的支撑下,冻滴碰并过冷水增长,导致冻滴含量增大,液态水含量减小。因此,改进模式能较好的模拟冻滴的形成过程,可以将该分档处理的微物理方案耦合到三维WRF（Weather Research and Forecasting model）模式中,更深入地研究强雷暴风切变在冰雹生成过程中的作用。     

A Ka-band Solid-state Transmitter Cloud Radar and Data Merging Algorithm for Its Measurements

This study concerns a Ka-band solid-state transmitter cloud radar, made in China, which can operate in three different work modes, with different pulse widths, and coherent and incoherent integration numbers, to meet the requirements for cloud remote sensing over the Tibetan Plateau. Specifically, the design of the three operational modes of the radar(i.e., boundary mode M1, cirrus mode M2, and precipitation mode M3) is introduced. Also, a cloud radar data merging algorithm for the three modes is proposed. Using one month's continuous measurements during summertime at Naqu on the Tibetan Plateau,we analyzed the consistency between the cloud radar measurements of the three modes. The number of occurrences of radar detections of hydrometeors and the percentage contributions of the different modes' data to the merged data were estimated.The performance of the merging algorithm was evaluated. The results indicated that the minimum detectable reflectivity for each mode was consistent with theoretical results. Merged data provided measurements with a minimum reflectivity of -35 dBZ at the height of 5 km, and obtained information above the height of 0.2 km. Measurements of radial velocity by the three operational modes agreed very well, and systematic errors in measurements of reflectivity were less than 2 dB. However,large discrepancies existed in the measurements of the linear depolarization ratio taken from the different operational modes.The percentage of radar detections of hydrometeors in mid- and high-level clouds increased by 60% through application of pulse compression techniques. In conclusion, the merged data are appropriate for cloud and precipitation studies over the Tibetan Plateau.     

A Comparison of Cloud Layers from Ground and Satellite Active Remote Sensing at the Southern Great Plains ARM Site

Using the data collected over the Southern Great Plains ARM site from 2006 to 2010, the surface Active Remote Sensing of Cloud(ARSCL) and Cloud Sat-CALIPSO satellite(CC) retrievals of total cloud and six specified cloud types [low, mid–low(ML), high–mid–low(HML), mid, high–mid(HM) and high] were compared in terms of cloud fraction(CF), cloud-base height(CBH), cloud-top height(CTH) and cloud thickness(CT), on different temporal scales, to identify their respective advantages and limitations. Good agreement between the two methods was exhibited in the total CF. However, large discrepancies were found between the cloud distributions of the two methods at a high(240-m) vertical grid spacing. Compared to the satellites, ARSCL retrievals detected more boundary layer clouds, while they underestimated high clouds. In terms of the six specific cloud types, more low- and mid-level clouds but less HML- and high-level clouds were detected by ARSCL than by CC. In contrast, the ARSCL retrievals of ML- and HM-level clouds agreed more closely with the estimations from the CC product. Lower CBHs tended to be reported by the surface data for low-, ML- and HML-level clouds; however, higher CTHs were often recorded by the satellite product for HML-, HM- and high-level clouds. The mean CTs for low- and ML-level cloud were similar between the two products; however, the mean CTs for HML-, mid-, HM- and high-level clouds from ARSCL were smaller than those from CC.     

An Algorithm for Detecting Ice Cloud at Different Altitudes by Combining Dual CrIS Full Spectrum Resolution CO2 Channels

Using infrared sensors to detect ice clouds in different atmospheric layers is still a challenge. The different scattering and absorption properties of longwave and shortwave infrared channels can be utilized to fulfill this purpose.In this study, the release of Suomi-NPP Cross-track Infrared Sounder(Cr IS) full spectrum resolution is used to select and pair channels from longwave(～ 15 μm) and shortwave(～4.3 μm) CO_2 absorption bands under stricter conditions, so as to better detect ice clouds. Besides, the differences of the weighting function peaks and cloud insensitive level altitudes of the paired channels are both within 50 h Pa so that the variances due to atmospheric conditions can be minimized. The training data of clear sky are determined by Visible Infrared Imaging Radiometer Suite(VIIRS) cloud mask product and used to find the linear relationship between the paired longwave and shortwave CO_2 absorption channels. From the linear relationship, the so-called cloud emission and scattering index(CESI) is derived to detect ice clouds. CESI clearly captures the center and the ice cloud features of the Super Typhoon Hato located above 415 h Pa. Moreover, the CESI distributions agree with cloud top pressure from the VIIRS in both daytime and nighttime in different atmospheric layers.