基于多灰度共生矩阵特征值的黄渤海白天海雾识别算法

本文基于多灰度共生矩阵特征值,即相关性、对比度、同质性和能量,进行联合海雾遥感判识,提出一种高准确率黄渤海白天海雾识别算法。采用第二代静止气象卫星FY-4A可见光、近红外和红外数据,将该算法应用于黄渤海区域白天海雾判识,并利用2019—2020年沿黄渤海气象站点能见度实测数据及CALIPSO卫星数据产品对本算法识别结果进行精度验证。结果表明：海雾识别平均检测率(POD)为92%,误报率(FAR)为27%,临近成功指数(CSI)为69%,可以实现对海雾的动态监测,为海上交通等领域提供较好的数据支持。     

Fog Detection over China’s Adjacent Sea Area by using the MTSAT Geostationary Satellite Data

A fog threshold method for the detection of sea fog from Multi-function Transport Satellite (MTSAT1R) infrared (IR) channel data is presented.This method uses principle component analysis (PCA),texture analysis,and threshold detection to extract sea fog information.A heavy sea fog episode that occurred over China’s adjacent sea area during 7 8 April 2008 was detected,indicating that the fog threshold method can effectively detect sea fog areas nearly 24 hours a day.MTSAT-1R data from March 2006,June 2007,and April 2008 were processed using the fog threshold method,and sea fog coverage information was compared with the meteorological observation report data from ships.The hit rate,miss rate,and false alarm rate of sea fog detection were 66.1%,27.3%,and 33.9%,respectively.The results show that the fog threshold method can detect the formation,evolution,and dissipation of sea fog events over period of time and that the method has superior temporal and spatial resolution relative to conventional ship observations.In addition,through MTSAT-1R data processing and a statistical analysis of sea fog coverage information for the period from 2006 to 2009,the monthly mean sea fog day frequency,spatial distribution and seasonal variation characteristics of sea fog over China’s adjacent sea area were obtained.     

利用动态阈值方法改进的风云二号卫星海雾检测技术

有效的观测是提高对海雾认知和预报水平的关键因素,卫星数据是当前最可行的观测数据源,但需要高质量的观测数据和精细的检测技术。本文为提高风云二号海雾检测水平,在现有卫星观测数据条件下借鉴了动态获取云雾阈值的思想,定制设计了一套从获取动态检测阈值到温度、纹理、噪声检测等步骤的黄渤海海雾检测方法流程。对黄渤海白天海雾检测结果的检验表明,虽然对于秋、冬非海雾季月份的效果还有待提高,但在春季海雾季已接近国际同类产品水平。同时该技术方法也需要继续搜集实例,进一步优化阈值获取方案。     

基于深度学习的卫星多通道图像融合的海雾监测处理方法

海雾无论在海上还是在沿岸地带，都因其恶劣的能见度对交通运输、海洋捕捞和海洋开发工程以及军事活动等造成不良影响，因此对于海雾的实时监测和预报就显得尤为重要。本文提出了基于深度学习的静止气象卫星多通道图像融合分割算法，使用D LinkNet深度卷积神经网络语义分割算法模型对黄渤海海域范围的16通道、空间分辨率为0.5 km的Himawari 8卫星数据进行研究。分别采用均交并比（mIOU）以及观测值检验作为评价指标，在测试集上的mIOU为0.9436，并且用卫星测试数据结果与海上观测数据结果进行对比，得出雾区准确率（检测有雾且真实有雾/检测有雾）为66.5%，雾区识别率（检测有雾且真实有雾/（真实有雾-云覆盖））为51.9%，检测正确率(检测正确/总样本)93.2%。本文提出的方法能为海雾监测提供一个可靠的参考。     

Fog detection using geostationary satellite data: Temporally continuous algorithm

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.     

