基于MODIS数据的台湾海峡SST区域遥感监测模型研究

采用SeaDAS模型开展基于MODIS数据的台湾海峡海洋表面温度SST遥感监测时,发现SeaDAS模型在台湾海峡中部SST的监测精度能够满足要求,但在台湾海峡近岸SST的监测误差明显偏大。为此根据2003—2006年台湾海峡近岸观测站点和台湾海峡中部浮标的实测SST数据,采用线性多通道算法建立台湾海峡SST区域遥感监测统计模型,并选择2007年30个SST数据样本对区域统计模型的监测效果进行验证分析。结果表明：在台湾海峡海域采用SeaDAS模型监测SST绝对误差的平均值是1.2 ℃,标准差是0.69 ℃,而采用区域统计模型监测SST绝对误差的平均值下降到0.89 ℃,标准差下降到0.52 ℃,区域统计模型优于SeaDAS模型。     

基于非线性算法的FY 3A/VIRR SST反演

利用非线性算法实现了FY-3A/VIRR数据的海洋表面温度SST产品的反演.对2010年的全球船舶站观测数据和FY-3A/VIRR数据建立匹配数据集,选择单月的匹配数据采用多元回归模型计算得到了适用于FY-3A/VIRR数据的非线性海表温度反演算法NLSST的系数,能够实现FY-3A/VIRR数据的高精度SST产品反演.并利用独立于反演算法的双月匹配数据采用最小绝对偏差方法通过线性模型对SST算法的精度进行检验,结果显示白天和夜间的偏差分别为0.05℃和-0.05℃,绝对偏差在0.50℃以下,标准偏差在0.65℃以下.通过文中实现的算法反演了VIRR数据的SST产品,并和MODIS的官方产品进行比较,结果显示两种SST产品具有很高的一致性.     

基于模糊评价的福建沿海水质卫星遥感监测模型

卫星遥感技术对于开展连续、实时、大尺度的海洋水质状况监测具有明显优势。该文充分利用卫星遥感的标准算法模型和半分析算法模型分别反演得到叶绿素a、颗粒状有机碳、黄色物质和透明度等海洋生态参数,并选取与福建沿海海水受污染程度密切相关的溶解氧、化学耗氧量、无机氮和活性磷酸盐作为海洋水质的评价因子,通过建立海洋生态参数与海洋水质评价因子两者之间的统计关系模型,在海洋水质综合评价中引入模糊综合评价法,最终建立一套基于卫星遥感和模糊评价的海洋水质监测模型,并利用2009—2013年福建沿海同步获取的海洋水质现场实测数据对模型的反演精度进行验证。结果表明:使用该监测模型开展对福建沿海海洋水质卫星遥感监测是可行的,监测准确率为81%,具有较好的业务化应用前景,由于模型对于Ⅳ类海洋水质监测的准确率明显高于Ⅱ类和Ⅲ类海洋水质,因此,比较适合于福建近岸海域的海洋水质监测。     

基于大气辐射传输模型的单通道海表温度反演算法研究

在分析大气辐射传输模型基础上,提出了单通道海表温度反演算法。首先,利用像元尺度的大气廓线精确计算大气上行辐射、下行辐射和透过率,然后将这些参数与海水比辐射率代入大气红外辐射传输方程,求解海表温度。与传统的劈窗算法相比,该算法具有时空变化适应性强的特点,尤其适用于缺乏浮标数据的海域。为了验证算法的可行性和准确性,利用2009年全年的MODIS数据在美国西海岸海域进行试验。通过与浮标实测数据比对可发现,单通道海表温度反演算法与浮标实测值的标准方差为0.73 ℃,平均偏差为0.23 ℃,说明该算法具有较高的精度,能满足常规的业务观测需求,另外也可用于标定劈窗算法的系数。      

台湾海峡海水透明度遥感监测及时空变化分析

台湾海峡水文气象环境条件及水体光学特性较复杂。本文基于海洋水体固有光学特性和MODIS卫星数据建立了台湾海峡海水透明度的遥感反演模型,利用2005—2012年福建省近岸海域同步获取的海水透明度实测数据对模型的反演精度进行验证,并根据多年遥感反演结果初步分析了台湾海峡海水透明度的分布特点和变化规律。结果表明:台湾海峡遥感反演的海水透明度与实测的海水透明度具有较好的一致性,两者相关系数为0.849,平均相对误差为26.0%,平均均方根误差为0.332m,在中高透明度海区反演误差更低,因此利用建立的遥感反演模型对台湾海峡海水透明度进行卫星遥感监测是可行的。从多年平均的遥感监测结果来看,台湾海峡一般沿岸浅海的海水透明度较低,外海的海水透明度较高;海峡南部的海水透明度明显高于北部,澎湖列岛东南海域海水透明度常年较大;春季和夏季台湾海峡透明度高的海域面积明显比秋季和冬季大,海峡东南部的海水高透明度区春夏季比秋冬季更向北伸展。     

