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基于温度链浮标获取南极普里兹湾积雪和固定冰厚度的研究 总被引：2，自引：2，他引：0
极地积雪和海冰厚度是气候变化的重要指标,也是船舶在冰区航行需要掌握的主要参数。2014和2015年在南极普里兹湾中山站附近布放了一种新式的温度链浮标,该浮标每天进行4次常规温度观测和1次加热升温观测,用于实时获取积雪和海冰剖面温度及厚度数据的研究。通过分析剖面温度曲线和升温曲线反映出的大气、积雪、海冰和海水4种介质的热传导特性差异,可利用人工识别的方法（人工经验法）获得大气/积雪、积雪/海冰和海冰/海水界面的位置。根据统计不同介质在升温响应和垂直温度梯度等方面的特性,找到合理阈值,可通过编写程序自动判断各界面的位置（自动程序法）。本文利用这两种方法来判断不同物质界面位置从而计算得到积雪和海冰厚度。与现场人工观测的海冰厚度相比,人工经验法的平均偏差和均方根偏差分别为2.1 cm和6.4 cm（2014年）以及4.3 cm和6.5 cm（2015年）,自动程序法的平均偏差和均方根偏差分别为-6.8 cm和6.4 cm（2014年）以及4.5 cm和 6.6 cm（2015年）;对于积雪,人工经验法与现场人工观测的平均偏差和均方根偏差分别为0.5 cm和 8.5 cm,而自动程序法的平均偏差和均方根偏差分别为4.7 cm和10.8 cm。自动程序法误差较人工经验法偏大,但考虑到整体冰厚和现场观测的误差,两种方法的结果均是可信的,精度是可以接受的。利用新式的温度链浮标实时获取南极普里兹湾积雪和海冰厚度是可行的。 相似文献
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《海洋预报》2018,(5)
利用我国南极中山站的越冬观测平台,国家海洋环境预报中心自2010年起,在中山站附近海域选取固定海冰观测点,陆续布放各种自动化观测设备,初步建立海冰综合观测系统,开展常规气象要素、辐射、涡动通量、积雪和海冰温度、厚度、海水温盐等多要素的业务化观测。目前每年度的现场观测始于南半球初冬,即3—4月份,持续时间一般为8—10个月,自动观测要素的采样频率一般为1 min,人工观测要素的采样频率一般为7 d。结果表明:获取的高精度、长时间序列的现场数据可以广泛应用于我国的南极海冰数值预报和雪龙船海冰服务保障等工作中,初步解决了极地海冰预报保障对现场海冰观测数据的迫切需求。 相似文献
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2016年4月至11月在南极中山站普里兹湾布设了A1、A2、A3 3套冰雪情检测传感器。传感器每隔1 h采集一次数据,实时获取了被测点空气、积雪、海冰和海水的剖面温度数据。通过对不同介质剖面温度的分析,系统可以有效反映出海冰、积雪在气温影响下的温度变化差异,即空气、积雪、海冰和海水的热传导特性差异。通过寻找合理的温度阈值,编写MATLAB程序分别对积雪、海冰上下界面位置进行了自动判断,从而得到整个观测期间海冰厚度和积雪深度的变化过程。并与人工观测进行比较,结果表明:从传感器安装时间开始,海冰持续增长,10月开始海冰增长速度放慢,直至10月末达到最大海冰厚度170 cm左右。A1、A2、A3传感器采集的冰厚值与人工观测值之间平均误差分别为5.1 cm(A1)、3.4 cm(A2)、3.6 cm(A3);积雪深度的平均误差分别为3.2 cm(A1)、3.5 cm(A2)、2.7 cm(A3),传感器测得的积雪、海冰厚度结果可以较好的反映出被测地点冰雪情的演变过程,是一种可以应用于条件恶劣地区的冰雪环境有效检测手段。 相似文献
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2017年夏季北极中央航道海冰观测特征及海冰密集度遥感产品评估 总被引：2，自引：2，他引：0