A comparison of satellite-retrieved and simulated cloud coverage in the Baltic Sea area as part of the BALTIMOS project

A cloud-detection algorithm for METEOSAT first generation data has been developed. The algorithm utilizes solely infrared data from the METEOSAT thermal infrared window channel at around 11.5 μm. The developed algorithm estimates an assumed clear-sky brightness temperature from time series analysis on pixel bases. Land-/sea-depending dynamic thresholds are then utilized discriminating the infrared images in cloudy, undecided, and cloud free pixels. The cloud-detection algorithm has been validated against synoptic observations. The developed cloud-detection scheme has been applied to 10 years (1992–2001) of METEOSAT data, extracting cloud coverage statistics for the Baltic Sea catchment area. These have been compared to corresponding cloud coverage statistics derived from the BALTIMOS coupled model system. Building overall averaged values of the cloud coverage in the period from 1999 to 2001 gives results with very good agreement between simulation and observation: the total METEOSAT-derived cloud coverage amounts to 0.65 compared to 0.63 for BALTIMOS. In contrast, large discrepancies in the phase of the diurnal cycle of cloud coverage have been observed. A significant trend in total cloud amount was observed neither from the model nor from the satellite.     

DETECTION OF DAYTIME FOG IN SOUTH CHINA SEA USING MODIS DATA

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.     

南海西北部一次海上海雾的微物理及化学特性分析

利用2017年3月MICAPS资料、欧洲气象中心再分析ERA-Interim数据及在南海西北部海上的海雾观测数据,分析了南海西北部一次海上海雾的微物理特征和雾水化学特性,并将海上海雾与南海岸边海雾进行对比分析。结果表明:此次海雾为南海偏南暖气流移至冷海面发生冷却并达到饱和而形成。海雾过程中气压与气温变化趋势相反,相对湿度不断增加,雾滴数浓度、液态含水量和平均直径的平均值分别为198 cm-3、0.116 g/cm3和5.6 μm。相比广东湛江东海岛和广东茂名博贺地区岸边海雾个例,本次海上海雾水汽充足,雾滴偏大。本次海雾属于酸性海雾,pH值变化范围为2.51~3.50,海雾后的雨水样本pH值则为4.05。海雾发生初期电导率比其它阶段高很多,说明海雾发生的初始阶段雾水溶解了大量的气溶胶。海上雾水中Na+和Cl-浓度最高,浓度分别为32 535 μmol/L和53 466 μmol/L,K+浓度远高于Mg2+和Ca2+,而东海岛岸边海雾相反。      

Improved fog detection from satellite in the presence of clouds

This study analyzes radiative effect of the higher clouds over the fog layer and presents the improvement of fog detection over the Korean peninsula, utilizing satellite data of the Multi-functional Transport SATellite (MTSAT)-1R and the MODerate resolution Imaging Spectroradiometer (MODIS) and the Look-Up Table (LUT) based on Radiative Transfer Model (RTM) simulations. Fog detection utilizing the satellite data from visible (0.68 µm) and infrared (3.75 µm and 10.8 µm) channels has been evaluated in comparison with ground-based observations over 52 meteorological stations in the Korean Peninsula from March 2006 to February 2007. The threshold values for fog sensing have been derived from the difference (i.e., T_3.7–11) in brightness temperature between 3.75 µm (T_3.7) and 10.8 µm (T₁₁) during day and night, and also from the reflectivity at 0.68 µm (R_0.68) in the daytime. In the twilight, however, the difference between the temperature values at 10.8 µm and their maximum within previous 15 days (i.e., T_11max-11) are used instead, because the 3.75 µm channel is inaccurate for the fog detection at dawn/dusk. The sensitivity of the T_3.7–11 values with respect to the clouds is investigated based on the cloud variables such as its height, optical thickness, and amount. The values of T_3.7–11 are the most sensitive to cloud height, followed by cloud optical thickness and effective radius, while R0.68 is insensitive to cloud height. The sensitivity is examined with various conditions of cloud phases and day/night. Sixteen cases among eighteen fog occurrences, which have been unable to be sensed by using only the conventional threshold values, are successfully detected with the additional LUT corrections, indicating a significant improvement. The method of fog detection in this study can be useful to the Communication, Ocean, and Meteorological Satellite (COMS) Meteorological Data Processing System (CMDPS) by reducing the cloud effect on fog sensing.     

