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1.
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。 相似文献
2.
该文介绍了卫星观测海表温度 (SST) 算法的发展历程,给出了所用SST算法的回归模型,并在FY-3B/VIRR业务SST算法的基础上进行了改进。基于NOAA-19/AVHRR匹配数据集,进行多算法建模分析及精度评估,白天最优算法为非线性SST (NL) 算法,夜间最优算法为三通道SST (TC) 算法,最优算法的确定与NESDIS/STAR一致。建立2012年8月—2013年3月FY-3B/VIRR匹配数据集,并在此基础上进行多算法回归建模及精度评估,白天和夜间的最优均为NL算法,分析发现夜间TC算法采用匹配数据集版本2(MDB_V2) 时,3.7 μm通道存在类似百叶窗的条带现象。以2012年10—12月FY-3B/VIRR匹配数据集计算回归系数,以2013年1—3月独立样本进行精度评估,与浮标SST相比,NL算法白天和夜间的均方根误差分别为0.41℃和0.43℃。与日平均最优插值海温 (OISST) 相比,NL算法白天和夜间的均方根误差分别为1.45℃和1.5℃; 选择与OISST偏差在2℃以内的样本,NL算法白天和夜间均方根误差分别为0.82℃和0.84℃。 相似文献
3.
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. 相似文献
4.
云液态水路径是气候和天气系统分析的重要参数,可以从卫星观测资料反演获得.目前,基于卫星微波探测仪器观测资料的云水算法可由23.8和31.4 GHz两个通道产生.本研究使用先进技术微波探测仪(ATMS)观测数据,对物理和经验两种算法反演出的云液态水路径进行验证评估.结果表明,经验算法和物理算法都可以描述云液态水在全球洋面... 相似文献
5.
Himawari-8是日本发射的新一代静止气象卫星,与前一代的MTSAT-2相比,在时间、空间分辨率上都有了很大提升,特别是红外通道数量从4个增至10个,为红外遥感沙尘提供了新的观测数据。本研究利用Himawari-8的红外观测数据,发展了仅用红外通道的沙尘全天候判识算法,可以实现对白天和夜间的连续监测。算法在前人基础上去除了可见光通道,同时引入更多红外通道来进行云检测和沙尘判识。由于一日之中,地表温度发生变化,因此针对白天和夜间设置了两套不同的判别阈值,来保证算法的全天适用性。最后通过两次沙尘事件对沙尘判别结果的分析和检验表明,遥感判识结果与地面气象站和PM_(10)观测较为一致,说明了只用红外通道全天候判识沙尘的可行性。 相似文献
6.
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。 相似文献
7.
Mee-Ja Kim Mi-Lim Ou Eun-Ha Sohn Yoonjae Kim 《Asia-Pacific Journal of Atmospheric Sciences》2011,47(5):421-427
Multi-channel sea surface temperature (MCSST) data were retrieved from the Japanese geostationary satellite, MTSAT-1R, for East Asia in western North Pacific. The coefficients used to calculate the MCSST data were estimated by assuming a linear relationship between the brightness temperatures obtained from the satellite and the in-situ buoy SST. It is important to remove cloud contamination pixels to retrieve meaningful information from infrared data. Therefore, the cloud detection algorithm was improved by using a 10-day maximum or minimum composite map for infrared and visible channels. The RMSE of the MCSST in comparison with the two-year buoy SST was about 0.89oC. The error was the largest at mid-latitudes in summer. Additionally, the error between the two SSTs showed that diurnal variation had a positive bias during daytime and a negative bias during nighttime. Furthermore, in 2007, both SSTs showed seasonal and spatial diurnal variation. The magnitude of the daily variation in the MCSST was two times larger than that in the buoy SST, and this was attributed to diurnal heating with a weak surface wind speed. 相似文献
8.
