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1.
《海洋通报(英文版)》2016,(2)
天津近岸大气气溶胶光学特性季节变化对提高海洋卫星的大气校正精度和了解天津与渤海海陆交界地区的环境具有重要意义。本文利用CE317太阳光度计定点观测了天津近岸2010年4月到2011年5月气溶胶光学数据,分析了渤海湾近岸地区气溶胶周年光学特性。结果表明:气溶胶光学厚度(Aerosol Optical Thickness,AOT)光谱基本满足Angstrom关系;AOT日变化基本有三种变化趋势:上升型,平稳型,下降型;天津近岸秋季AOT最高,平均值0.686,春夏次之,冬季最低;Angstrom指数α春夏秋冬依次升高,春季由于沙尘的影响,其Angstrom指数α最小,平均值为0.854。该结果与黄海近岸的青岛地区作了比较分析,表明了气溶胶光学性质的区域性。 相似文献
2.
SeaWiFS遥感资料分析中国海域气溶胶光学厚度的季节变化和分布特征 总被引：1，自引：0，他引：1
通过与地基气溶胶观测数据的对比,确认了SeaWiFS气溶胶光学厚度产品用于研究中国海域气溶胶分布和变化特征的有效性。在此基础上,分析了中国海域气溶胶光学厚度的季节变化和地理分布特征。研究结果表明,中国东部海域平均气溶胶光学厚度存在以中纬度为中心的纬向分布;受沙尘、季风气候的影响,中国海域气溶胶光学厚度存在季节变化,不同海区有不同的季节变化和分布特征。渤海、黄海及东海有类似的变化特征,春季都受到沙尘气溶胶的影响,使中国东部海域气溶胶光学厚度普遍高于0.160,且对东海的影响最大;夏、秋季逐渐减小,冬季有所回升。南海气溶胶光学厚度均值为0.150,随时间变化不明显,但地理分布变化显著;受季风气候的影响,从春季到冬季,气溶胶光学厚度高值中心从高纬海域向低纬海域转移,范围也逐渐扩大。冬季南海大部分海域气溶胶光学厚度都达到0.160以上,是整个中国海域冬季气溶胶光学厚度最大的海区。气溶胶光学厚度的季节变化和地理分布特征为研究中国海区域气候变化和海洋生态提供了依据。 相似文献
3.
利用2003—2008年在渤海所测的212组气溶胶数据,研究了气溶胶光学厚度(AOT)、浑浊度系数(β)和ngstrm指数α的时空分布规律,并初步探讨了气溶胶特征参数之间的关系。结果表明,在时间分布方面,渤海气溶胶光学厚度、浓度和粒径尺度在一天中的变化比较小,中午AOT较大,10点左右AOT较小。从6月份到9月份,AOT、β和α的范围和均值均呈下降趋势;3月份AOT、β和α均明显小于六、8、9月份的对应值。离岸25km之内的大部分地区,AOT随离岸距离的增大而减小;但25km以外,气溶胶变化没有规律。比较而言,气溶胶AOT和β变化比较一致。浑浊度系数(浓度因子)β比ngstrm指数(尺度因子)α对气溶胶光学厚度AOT的影响更明显,AOT和β的关系可采用一阶线性回归方程表示。 相似文献
4.
气溶胶光学厚度(AOD)是表征大气浑浊程度的关键的物理量,也是确定气溶胶气候效应的重要因素。本文以环渤海区域为例,首先利用太阳光度计观测数据对MODIS数据气溶胶光学厚度产品进行验证分析,暗像元法反演结果最好;然后基于2005年1月至2019年2月MODIS暗像元法反演结果分析环渤海地区气溶胶光学厚度的时空分布。结果表明:环渤海地区AOD分布呈现出明显的季节变化,春夏季节气溶胶处于高值区(0.7-0.9),秋冬季节处于低值区(0.2-0.5),AOD高值区主要分布于北京、天津及河北和山东交界地区。气溶胶分布特征可能与沙尘天气、季风变化和工业生产生活的污染物排放有关。 相似文献
5.