An observational and modeling study of a sea fog event over the Yellow Sea on 1 August 2003

A dense sea fog episode that occurred near the coastal city of Qingdao in the Shandong Peninsula of China on 1 August 2003 is investigated by using all of the available observational data and high-resolution modeling results from the Regional Atmospheric Modeling System (RAMS). This fog event reduced the horizontal visibility to be less than 60 m in some locations and caused several traffic accidents locally. In this paper, all of the available observational data, including visible satellite imagery of Geostationary Operational Environmental Satellite (GOES)-9 and MODerate-resolution Imaging Spectroradiometer (MODIS), objectively reanalyzed Final Analysis (FNL) data issued by the National Centers for Environmental Prediction (NCEP), sounding data at the Qingdao and Dalian stations, and the latest 4.4 version of the RAMS model, were employed to study this sea fog case. We begin with the analyses of the environmental conditions of the sea fog event, including the large-scale conditions, the difference between T _2m (air temperature at 2 m altitude) and sea surface temperature (SST), and the atmospheric sounding profiles of the two stations. The characteristics of this sea fog event was documented by using visible satellite imagery of GOES-9 and MODIS. In order to better understand the fog formation mechanism, a high-resolution RAMS model of dimensions 4 km × 4 km was designed, which was initialized and validated by FNL data. A 54-h modeling period that started from 18 UTC 31 July 2003 reproduced the main characteristics of this sea fog event. The simulated lower visibility area agreed well with the sea fog area identified from the satellite imagery. It is shown that advection cooling effect plays a significant role in the fog formation.     

珠江口附近春季一次海雾的观测分析及三维数值模拟

为了探讨南海春季海雾的成因及数值模式的预报能力,利用边界层观测资料对2006年3月21—22日珠江口附近春季一次海雾的形成和发展过程进行了分析,然后用WRF中尺度模式对该次海雾过程进行了三维数值模拟。观测表明,该次海雾属于北方冷空气南下到达南海,然后减弱消失,南海北部受到偏南暖湿气流影响后迅速增暖,冷海面与近海面的暖湿空气相互作用而形成的一次平流冷却雾。模拟结果显示,模式模拟的这次海雾形成时间和发展演变过程与观测非常相近,模拟的海雾空间变化、边界层大气层结变化、地面感热通量变化以及海雾形成原因与观测事实均比较吻合,数值模式对这次海雾过程表现出了较高的模拟能力。这次海雾的形成和发展主要与冷的下垫面、暖湿空气的影响和近地面稳定的大气层结有关。     

2015-04-28渤海海雾形成过程中的海气相互作用分析

利用FY和MTSAT卫星资料、ERA Interim再分析资料、黄渤海浮标站资料、黄渤海自动站逐小时观测资料,对发生在2015年4月28—29日的渤海海雾成因进行分析,着重探讨了海雾形成过程中的海气相互作用。结果发现,近海面处大气低层逆温层抬升,大于90%的大湿度区向上、向西扩展,对海雾形成非常有利;海雾生成前、生成发展过程中存在明显的东到东南风,有利于黄海水汽向渤海输送,海面上空有水汽通量大值区由渤海海峡向渤海中部移动,使得渤海上空水汽输送加强,提供了海雾形成所需的水汽;在海雾形成过程中渤海上空气温高于海温,风切变造成的海气界面湍流热交换为大气输送向海洋,使得冷海面上空暖湿空气降温冷却达到饱和形成海雾,是平流冷却雾。      

黄渤海一次持续性大雾过程特征和成因分析

利用日本MTSAT1R卫星数据、常规地面和高空观测数据、NCEP FNL客观再分析资料和NEARGOOS（NorthEast Asian Regional Global Ocean Observing System）的海表温度（SST）数据,分析了2010年5月31日至6月5日发生在黄渤海及周边地区的一次持续性海雾天气的形成、维持、消散特征及其物理机制。结果表明：大雾形成前低层水汽非常充沛,入海变性冷高压的稳定维持为这次持续性海雾过程提供了有利的背景条件,海雾在夜间辐射冷却作用下形成;大雾期间黄渤海     