2017年夏季中国第八次北极科学考察期间，"雪龙"号极地考察船首次成功穿越北极中央航道，期间全程开展了海冰要素的人工观测。中央航道走航期间的平均海冰密集度和平均冰厚分别为0.64和1.5 m，海冰密集度时空变化大且以厚当年冰为主，高纬密集冰区的浮冰大小显著高于海冰边缘区。基于"雪龙"号的船基走航观测海冰密集度评估比较了国际上常用的5种常用的微波遥感反演海冰密集度产品，同走航目测海冰密集度点对点的比较，误差最大的为德国不来梅大学AMSR2基于Bootstrap算法的产品，平均误差和均方根误差分别为0.19和0.28；误差最小的为欧洲气象卫星应用组织基于AMSR2数据和OSHD和TUD两种不同算法的产品，平均误差分别为-0.02和0.01，均方根误差均为0.20。从日平均比较来看，AMSR2基于Bootstrap算法的误差最大，平均误差和均方根误差分别为0.15和0.20；AMSR2/OSI SAF（TUD）的误差最小，平均误差和均方根误差分别为0.0和0.11，OSI SAF产品更接近人工观测结果。 相似文献
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基于2018年8月至2019年5月布放在北极随海冰漂流的自动气象站和温度链浮标获取的观测数据,分析了北极高纬度区域的大气特征和海冰生消过程。根据海冰的漂移轨迹分为两个阶段分析,第1阶段,海冰主要向东南漂移;第2阶段,海冰主要向东北漂移。第1阶段观测的平均气温和平均相对湿度分别为–6.6℃和93%,第2阶段观测的平均气温和平均相对湿度分别为–29.3℃和76%,第2阶段平均气压高于第1阶段。海冰的漂移轨迹主要受到波弗特高压外围气流的影响。利用自动气象站漂移轨迹计算得到海冰漂移速度,与美国国家冰雪数据中心海冰漂移速度比较显示,两者纬向速度更为接近。海冰在第1阶段以融化为主,海冰厚度略有减小,8月份海冰生长率为–0.11 cm/d;海冰的生长过程主要发生在第2阶段,1–3月生长率均超过0.9 cm/d,2019年3月海冰生长最快,平均生长率为1.3 cm/d,海冰的增长一直持续至观测结束。 相似文献
6.
漂流式海气界面浮标是创新研制的“小型化、轻质化、免维护”的漂流观测系统,能够测量海面以上3 m气象、水下20 cm海表面温度和波浪参数等11个不同的物理参数,并且已经经过多次观测应用,结果均较好。为实现漂流式海气界面浮标观测数据全球范围的应用,利用2018年黑潮延伸体海域Argo观测的海表面温度(sea surface temperature, SST)、SVP (surface velocity program)浮标观测的海表温度和OISST (optimum interpolation sea surface temperature)数据,通过将其与漂流式海气界面浮标观测数据进行时空匹配以及对比验证,对漂流式海气界面浮标观测的海表面温度进行了系统评估,检验其在黑潮延伸体复杂水文环境下的观测准确性。结果表明,漂流式海气界面浮标观测SST数据与Argo观测SST数据相关系数达到0.9737,均方根误差和平均误差分别为0.5790°C和0.4539°C;与SVP浮标SST数据的相关系数弱于与Argo的相关系数,为0.9285,均方根误差为1.323 0°C,平均误差约为0.979 4°C... 相似文献
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海冰厚度是监测与研究渤海海冰的重要参数。为了获取更加可靠的渤海海冰厚度数据,本研究基于MODIS数据对海冰厚度反演中的冰水分离环节和冰厚计算方法都进行了改进。对于冰水分离环节,本文在Canny边缘检测算子提取海冰基础上,加入了二值化处理、阈值判别等步骤,实现了较高精度的渤海海冰范围自动化提取。通过试验确定了海冰厚度与反照率指数关系模型中的参数,包括海冰衰减系数和海水反照率参数,使其更加符合渤海海区的物理特征。将改进后算法的海冰厚度反演结果与渤海海上石油平台实测数据进行比较,并分析了误差来源。结果表明,经过对算法的改进,海冰厚度与反照率指数关系模型的反演结果与实测数据之间的平均绝对误差由7.05 cm缩小到2.74 cm,相关系数由0.434提高到0.485。 相似文献
10.