水平能见度经验公式在青岛沿海地区的应用 总被引：3，自引：1，他引：2
利用青岛4 a的能见度观测资料和大气气溶胶光学参数反演资料检验了4种能见度经验计算公式的适用性。发现Koschmieder公式和乘幂公式能更好地刻画能见度和气溶胶光学厚度间变化趋势上的相关性,但是由于能见度观测的不确定性和气溶胶光学参数反演的不准确性导致的数据离散性,需要确定更有代表性的经验参数。利用最大发生频率原则确定不同光学厚度对应的能见度值,据此确定适合青岛的各经验公式的经验参数,最后利用MODIS光学厚度资料将乘幂经验公式应用到青岛附近沿海地区能见度的计算,发现春季和夏季的能见度低于其他季节。 相似文献
6.
利用MODIS气溶胶产品中550nm气溶胶光学厚度和小颗粒比例两个参数的关系,直接计算得到我国海域人为和沙尘气溶胶光学厚度,并对人为和沙尘气溶胶的分布进行了分析,结果表明:在我国海域,利用550nm气溶胶光学厚度和小颗粒比例的关系直接计算人为和沙尘气溶胶光学厚度是可行的;我国海域的人为和沙尘气溶胶光学厚度有明显的时空变化.人为气溶胶在春夏季最大,而在秋冬季最小;沙尘气溶胶在冬春季最大,而在夏秋季最小.在该海域人为和沙尘气溶胶光学厚度有显著的空间变化,在纬向上,人为气溶胶光学厚度在30°~45°N达到最大值,向南北递减;沙尘气溶胶光学厚度在33°~40°N最大,在其他纬度都很小.在经向上,人为气溶胶光学厚度与离岸距离有关,在沿海120°E处最大,随着经度增加而减小.沙尘作为大颗粒气溶胶,传输距离小,所以它只是在120°~123°E出现最大值,在其他经度处都很小. 相似文献
7.
1988年12月至1990年3月,利用中美海气实验厦门观测站,采集了厦门海域107个气溶胶样品,利用离子色谱法和原子吸收分光法测定其中的Na+、Cl-、SO42-、NO3-、NH4+、Al、Fe、V、Pb、Cu、Cd.本文分析了厦门海域大气气溶胶中物质的浓度分布特征及季节变化规律.结果表明,厦门海域大气气溶胶物质主要来源为海水、土壤尘、煤烟尘和燃油灰,陆源物质具有明显的季节变化,湿季浓度明显低于干季,雨水对大气中颗粒物的清除起着重要作用. 相似文献
8.
在卫星数据反演气溶胶光学厚度产品的基础上,讨论了二次反演大气柱中气溶胶粒子密度的问题.通过理论分析,利用多波段气溶胶光学厚度提取大气柱中气溶胶粒子密度是可行的,并指出能否准确确定多波段气溶胶光学厚度会直接影响粒子密度的反演结果.定义并分析了气溶胶粒子消光体积权重系数随粒子半径的变化,表明从气溶胶光学厚度中反演大气柱中气溶胶积聚模态和粗模态粒子密度的结果是可信的.利用SeaWiFS气溶胶光学厚度产品,运用蒙特卡罗法反演了2002年我国海域上空大气柱中积聚模态和粗模态气溶胶粒子密度,结果表明,积聚模态粒子密度比粗模态的高2~3个量级,它们的空间分布趋势一致;我国近岸海域大气柱中气溶胶粒子密度高于离岸海域的;春季气溶胶粒子密度高于其他季节的,特别在黄海、东海海区是如此. 相似文献
9.
以渤海海域为试验区,对经过时间、空间和波段匹配的 MODIS/Aqua 550 nm 气溶胶光学厚度产品与 CALIOP 532 nm 通道反演得到的气溶胶信息在五种不同空间采样窗口(10 km ×10 km,30 km ×30 km,50 km ×50 km,70 km ×70 km 和90 km ×90 km)、三种不同时间尺度(日、月、季度)下进行了相关性拟合分析.研究发现,较小的空间采样窗口可以更准确地反映气溶胶的局部变化特征,而以季度为时间统计单元能更好地体现气溶胶的季节变化特性.实验结果表明,在10 km ×10 km 采样窗口中,春季的日数据之间相关性较高;春季和秋季的月均值之间高度相关(R 均大于0.950).从而证明,在特定时间和空间尺度下,上述两种数据之间确存在良好的相关性,为利用遥感数据反演渤海海域气溶胶光学厚度信息提供了新的途径. 相似文献
10.