基于决策树算法的海州湾地区海雾预测北大核心

基于连云港西连岛站点2014—2018年逐小时气象观测资料,经过对海雾事件及气象要素特征的统计分析探寻海州湾海雾发生发展的基本规律,并基于机器学习中的经典的C4.5算法对海雾天气建立气象要素预测模型。结果表明:基于C4.5算法的决策树预测模型能够较为直观准确的对海州湾海雾进行预测,并且该决策树模型具有较高的泛化能力。利用2014—2017年的样本数据进行学习,模型的学习准确率为92.85%,利用2018年的样本数据对模型的泛化能力进行测试,测试准确率为93.51%。决策树算法在海雾预测中具有方便简洁、科学实用,准确率高等特点。     

下垫面热力作用对黄海春季海雾的影响——观测与数值试验

利用海上浮标站、高分辨率数字式探空仪等多种观测手段和中尺度模式WRF,对2008年5月2—3日黄海发生的一次海雾过程进行了观测分析和数值模拟。观测表明,出现海雾时,气温明显下降,气海温差(海表面以上2 m气温减海表面以下1m水温)减小,不足0.5℃,浓雾时,甚至出现海温(SST)高于气温的现象。较强的湍流活动出现在大气边界层低层150 m以下。反映了低层大气稳定性减弱,可能有利于海雾的维持。海雾消散阶段,海气温差明显加大,湍流强度减弱,湍流发展高度升高。海雾过程中,可能存在动量下传的局地海-气相互作用机制,SST的升高可使雾中能见度好转。数值模拟的结果与观测基本一致,雾区内的气海温差明显小于雾区外,敏感性试验进一步表明:100 m以下气层稳定性和湍流发展条件对SST的变化敏感。SST的变化对稳定度的影响和对雾区范围的影响与近海面的水汽含量有关:在湿度较小(q0.5 g/kg)的薄海雾区,SST增加1℃,稳定度明显减弱(θ_v/z≤0.01 K/m),海雾面积缩小;SST下降1℃,稳定度增加(θ_v/z≥0.07 K/m),薄海雾面积增大。在湿度较大(q0.6 g/kg)的浓海雾区,SST的变化对静力稳定度的影响不大,海雾仍然维持。因此,当海气温差减小,甚至出现SST高于气温时,如果仍然有海雾,则一般是水汽含量比较大的浓海雾。该结果有助于对海雾形成机制的认识。     

基于MTSAT卫星遥感监测的浙江省及周边海区大雾分布特征

利用日本静止气象卫星MTSAT逐时资料,综合地面气象观测数据,对浙江省及其周边海区陆地和海上2008—2012年的大雾进行了专题信息提取,并给出了浙江省陆域、周边海域0.05°×0.05°网格点的小时尺度的遥感大雾产品,结果表明:(1)基于MTSAT卫星观测数据,采用分级判识太阳高度角阈值和归一化大雾指数的方法,构建的浙江及其周边地区陆地和海上遥感大雾监测模型,大雾判识精度总体超过75%,基本满足使用需求。(2)浙江省陆域近5年大雾年平均累计为411.7 h,约占全年的4.7%,基本呈南多北少,山区多平原少的格局,其中浙江南部高山区、舟山和温州部分海岛及西部山区为大雾多发区,且大雾季节分布为冬秋季较多,春夏季较少,22时至09时是浙江省陆域大雾的高发时段,10时以后大雾逐渐消散,至后半夜、凌晨前后,大雾频次逐渐增多。(3)研究区海雾主要发生在大陆近海,呈现由近海向外海减少的空间格局,东海海域年大雾累计为311.7 h,以东海西南部地区大雾出现最多,浙江省沿海大雾的高发区位于温州海域及钱塘江口。研究区海域大雾具有明显的季节特征,主要表现为春季较多,夏季次之,秋冬季较少的分布格局,且海上主要受平流雾影响,大雾不易消散,持续时间较长。从各海区大雾发生频次从高到低依次为:东海东南部、台湾以东洋面、东海中东部、黄海西南部、东海中西部、台湾海峡、东海西北部、黄海东南部、东海西南部和东海东北部。     