海洋叶绿素a浓度反演及其在赤潮监测中的应用

采用OC2和OC3两种标准经验算法以及Clark和NSMC-CASE2两种半分析算法进行了MODIS海洋叶绿素a浓度反演, 并根据2004年福建近海赤潮监控区内10个站点的叶绿素a浓度观测数据对反演结果进行了分析。 利用2002—2005年MODIS叶绿素a浓度反演结果对同期发生在福建近海的赤潮灾害进行了初步研究, 并探讨了250 m 和500 m 分辨率的MODIS可见光数据对赤潮灾害监测的可能性。 结果表明: 两种标准经验算法和两种半分析算法对叶绿素a浓度的反演均存在不同程度的偏高, 相对而言, OC3标准经验算法比较适合基于MODIS的福建近海叶绿素a浓度反演; MODIS红光 (250 m) 和绿光 (500 m) 通道数据的比值在赤潮灾害发生过程中发生了显著变化, 在灾害发生时其值明显较灾前和灾后均偏大。     

基于IDL的MODIS1B数据SST反演

海表温度是重要的海洋环境参数。MODIS(Moderate Resolution Imaging Spectrora-diometer)具有强大的海洋信息探测功能。采用多通道分裂窗算法,通过交互式数据语言IDL编程,实现了直接利用MODIS1B数据进行海表温度(SST)反演,并将反演结果存贮为标准的HDF文件以供其他软件使用。该方法能够适应对整条轨道的MODIS1B数据进行快速、实时和自动处理,大大节省人力资源,同时提高遥感产品数据的分发和共享能力。     

青藏高原MODIS地表反照率和GLASS地表反照率的对比分析

对比分析了青藏高原MODIS地表反照率产品和GLASS地表反照率产品的空间分布连续性、高质量反演结果的比例,应用青藏高原CAMP/Tibet试验期间的高精度观测数据评估了两种产品的精度,通过人工目视解译MODIS地表反射率图像并结合MODIS积雪产品分析了影响两种产品精度的原因,结果表明：1）GLASS地表反照率产品具有比MODIS地表反照率产品更好的空间分布连续性和更高的反演质量;2）绝大多数时段内两种产品都能与地面观测结果保持较好的一致性,能准确地反映地表反照率的异常变化过程;3）局地积雪是影响两种产品精度的重要因素之一;4）积雪条件下,GLASS地表反照率反演算法比MODIS地表反照率反演算法更具优势。研究结果有助于促进人们对地表反照率卫星遥感反演产品的认识,改进青藏高原地表反照率卫星遥感反演算法,提高青藏高原地表反照率卫星遥感反演结果的精度、反演质量和空间分布连续性。     

基于MODIS数据地表温度反演劈窗算法的比较研究

选取QIN和SOB两种代表性劈窗算法对辽宁地区地表温度进行反演,并分析二者的精度和误差分布。结果表明：QIN和SOB算法反演的地表温度（TS）与地面气象台站准同步观测的气温和地温的线性拟合显著,SOB算法线性拟合更好;从误差分布直方图上看,两种算法的反演结果与地温更接近,SOB算法与同步气温和地温在±2 ℃之间的误差比例略高于QIN算法;在野外开展与卫星遥感空间尺度一致的地表温度观测试验,QIN和SOB算法与实测值的平均绝对误差均为1.5 ℃;与NASA官网发布的地表温度产品对比发现,QIN和SOB算法的平均绝对误差分别为1.75 ℃、1.70 ℃;因此QIN、SOB算法在辽宁地区均适用,SOB算法误差更小。     

基于HJ-1B卫星数据的地表温度反演方法研究

以典型岩溶地貌区为研究区,HJ-1B遥感数据为数据源,通过分别采用覃志豪单窗算法、普适性单通道算法、基于影像的Artis反演算法,并对其中的经验关系式进行修订,最后反演出研究区的地表温度,与MODIS温度产品(MOD11_L2)进行对比分析,探寻适用于岩溶地貌区利用HJ卫星遥感数据进行干旱监测的地表温度反演算法。结果表明,修订后的普适性单通道算法优于其他两种算法,其与MODIS温度产品平均温差相差0.36 K,反演精度达到1 K之内,说明该算法经过修订后适用于反演岩溶地貌区的地表温度。     