西北太平洋海表温度融合产品交叉比对分析 总被引：2，自引：0，他引：2
海表温度产品是研究全球海洋大气系统的重要数据源,在海洋相关领域的研究和应用方面具有重要价值。以西北太平洋海域为研究区域,本文对2007-2014年的3个海表温度融合数据（AVHRR OISST,MISST和OSTIA）的产品特性与Argo浮标进行了真实性检验,并对融合产品进行了交叉比对分析。结果表明,3个融合产品在空间尺度上均能反映西北太平洋海域的海表温度变化趋势。融合数据与Argo浮标的平均偏差在±0.1℃之间,均方根误差小于0.9℃。融合数据与浮标数据存在明显的季节性变化,其中冬季融合数据与浮标数据的平均偏差和均方根误差较小。在高纬海域,融合产品和浮标存在正偏差。与另两个融合产品相比,OSTIA的数据质量与Argo浮标最为接近。3个融合产品在近岸和高纬海域差异较大,三者对海冰的标识和处理方式不同对融合结果也有影响。在2012年6月之前MISST和OSTIA的海表温度数据质量更为接近,但在此之后MISST存在系统误差。红外数据、微波数据和实测数据作为输入数据,是制作高时空分辨率高精度海表温度融合产品必不可少的要素。 相似文献
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基于中国北极考察布放浮标观测和数值模拟的海冰和积雪厚度变化分析 总被引：1，自引：0，他引：1
Sea ice and the snow pack on top of it were investigated using Chinese National Arctic Research Expedition(CHINARE) buoy data.Two polar hydrometeorological drifters,known as Zeno? ice stations,were deployed during CHINARE 2003.A new type of high-resolution Snow and Ice Mass Balance Arrays,known as SIMBA buoys,were deployed during CHINARE 2014.Data from those buoys were applied to investigate the thickness of sea ice and snow in the CHINARE domain.A simple approach was applied to estimate the average snow thickness on the basis of Zeno~ temperature data.Snow and ice thicknesses were also derived from vertical temperature profile data based on the SIMBA buoys.A one-dimensional snow and ice thermodynamic model(HIGHTSI) was applied to calculate the snow and ice thickness along the buoy drift trajectories.The model forcing was based on forecasts and analyses of the European Centre for Medium-Range Weather Forecasts(ECMWF).The Zeno~ buoys drifted in a confined area during 2003–2004.The snow thickness modelled applying HIGHTSI was consistent with results based on Zeno~ buoy data.The SIMBA buoys drifted from 81.1°N,157.4°W to 73.5°N,134.9°W in 15 months during2014–2015.The total ice thickness increased from an initial August 2014 value of 1.97 m to a maximum value of2.45 m before the onset of snow melt in May 2015;the last observation was approximately 1 m in late November2015.The ice thickness based on HIGHTSI agreed with SIMBA measurements,in particular when the seasonal variation of oceanic heat flux was taken into account,but the modelled snow thickness differed from the observed one.Sea ice thickness derived from SIMBA data was reasonably good in cold conditions,but challenges remain in both snow and ice thickness in summer. 相似文献
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Sea ice characteristics between the middle Weddell Sea and the Prydz Bay, Antarctica during the austral summer of 2003 总被引：1，自引：0，他引：1
The antarctic sea ice was investigated upon five occasions between January 4 and February 15, 2003. The investigations included: (1) estimation of sea ice distribution by ship-based observations between the middle Weddell Sea and the Prydz Bay; (2) estimation of sea ice distribution by aerial photography in the Prydz Bay; (3) direct measurements of fast ice thickness and snow cover, as well as ice core sampling in Nella Fjord; (4) estimation of melting sea ice distribution near the Zhongshan Station; and (5) observation of sea ice early freeze near the Zhongshan Station. On average, sea ice covered 14.4% of the study area. The highest sea ice concentration (80%) was observed in the Weddell Sea. First-year ice was dominant (99.7%-99.8%). Sea ice distributions in the Prydz Bay were more variable due to complex inshore topography, proximity of the Larsemann Hills, and/or grounded icebergs. The average thickness of landfast ice in NeUa Fjord was 169.5 cm. Wind-blown snow redistribution plays an important role in affecting the ice thickness in Nella Fjord. Preliminary freezing of sea ice near the Zhongshan Station follows the first two phases of the pancake cycle. 相似文献