Satellite and human visual observation are two of the most important observation approaches for cloud cover. In this study, the total cloud cover(TCC) observed by MODIS onboard the Terra and Aqua satellites was compared with Synop meteorological station observations over the North China Plain and its surrounding regions for 11 years during daytime and7 years during nighttime. The Synop data were recorded eight times a day at 3-h intervals. Linear interpolation was used to interpolate the Synop data to the MODIS overpass time in order to reduce the temporal deviation between the satellite and Synop observations. Results showed that MODIS-derived TCC had good consistency with the Synop observations; the correlation coefficients ranged from 0.56 in winter to 0.73 in summer for Terra MODIS, and from 0.55 in winter to 0.71 in summer for Aqua MODIS. However, they also had certain differences. On average, the MODIS-derived TCC was 15.16%higher than the Synop data, and this value was higher at nighttime(15.58%–16.64%) than daytime(12.74%–14.14%). The deviation between the MODIS and Synop TCC had large seasonal variation, being largest in winter(29.53%–31.07%) and smallest in summer(4.46%–6.07%). Analysis indicated that cloud with low cloud-top height and small cloud optical thickness was more likely to cause observation bias. Besides, an increase in the satellite view zenith angle, aerosol optical depth, or snow cover could lead to positively biased MODIS results, and this affect differed among different cloud types. 相似文献
9.
The Cloud Feedback Model Intercomparisons Project (CFMIP) Observation Simulator Package (COSP) is adopted in the Grid-point Atmospheric Model of IAP LASG (GAMIL2) during CFMIP at Phase II to evaluate the model cloud fractions in a consistent way with satellite observations. The cloud simulation results embedded in the Atmospheric Model Intercomparison Project (AMIP) control experiment are presented using three satellite simulators: International Satellite Cloud Climatology Project (ISCCP), Moderate Resolution Imaging Spectroradiometer (MODIS), and Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) lidar onboard the Cloud- Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO). Overall, GAMIL2 can produce horizontal distributions of the low cloud fraction that are similar to the satellite observations, and its similarities to the observations on different levels are shown in Taylor diagrams. The discrepancies among satellite observations are also shown, which should be considered during evaluation. 相似文献
10.
This paper presents a nighttime sea fog detection algorithm incorporating unsupervised learning technique. The algorithm is based on data sets that combine brightness temperatures from the 3.7 μm and 10.8 μm channels of the meteorological imager (MI) onboard the Communication, Ocean and Meteorological Satellite (COMS), with sea surface temperature from the Operational Sea Surface Temperature and Sea Ice Analysis (OSTIA). Previous algorithms generally employed threshold values including the brightness temperature difference between the near infrared and infrared. The threshold values were previously determined from climatological analysis or model simulation. Although this method using predetermined thresholds is very simple and effective in detecting low cloud, it has difficulty in distinguishing fog from stratus because they share similar characteristics of particle size and altitude. In order to improve this, the unsupervised learning approach, which allows a more effective interpretation from the insufficient information, has been utilized. The unsupervised learning method employed in this paper is the expectation–maximization (EM) algorithm that is widely used in incomplete data problems. It identifies distinguishing features of the data by organizing and optimizing the data. This allows for the application of optimal threshold values for fog detection by considering the characteristics of a specific domain. The algorithm has been evaluated using the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) vertical profile products, which showed promising results within a local domain with probability of detection (POD) of 0.753 and critical success index (CSI) of 0.477, respectively. 相似文献
11.
Based on a current fog detection theory, a multiband threshold method for MODIS data was put forward to detect daytime fog in the South China Sea. It used Bands 1, 2, 18, 20 and 31 of MODIS data to separate fog from the cloud and the sea surface. The digital detection indexes were as follows. If RB1<20%, RB2<20% and RB1>RB2, the pixel was identified to be the sea surface. If RB1>55%, RB2>55% and TB31<273 K, the pixel was identified to be a middle- and high-level cloud. If IFC>20, the pixel was classified to be sea fog. The method was verified with sea fog data observed from the coastal region of Guangdong during January-April 2011. Out of the 13 samples of satellite detection, nine were consistent with the surface observations, three were identified to be low-level the cloud according to the satellite detection but fog according to the surface observations, and only one sample was identified to be the ocean surface by the satellite detection but fog by the surface observations. Because the MODIS data cannot penetrate the cloud or fog, the model was designed for a single field of view which had only one layer of cloud or fog. It can accurately distinguish fog which is not covered by the cloud, but it identifies fog as cloud if the former is covered by a cloud. Generally speaking, the model is effective and feasible. 相似文献
12.