CHARACTERISTICS AND TRENDS OF CLIMATIC EXTREMES IN CHINA DURING 1959–2014

The spatial and temporal variations of daily maximum temperature(Tmax), daily minimum temperature(Tmin), daily maximum precipitation(Pmax) and daily maximum wind speed(WSmax) were examined in China using Mann-Kendall test and linear regression method. The results indicated that for China as a whole, Tmax, Tmin and Pmax had significant increasing trends at rates of 0.15℃ per decade, 0.45℃ per decade and 0.58 mm per decade,respectively, while WSmax had decreased significantly at 1.18 m·s~(-1) per decade during 1959—2014. In all regions of China, Tmin increased and WSmax decreased significantly. Spatially, Tmax increased significantly at most of the stations in South China(SC), northwestern North China(NC), northeastern Northeast China(NEC), eastern Northwest China(NWC) and eastern Southwest China(SWC), and the increasing trends were significant in NC, SC, NWC and SWC on the regional average. Tmin increased significantly at most of the stations in China, with notable increase in NEC, northern and southeastern NC and northwestern and eastern NWC. Pmax showed no significant trend at most of the stations in China, and on the regional average it decreased significantly in NC but increased in SC, NWC and the mid-lower Yangtze River valley(YR). WSmax decreased significantly at the vast majority of stations in China, with remarkable decrease in northern NC, northern and central YR, central and southern SC and in parts of central NEC and western NWC. With global climate change and rapidly economic development, China has become more vulnerable to climatic extremes and meteorological disasters, so more strategies of mitigation and/or adaptation of climatic extremes,such as environmentally-friendly and low-cost energy production systems and the enhancement of engineering defense measures are necessary for government and social publics.     

Could the Recent Taal Volcano Eruption Trigger an El Ni?o and Lead to Eurasian Warming?

正The Taal Volcano in Luzon is one of the most active and dangerous volcanoes of the Philippines. A recent eruption occurred on 12 January 2020(Fig. 1a), and this volcano is still active with the occurrence of volcanic earthquakes. The eruption has become a deep concern worldwide, not only for its damage on local society, but also for potential hazardous consequences on the Earth's climate and environment.     

ANALYSIS OF “BIMODAL PATTERN” STORM ACTIVITY CHARACTERISTICS OF THE BAY OF BENGAL

Storms that occur at the Bay of Bengal (BoB) are of a bimodal pattern, which is different from that of the other sea areas. By using the NCEP, SST and JTWC data, the causes of the bimodal pattern storm activity of the BoB are diagnosed and analyzed in this paper. The result shows that the seasonal variation of general atmosphere circulation in East Asia has a regulating and controlling impact on the BoB storm activity, and the “bimodal period” of the storm activity corresponds exactly to the seasonal conversion period of atmospheric circulation. The minor wind speed of shear spring and autumn contributed to the storm, which was a crucial factor for the generation and occurrence of the “bimodal pattern” storm activity in the BoB. The analysis on sea surface temperature (SST) shows that the SSTs of all the year around in the BoB area meet the conditions required for the generation of tropical cyclones (TCs). However, the SSTs in the central area of the bay are higher than that of the surrounding areas in spring and autumn, which facilitates the occurrence of a “two-peak” storm activity pattern. The genesis potential index (GPI) quantifies and reflects the environmental conditions for the generation of the BoB storms. For GPI, the intense low-level vortex disturbance in the troposphere and high-humidity atmosphere are the sufficient conditions for storms, while large maximum wind velocity of the ground vortex radius and small vertical wind shear are the necessary conditions of storms.     

LOW-FREQUENCY CIRCULATION AND EXTENDED-RANGE FORECAST IN CONNECTION WITH AUTUMN EXTREME HEAVY RAINFALL PROCESS IN HAINAN

Observed daily precipitation data from the National Meteorological Observatory in Hainan province and daily data from the National Centers for Environmental Prediction/National Center for Atmospheric Research (NCEP/NCAR) reanalysis-2 dataset from 1981 to 2014 are used to analyze the relationship between Hainan extreme heavy rainfall processes in autumn (referred to as EHRPs) and 10–30 d low-frequency circulation. Based on the key low-frequency signals and the NCEP Climate Forecast System Version 2 (CFSv2) model forecasting products, a dynamical-statistical method is established for the extended-range forecast of EHRPs. The results suggest that EHRPs have a close relationship with the 10–30 d low-frequency oscillation of 850 hPa zonal wind over Hainan Island and to its north, and that they basically occur during the trough phase of the low-frequency oscillation of zonal wind. The latitudinal propagation of the low-frequency wave train in the middle-high latitudes and the meridional propagation of the low-frequency wave train along the coast of East Asia contribute to the ‘north high (cold), south low (warm)’ pattern near Hainan Island, which results in the zonal wind over Hainan Island and to its north reaching its trough, consequently leading to EHRPs. Considering the link between low-frequency circulation and EHRPs, a low-frequency wave train index (LWTI) is defined and adopted to forecast EHRPs by using NCEP CFSv2 forecasting products. EHRPs are predicted to occur during peak phases of LWTI with value larger than 1 for three or more consecutive forecast days. Hindcast experiments for EHRPs in 2015–2016 indicate that EHRPs can be predicted 8–24 d in advance, with an average period of validity of 16.7 d.     