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Jiechen Zhao Tao Yang Qi Shu Hui Shen Zhongxiang Tian Guanghua Hao Biao Zhao 《海洋学报(英文版)》2021,40(7):129-141
A high resolution one-dimensional thermodynamic snow and ice(HIGHTSI) model was used to model the annual cycle of landfast ice mass and heat balance near Zhongshan Station, East Antarctica. The model was forced and initialized by meteorological and sea ice in situ observations from April 2015 to April 2016. HIGHTSI produced a reasonable snow and ice evolution in the validation experiments, with a negligible mean ice thickness bias of(0.003±0.06) m compared to in situ observations. To further examine the impact of different snow conditions on annual evolution of first-year ice(FYI), four sensitivity experiments with different precipitation schemes(0, half, normal, and double) were performed. The results showed that compared to the snow-free case,the insulation effect of snow cover decreased bottom freezing in the winter, leading to 15%–26% reduction of maximum ice thickness. Thick snow cover caused negative freeboard and flooding, and then snow ice formation,which contributed 12%–49% to the maximum ice thickness. In early summer, snow cover delayed the onset of ice melting for about one month, while the melting of snow cover led to the formation of superimposed ice,accounting for 5%–10% of the ice thickness. Internal ice melting was a significant contributor in summer whether snow cover existed or not, accounting for 35%–56% of the total summer ice loss. The multi-year ice(MYI)simulations suggested that when snow-covered ice persisted from FYI to the 10 th MYI, winter congelation ice percentage decreased from 80% to 44%(snow ice and superimposed ice increased), while the contribution of internal ice melting in the summer decreased from 45% to 5%(bottom ice melting dominated). 相似文献
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Takenobu Toyota Shinya Takatsuji Kazutaka Tateyama Kazuhiro Naoki Kay I. Ohshima 《Journal of Oceanography》2007,63(3):393-411
The general properties of sea ice and overlying snow in the southern Sea of Okhotsk were examined during early February of
2003 to 2005 with the P/V “Soya”. Thin section analysis of crystal structure revealed that frazil ice (48% of total core length)
was more prevalent than columnar ice (39%) and that stratigraphic layering was prominent with a mean layer thickness of 12
cm, indicating that dynamic processes are essential to ice growth. The mean thickness of ice blocks and visual observations
suggest that ridging dominates the deformation process above thicknesses of 30 to 40 cm. As for snow, it was found that faceted
crystals and depth hoar are dominant (78%), as which is also common in the Antarctic sea ice, and is indicative of the strong
vertical temperature gradients within the snow. Stable isotope measurements (δ18O) indicate that snow ice occupies 9% of total core length and that the mass fraction of meteoric ice accounts for 1 to 2%
of total ice volume, which is lower than the Antarctic sea ice. Associated with this, the effective fractionation coefficient
during the freezing of seawater was also derived. Snow ice was characterized by lower density, higher salinity, and nearly
twice the gas content of ice of seawater origin. In addition, it is shown that the surface brine volume fraction and freeboard
are well correlated with ice thickness, indicating some promise for remote sensing approaches to the estimation of ice thickness. 相似文献
15.