利用2015年4、5、6月在长江口外开展综合海洋调查获取的实测数据,分析春季口门外海域长江冲淡水（Changjiang diluted water,CDW）时空分布特征及扩散过程,并结合同期的多源环境观测数据,探讨各环境动力因素对春季长江冲淡水分布的影响,深化对冲淡水在口门外海域扩展及其动态变化的认识。观测结果显示,2015年春季长江冲淡水的扩散范围逐月增大,主体最远可到达123°E以东海域,其逐月变化主要受控于口外水文气象环境。长江径流量大小决定了冲淡水出口门后的分布范围以及表层水盐度,风向则控制冲淡水的扩展态势。在风场与径流的共同作用下,春季口门外海域长江冲淡水的扩散呈现三种模式：4月份的顺岸南下型（冬季型）、5月份的东北转向型（过渡型）和6月份的东南-东北双向分支型（夏季型）。春季台湾暖流深层水已到达长江口外海域,与表层冲淡水层相互作用较弱,但随着上升流的逐月增强,其与上层低盐冲淡水之间的跃层效应愈发显著,一方面抑制长江冲淡水的向下扩展,同时上升流的涌升也减薄了冲淡水的厚度。再悬浮泥沙向上扩散的厚度显示出春季潮混合过程难以影响至表层,但在大潮情况下,水位波动变化更为剧烈,使外海高盐海水向陆上溯更远,导致5月份12250E断面的水体盐度整体相对较高。 相似文献
11.
Forecasting ocean wave energy: Tests of time-series models 总被引：1，自引：0，他引：1
Gordon Reikard 《Ocean Engineering》2009,36(5):348-356
This paper evaluates the ability of time-series models to predict the energy from ocean waves. Data sets from four Pacific Ocean sites are analyzed. The energy flux is found to exhibit nonlinear variability. The probability distribution has heavy tails, while the fractal dimension is non-integer. This argues for using nonlinear models. The primary technique used here is a time-varying parameter regression in logs. The time-varying regression is estimated using both a Kalman filter and a sliding window, with various window widths. The sliding window method is found to be preferable. A second approach is to combine neural networks with time-varying regressions, in a hybrid model. Both of these methods are tested on the flux itself. Time-varying regressions are also used to forecast the wave height and wave period separately, and combine the forecasts to predict the flux. Forecasting experiments are run at an hourly frequency over horizons of 1-4 h, and at a daily frequency over 1-3 days. All the models are found to improve relative to a random walk. In the hourly data sets, forecasting the components separately achieves the best results in three out of four cases. In daily data sets, the hybrid and regression models yield similar outcomes. Because of the intrinsic variability of the data, the forecast error is fairly high, comparable to the errors found in other forms of alternative energy, such as wind and solar. 相似文献
12.
中国近海沿岸海温多时间尺度变率及影响其变化的天气气候因素 总被引：3，自引：0，他引：3
本文通过对中国近海13个海洋站1959年～2003年逐日日平均海表温度及其相关数据的处理分析,结合中国近海多年逐日天气实况,研究了中国近海水温的短时（1d）、过程、旬、月、年际变率以及海温的长期趋势变化,并在此基础上详细讨论了影响中国近海各时间尺度水温变化的天气气候因素. 相似文献
13.