一次黄海海雾的数据同化试验与形成机制研究

选取2012年4月14日的一次存在东西2片雾区的黄海春季海雾为研究对象,借助WRF（Weather Research and Forecasting）模式,采用循环3DVAR（3-Dimensional Variational）数据同化方案,考虑了湿度控制变量的背景误差协方差CV6,进行了AIRS（Atmospheric Infrared Sounder）卫星温度与湿度廓线数据的同化试验,并基于同化试验结果探讨了此次海雾的形成机制。同化试验结果表明：同化 AIRS 卫星温度与湿度廓线数据后,模式能成功再现海雾的形成过程,特别是东西2片雾区之间的晴空区的存在,这归功于AIRS数据的同化能够显著改善海上大气边界层的温湿结构、影响海雾的低层高压的范围与强度;机制分析揭示：东西2片雾均为典型的平流冷却雾,但二者厚薄和气团来源不同;海上高压控制黄海西岸陆地的暖空气入海,受低海温的冷却作用降温先形成逆温层,然后逆温层底部生成了较薄的西侧雾区;来自黄海中部的空气向东北移动至朝鲜半岛西部海域,高压下沉增温形成一个顶部较高的稳定层,从而生成较厚的东侧雾区;高压中心下沉区内,近海面风速小使得机械湍流弱,空气增温与海温暖舌共同作用下使得近海面气海温差小,海雾无法生成导致了晴空区的存在。     

Predictability of the Meteorological Conditions Favourable to Radiative Fog Formation During the 2011 ParisFog Campaign

Radiative fog formation is a complex phenomenon involving local physical and microphysical processes that take place when particular meteorological conditions occur. This study aims at quantifying the ability of a regional numerical weather model to analyze and forecast the conditions favourable to radiative fog formation at an instrumental site in the Paris area. Data from the ParisFog campaign have been used in order to quantify the meteorological conditions favorable to radiative fog formation (pre-fog conditions) by setting threshold values on the key meteorological variables driving this process: 2-m temperature tendency, 10-m wind speed, 2-m relative humidity and net infrared flux. Data from the ParisFog observation periods of November 2011 indicate that use of these thresholds leads to the detection of 87 % of cases in which radiative fog formation was observed. In order to evaluate the ability of a regional weather model to reproduce adequately these conditions, the same thresholds are applied to meteorological model fields in both analysis and forecast mode. It is shown that, with this simple methodology, the model detects 74 % of the meteorological conditions finally leading to observed radiative fog, and 48 % 2 days in advance. Finally, sensitivity tests are conducted in order to evaluate the impact of using larger time or space windows on the forecasting skills.     

基于FY-3B卫星资料的中国南海海区1—3月海雾时空分布特征研究

为全面了解中国南海海区海雾的分布特征,为南海海雾气象服务提供基础背景资料,利用2011—2016年1—3月FY-3B气象卫星资料的雾监测产品,分析了中国南海海区海雾的时空分布特征。结果表明：中国南海海雾具有特定的区域特征,中国南海海雾多出现在华南沿海、北部湾沿海、琼州海峡和海南岛东北部沿海海区,南海南部海域出现海雾概率低;南海出现高频次海雾的时间多发生在2月,1月次之,3月最少。该研究结果可为中国南海海雾研究提供背景资料。     

黄海海雾天气气候特征及其成因分析

利用2002-2008年的地面观测资料、青岛小浮标站资料、海洋大气综合资料、NCEP再分析资料和卫星云图等资料,对黄海海雾的天气气候特征、环流型和相关的水文、气象因子进行分析研究.结果表明,黄海海雾呈现逐年递增的年际变化,且集中发生在春夏季,自南向北雾季逐渐推迟,发生频数增多；一日中主要出现在夜间至早晨.黄海海雾可归纳为冷锋型、高压后部型和均压场型三种天气类型.海雾是流入黄海的黑潮暖流分支与沿岸的冷水流相遇,在适宜的海温、气海温差、大气稳定度和风场等水文、气象条件下形成的,这些条件可为海雾发生、发展的预报提供一些参考依据.