On the assimilation of satellite sounder data in cloudy skies in numerical weather prediction models 总被引：1，自引：1，他引：0
Satellite measurements are an important source of global observations in support of numerical weather prediction (NWP). The assimilation of satellite radiances under clear skies has greatly improved NWP forecast scores. However, the application of radiances in cloudy skies remains a significant challenge. In order to better assimilate radiances in cloudy skies, it is very important to detect any clear field-of-view (FOV) accurately and assimilate cloudy radiances appropriately. Research progress on both clear FOV detection methodologies and cloudy radiance assimilation techniques are reviewed in this paper. Overview on approaches being implemented in the operational centers and studied by the satellite data assimilation research community is presented. Challenges and future directions for satellite sounder radiance assimilation in cloudy skies in NWP models are also discussed. 相似文献
13.
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. 相似文献
14.
云是天气与气候变化的重要影响因子,准确估量云顶高度和云量对分析云特性、降水及强天气预报、估算云辐射强迫等都具有重要意义。利用2006-2010年6-8月CloudSat卫星搭载的微波云廓线雷达（CPR,简称微波雷达）和CALIPSO卫星搭载的云-气溶胶偏振激光雷达（CALIOP,简称激光雷达）的探测资料,分析了全球云顶高度及云量的空间分布特征。结果表明,热带地区微波雷达探测云顶高度平均比激光雷达低约4 km,但均超过12 km;副热带洋面云顶高度在4 km以下,且两部雷达探测的云顶高度差异存在地域性。微波雷达对薄云、云砧及云顶高度低于2.5 km的低云存在漏判,对厚云的云顶高度偏低估;微波雷达探测的全球总云量均值为51.1%,比激光雷达少23.3%;两者给出的云量分布也存在显著的海-陆差异,其中洋面云量差异更大,如微波雷达测出局部洋面云量为80%,而激光雷达的探测结果却超过90%。由于激光雷达发射波长短,对云顶微小粒子比较敏感,而微波雷达波长较长,对相对较小粒子的探测存在局限性。因此,激光雷达对云顶高度的探测优于微波雷达。此结果不仅加强了对激光雷达和微波雷达探测原理的认识,而且进一步理解了云的气候特征。 相似文献
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16.
A complex method for analysis of measurement data of the AVHRR radiometer of the NOAA satellite is presented, which allows detecting clouds, classifying their types, detecting precipitation zones, and estimating cloud and precipitation parameters in the daytime the year round in the midlatitudes. Tuning and testing of the method (threshold algorithms of classification) are carried out on the synchronous satellite and surface meteorological and radar data archive for central European Russia in 1998–2006. As a result of validation, characteristics are presented of reliability of satellite estimates of cloud amount, top height, maximum liquid water content in the cloud layer, and precipitation rate. 相似文献
17.
A method is presented for fog and low stratus detection from daytime satellite imagery based on Meteosat 8 SEVIRI (Spinning-Enhanced Visible and Infra-Red Imager) data. With its excellent spatial, spectral and temporal resolutions, this imagery is an ideal basis for operational fog monitoring. The scheme utilizes a range of pixel-based and novel object-oriented techniques to separate fog and low stratus clouds from other cloud types. Fog and low stratus are identified by a number of tests which explicitly and implicitly address fog/low stratus spectral, spatial and microphysical properties. The scheme's performance is evaluated using ground-based measurements of cloud height over Europe. The algorithm is found to detect low clouds very accurately, with probabilities of detection (POD) ranging from 0.632 to 0.834 (for different inter-comparison approaches), and false alarm ratios (FAR) between 0.059 and 0.021. The retrieval of sub-pixel and temporal effects remain issues for further investigation. 相似文献
18.