基于卫星的气溶胶光学厚度(aerosol optical thickness,AOT)是研究大气污染程度及时空变化的重要参考,由于大气污染物排放特征、地理和气候背景不同,不同区域AOT的时空分布及其与地面大气污染物质量浓度的相关性存在一定的差异。选取了2017年7月—2020年7月山东89个国家环境空气质量监测站数据、日本宇宙航空研究开发机构(Japan Aerospace Exploration Agency,JAXA)发布的葵花8号和9号气象卫星(Himawari-8/9)AOT产品、欧洲中期天气预报中心(European Center for Medium-Range Weather Forecasts,ECMWF)的ERA5再分析数据产品,研究了山东地区卫星AOT时空分布特征,AOT与地面污染物质量浓度的相关性,并得出了以下结论:1)山东存在两个主要的AOT低值区,分别位于鲁中山区一带,半岛丘陵并延伸到东部沿海一带,低值区的分布没有明显的季节变化;山东AOT年平均的高值区主要分布在山东西部、南部与外省接壤附近地区,以及渤海南部至莱州湾沿岸一带,在分析气溶胶跨省传输时值得关注。不同季节AOT的高值区分布存在差异。2)山东AOT白天变化呈现双峰结构,08时由峰值逐渐下降,11时转为上升,14时达全天最大值0.608;AOT的日变化趋势与细颗粒物(PM2.5)、O3等大气污染物质量浓度变化明显不同,是影响其相关性的重要因素。AOT月际变化中,存在两个显著的峰值6月(0.648)和10月(0.622),2月AOT最低。AOT的季节变化与地面污染物质量浓度的季节变化呈现一定的反位相特征。3)总体上AOT与PM2.5、O3等主要大气污染物质量浓度的相关性不高,一年之中,6月AOT与污染物的相关程度最低,1月的相关性最高;15—17时是AOT与污染物相关性最强的时间段,而10时相关性最差。单凭AOT难以定量反映污染物的分布特征,使用卫星开展地面大气污染监测分析还需纳入更多的因子进行分析。 相似文献
14.
Vicente Fernndez David E. Dietrich Robert L. Haney Joaquín Tintor 《Progress in Oceanography》2005,66(2-4):321
In this paper, we present the results from a 1/8° horizontal resolution numerical simulation of the Mediterranean Sea using an ocean model (DieCAST) that is stable with low general dissipation and that uses accurate control volume fourth-order numerics with reduced numerical dispersion. The ocean model is forced using climatological monthly mean winds and relaxation towards monthly climatological surface temperature and salinity. The variability of the circulation obtained is assessed by computing the volume transport through certain sections and straits where comparison with observations is possible. The seasonal variability of certain currents is reproduced in the model simulations. More important, an interannual variability, manifested by changes in currents and water mass properties, is also found in the results. This may indicate that the oceanic internal variability (not depending on external atmospheric forcing), is an important component of the total variability of the Mediterranean circulation; variability that seems to be very significant and well documented by in situ and satellite data recovered in the Mediterranean Sea during the last decade. 相似文献
15.
Assessment and integration of conventional,RTK-GPS and image-derived beach survey methods for daily to decadal coastal monitoring 总被引：1，自引：0，他引：1
Coastal monitoring across a broad range of time-scales was recognized in the latest report by the Intergovernmental Panel on Climate Change as key to better understanding the likely impacts of climate change at the coast. A unique and historic coastal monitoring program undertaken at the Collaroy–Narrabeen embayment in south-eastern Australia comprises: 1) 30 years of monthly conventional (Emery method) surveys of five cross-shore profile lines; 2) three years of monthly three-dimensional surveys of the entire embayment using RTK-GPS mounted to an all-terrain vehicle (ATV); and 3) four years of hourly shoreline measurements using coastal imaging technology (ARGUS). This study evaluates the strengths and limitations of conventional, RTK-GPS and image-derived surveys for coastal monitoring at daily to decadal coastal time-scales. High-accuracy RTK-GPS was used to first assess the accuracy of the conventional and image-derived survey methods. The magnitude of daily to decadal coastal variability was then characterized by calculating the temporal semivariogram of the integrated survey dataset. With both measurement errors and the degree of beach variability quantified, the corresponding signal-to-noise ratios (SNR) of each survey method at different time-scales were determined. The value of the simple and cost-effective Emery method was verified by this analysis, with measurement error significantly smaller than the degree of overall beach variability (SNR = 8.4). The accuracy, speed and efficiency of ATV-mounted RTK-GPS meanwhile make it suitable for three-dimensional beach surveys. Image-derived surveys were found to be an effective means of remotely measuring the considerable degree of beach variability identified at time-scales of less than one month. These measurements however become indistinguishable from survey noise (i.e. SNR ≤ 1) when considering typical weekly (or smaller) variations at large distances from the cameras. 相似文献
16.
Using Morlet wavelet transform and harmonic analysis the multi-scale variability of subsurface temperature in the South China Sea is studied by analyzing one-year (from April 1998 to April 1999) ATLAS mooring data. By wavelet transform, annual and semi-annual cycle as well as intrasea-sonal variations are found, with different dominance, in subsurface temperature. For annual harmonic cycle, both the downward net surface heat flux and thermocline vertical movement partially control the subsurface temperature variability. For semi-annual cycle and intraseasonal variability, the subsurface temperature variability is mainly linked to the vertical displacement of thermocline. 相似文献
17.