AN ATTRIBUTION ANALYSIS OF CHANGES IN POTENTIAL EVAPOTRANSPIRATION IN THE BEIJING–TIANJIN–HEBEI REGION UNDER CLIMATE CHANGE

Based on the measurements obtained at 64 national meteorological stations in the Beijing–Tianjin–Hebei (BTH) region between 1970 and 2013, the potential evapotranspiration (ET0) in this region was estimated using the Penman–Monteith equation and its sensitivity to maximum temperature (Tmax), minimum temperature (Tmin), wind speed (Vw), net radiation (Rn) and water vapor pressure (Pwv) was analyzed, respectively. The results are shown as follows. (1) The climatic elements in the BTH region underwent significant changes in the study period. Vw and Rn decreased significantly, whereas Tmin, Tmax and Pwv increased considerably. (2) In the BTH region, ET0 also exhibited a significant decreasing trend, and the sensitivity of ET0 to the climatic elements exhibited seasonal characteristics. Of all the climatic elements, ET0 was most sensitive to Pwv in the fall and winter and Rn in the spring and summer. On the annual scale, ET0 was most sensitive to Pwv, followed by Rn, Vw, Tmax and Tmin. In addition, the sensitivity coefficient of ET0 with respect to Pwv had a negative value for all the areas, indicating that increases in Pwv can prevent ET0 from increasing. (3) The sensitivity of ET0 to Tmin and Tmax was significantly lower than its sensitivity to other climatic elements. However, increases in temperature can lead to changes in Pwv and Rn. The temperature should be considered the key intrinsic climatic element that has caused the "evaporation paradox" phenomenon in the BTH region.     

Revisiting the Concentration Observations and Source Apportionment of Atmospheric Ammonia

正While China’s Air Pollution Prevention and Control Action Plan on particulate matter since 2013 has reduced sulfate significantly, aerosol ammonium nitrate remains high in East China. As the high nitrate abundances are strongly linked with ammonia, reducing ammonia emissions is becoming increasingly important to improve the air quality of China. Although satellite data provide evidence of substantial increases in atmospheric ammonia concentrations over major agricultural regions, long-term surface observation of ammonia concentrations are sparse. In addition, there is still no consensus on     

COMPARATIVE ANALYSIS OF VARIOUS DATA OF AIR–SEA TEMPERATURE DIFFERENCE AND ITS VARIATION ACROSS SOUTH CHINA SEA IN THE PAST 35 YEARS

Using the International Comprehensive Ocean-Atmosphere Data Set(ICOADS) and ERA-Interim data, spatial distributions of air-sea temperature difference(ASTD) in the South China Sea(SCS) for the past 35 years are compared,and variations of spatial and temporal distributions of ASTD in this region are addressed using empirical orthogonal function decomposition and wavelet analysis methods. The results indicate that both ICOADS and ERA-Interim data can reflect actual distribution characteristics of ASTD in the SCS, but values of ASTD from the ERA-Interim data are smaller than those of the ICOADS data in the same region. In addition, the ASTD characteristics from the ERA-Interim data are not obvious inshore. A seesaw-type, north-south distribution of ASTD is dominant in the SCS; i.e., a positive peak in the south is associated with a negative peak in the north in November, and a negative peak in the south is accompanied by a positive peak in the north during April and May. Interannual ASTD variations in summer or autumn are decreasing. There is a seesaw-type distribution of ASTD between Beibu Bay and most of the SCS in summer, and the center of large values is in the Nansha Islands area in autumn. The ASTD in the SCS has a strong quasi-3a oscillation period in all seasons, and a quasi-11 a period in winter and spring. The ASTD is positively correlated with the Nio3.4 index in summer and autumn but negatively correlated in spring and winter.     

Analysis of Atmospheric Boundary Layer Height Characteristics over the Arctic Ocean Using the Aircraft and GPS Soundings

正ERRATUM to: Atmospheric and Oceanic Science Letters, 4(2011), 124-130 On page 126 of the printed edition (Issue 2, Volume 4), Fig. 2 was a wrong figure because the contact author made mistake giving the wrong one. The corrected edition has been updated on our website. The editorial office is sincerely sorry for any     

Index to Vol.31

正AN Junling;see LI Ying et al.;(5),1221—1232AN Junling;see QU Yu et al.;(4),787-800AN Junling;see WANG Feng et al.;(6),1331-1342Ania POLOMSKA-HARLICK;see Jieshun ZHU et al.;(4),743-754Baek-Min KIM;see Seong-Joong KIM et al.;(4),863-878BAI Tao;see LI Gang et al.;(1),66-84BAO Qing;see YANG Jing et al.;(5),1147—1156BEI Naifang;