A cloud optical and microphysical property product for the advanced geosynchronous radiation imager onboard China's Fengyun-4 satellites: The first version 
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下载免费PDF全文 《大气和海洋科学快报》2023,16(3):100337
Fengyun-4 (FY-4), the latest collection of Chinese geostationary meteorological satellites, monitors the Eastern Hemisphere with high spatiotemporal resolutions. This study developed a cloud optical and microphysical property product for the Advanced Geosynchronous Radiation Imager (AGRI) onboard the FY-4 satellites. The product focuses on cloud optical thickness (COT) and cloud effective radius (CER) using a bi-spectral retrieval algorithm, and also includes cloud mask and phase using machine learning (ML) algorithms as prerequisites for COT and CER retrievals. The ML-based algorithm develops four independent models using Random Forest methods for cloud mask, liquid water, ice, and mixed-phase/multi-layer clouds, respectively. A two-habit ice and sphere water cloud model are employed to give their optical properties. Look-up tables of cloud reflectance in the COT and CER sensitive channels are built for efficient forward simulations, and the retrieval is performed by an optimal estimation algorithm. Compared with collocated active observations, the cloud mask and phase results give true positive rates of ∼95% and ∼85% and are more sensitive to mixed-phase clouds. Meanwhile, the AGRI-based COT and CER agree closely with those given by the collocated MODIS and AHI cloud products, and the correlation coefficients between MODIS and the AGRI results are 0.76 and 0.63 for COT and CER, respectively. The COT and CER retrievals will be persistently maintained and improved as the operational product for FY-4/AGRI.摘要风云四号作为中国新一代静止气象卫星, 提供了高时空分辨率的监测产品. 本文介绍风云四号搭载的先进地球同步轨道辐射成像仪AGRI的云光学和微物理特性产品. 该产品包含了基于双光谱通道反演的云光学厚度和云粒子有效半径产品, 以及基于机器学习的云识别和云相态产品. 与时空匹配的主动卫星观测结果对比显示, 该产品的云识别和云相态的准确率分别在95%和85%; 该产品提供的云光学厚度和云有效粒径与经典的MODIS产品的相关系数达到0.76和0.63. 团队将持续优化和更新该云光学和微物理特性定量产品, 服务风云四号卫星定量应用. 相似文献
19.
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。 相似文献
20.
Jung-Rim Lee Chu-Yong Chung Mi-Lim Ou 《Asia-Pacific Journal of Atmospheric Sciences》2011,47(2):113-122
A fog detection algorithm that uses geostationary satellite data has been developed and tested. This algorithm focuses on continuous fog detection since temporal discontinuities, especially at dawn and dusk, are a major problem with current fog detection algorithms that use satellite imagery data. This is because the spectral radiance at 3.7 μm contains overlapping emissive and reflectance components. In order to determine the radiance at 3.7 μm under fog conditions, radiative transfer model simulations were performed. The results showed that the radiance at 3.7 μm obviously varies with the solar zenith angle, and the brightness temperature differences between 3.7 μm and 10.8 μm are completely dissimilar between day and night (positive and varying with the angle during the daytime, but negative and constant at night). In this algorithm, a dynamic threshold is used as a function of the solar zenith angle. Moreover, additional criteria such as infrared, split-window channels, and a water vapor channel are used to remove high-level clouds. Also, the visible reflectance (0.67 μm) channel is used in the daytime algorithm because visible channel images are very practical for confirming a fog area with the high reflectivity and the smooth texture. The clear-sky visible reflectance for the previous 15 days was also employed to eliminate the surface effect that appeared during dawn and dusk. As the results, fog areas were estimated continuously, allowing the lifecycle of the fog system, from its development to decline, to appear obviously in the resulting images. Moreover, the estimated fog areas matched well with surface observations, except in a high latitude region that was covered by thin cirrus clouds. 相似文献