《Journal of Sea Research》1999,41(1-2):141-148
Particulate amino acids (THAA) were measured in the East Frisian Wadden Sea at monthly intervals from February to December 1995. Each time two tidal cycles were sampled at hourly intervals. THAA made up between 3 and 50% (mean 15±7%) of the particulate organic carbon and between 9.3 and 104% (mean 49±20%) of the total nitrogen pools. Seasonal variations of THAA followed the development of phytoplankton in the water column while tidal variability was controlled largely by physical forcing such as tidal current velocities and wave action. The overall molar composition of the THAA pool appears to be dominated by a detrital signal originating from erosion of surface sediments. 相似文献
18.
哈马黑拉涡旋的季节变异研究 总被引：1，自引：0，他引：1
利用1992年1月～2006年11月期间的ECCO2海洋同化数据资料,研究了海洋上层50m平均的哈马黑拉涡旋的季节变异特征。结果表明,哈马黑拉涡旋在5月开始出现,7月达到最强,3月和4月消失,这主要取决于新几内亚沿岸流的季节变化。新几内亚沿岸流4～11月为西北向,从12月到次年的2月为东南向。随着西北向新几内亚沿岸流的增强,哈马黑拉涡旋开始出现并增强。而随着西北向新几内亚沿岸流的减弱和反向,哈马黑拉涡旋减弱并消失。同时棉兰老海流在夏季的增强也有利于哈马黑拉涡旋增强。旨在研究哈马黑拉涡旋的季节变异特征及其影响因子,为进一步探讨其垂向结构、年际变异等时空变化特征和该区域的环流特征提供研究基础。 相似文献
19.
A technique is presented to estimate photosynthetically available radiation (PAR) at the ocean surface from Geostationary Ocean Color Imager (GOCI) data. The sensor is adapted to the problem, since it measures at visible wavelengths and does not saturate over clouds, and the hourly data provides adequate temporal sampling to describe diurnal variability of clouds. Instantaneous surface PAR is computed as the difference between the solar irradiance incident at the top of the atmosphere (known) and the solar irradiance reflected back to space (derived from GOCI radiance), taking into account absorption and scattering by the clear atmosphere (modeled). Knowledge of pixel composition is not required. Apart from planetary albedo and sun zenith angle, the model parameters are fixed at their climatological values. The instantaneous PAR estimates at hourly intervals are integrated over time to provide daily values. The technique is applied to GOCI imagery acquired on 5 April 2011, and the GOCI daily PAR estimates are compared with those obtained from MODerate Resolution Imaging Spectrometer (MODIS) data. Agreement is good between the two types of estimates, with a coefficient of determination (r 2) of 0.778, a bias of 0.23 Em?2d?1 (0.5% with higher GOCI values), and a root-mean-squared difference of 5.00 Em?2d?1 (11.2%). Differences in cloudy conditions are attributed to daily cloudiness changes not captured by the MODIS observations. The comparison statistics indicate that GOCI PAR estimates have acceptable accuracy for regional studies of aquatic photosynthesis. 相似文献
20.
In this study, we used the National Centers for Environmental Prediction monthly sea surface temperature (SST) and surface
air temperature (SAT) data during 1982–1994 and the National Center for Atmospheric Research surface wind stress curl data
during 1982–1989 to investigate the Japan Sea SST temporal and spatial variabilities and their relations to atmospheric forcing.
First, we found an asymmetry in the correlation coefficients between SST and wind stress curl, which implies that the SST
variability at the scales of the order of one month is largely due to atmospheric forcing. Second, we performed three analyses
on the data fields: annual mean, composite analysis to obtain the monthly anomaly relative to the annual mean, and empirical
orthogonal function (EOF) analysis on the residue data relative to the summation of the annual mean and the monthly anomaly.
The first EOF mode of SST accounts for 59.9% of the variance and represents the Subpolar Front. The temporal variation of
the first EOF mode implies that the deep Japan Sea could be cooler in cold seasons (November–April) of 1984–1987. Third, we
computed cross-correlation coefficients among various principal components and found that the atmospheric warming/cooling
is the key factor causing intra-seasonal and interannual SST variabilities. 相似文献