Sea ice growth and consolidation play a significant role in heat and momentum exchange between the atmosphere and the ocean. However, few in situ observations of sea ice kinematics have been reported owing to difficulties of deployment of buoys in the marginal ice zone (MIZ). To investigate the characteristics of sea ice kinematics from MIZ to packed ice zone (PIZ), eight drifting buoys designed by Taiyuan University of Technology were deployed in the open water at the ice edge of the Canadian Basin. Sea ice near the buoy constantly increased as the buoy drifted, and the kinematics of the buoy changed as the buoy was frozen into the ice. This process can be determined using sea ice concentration, sea skin temperature, and drift speed of buoy together. Sea ice concentration data showed that buoys entered the PIZ in mid-October as the ice grew and consolidated around the buoys, with high amplitude, high frequency buoy motions almost ceasing. Our results confirmed that good correlation coefficient in monthly scale between buoy drift and the wind only happened in the ice zone. The correlation coefficient between buoys and wind was below 0.3 while the buoys were in open water. As buoys entered the ice zone, the buoy speed was normally distributed at wind speeds above 6 m/s. The buoy drifted mainly to the right of the wind within 45° at wind speeds above 8 m/s. During further consolidation of the ice in MIZ, the direct forcing on the ice through winds will be lessened. The correlation coefficient value increased to 0.9 in November, and gradually decreased to 0.7 in April. 相似文献
16.
基于卫星高度计的北极海冰厚度变化研究 总被引：5，自引：3，他引：2
A modified algorithm taking into account the first year(FY) and multiyear(MY) ice densities is used to derive a sea ice thickness from freeboard measurements acquired by satellite altimetry ICESat(2003–2008). Estimates agree with various independent in situ measurements within 0.21 m. Both the fall and winter campaigns see a dramatic extent retreat of thicker MY ice that survives at least one summer melting season. There were strong seasonal and interannual variabilities with regard to the mean thickness. Seasonal increases of 0.53 m for FY the ice and 0.29 m for the MY ice between the autumn and the winter ICESat campaigns, roughly 4–5 month separation, were found. Interannually, the significant MY ice thickness declines over the consecutive four ICESat winter campaigns(2005–2008) leads to a pronounced thickness drop of 0.8 m in MY sea ice zones. No clear trend was identified from the averaged thickness of thinner, FY ice that emerges in autumn and winter and melts in summer. Uncertainty estimates for our calculated thickness, caused by the standard deviations of multiple input parameters including freeboard, ice density, snow density, snow depth, show large errors more than 0.5 m in thicker MY ice zones and relatively small standard deviations under 0.5 m elsewhere. Moreover, a sensitivity analysis is implemented to determine the separate impact on the thickness estimate in the dependence of an individual input variable as mentioned above. The results show systematic bias of the estimated ice thickness appears to be mainly caused by the variations of freeboard as well as the ice density whereas the snow density and depth brings about relatively insignificant errors. 相似文献
17.
为解决现场调查数据覆盖不足的问题,利用卫星遥感数据(Landsat TM和ETM+)对桑沟湾海域的海冰厚度进行了反演。与Zubov模型计算结果相比,本反演结果与之接近(相关系数0.89)。由遥感影像提取结果看出,桑沟湾海冰厚度随时间和空间变化明显。在轻冰年份,桑沟湾基本无冰。在偏重冰年和重冰年份,桑沟湾出现大量浮冰,并且海冰在水动力和风应力的作用下,呈现由近岸到离岸冰厚不断减小的趋势。重冰年份桑沟湾南侧由于受潮汐和风力推动作用下发生挤压变形,近岸出现平均冰厚较大的海冰(20 cm),桑沟湾中部也出现平均厚度约5~10 cm的海冰。 相似文献
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基于MODIS热红外数据的渤海海冰厚度反演 总被引：3，自引：1，他引：2
Level ice thickness distribution pattern in the Bohai Sea in the winter of 2009–2010 was investigated in this paper using MODIS night-time thermal infrared imagery.The cloud cover in the imagery was masked out manually.Level ice thickness was calculated using MODIS ice surface temperature and an ice surface heat balance equation.Weather forcing data was from the European Centre for Medium-Range Weather Forecasts(ECMWF) analyses.The retrieved ice thickness agreed reasonable well with in situ observations from two off-shore oil platforms.The overall bias and the root mean square error of the MODIS ice thickness are –1.4 cm and 3.9 cm,respectively.The MODIS results under cold conditions(air temperature –10°C) also agree with the estimated ice growth from Lebedev and Zubov models.The MODIS ice thickness is sensitive to the changes of the sea ice and air temperature,in particular when the sea ice is relatively thin.It is less sensitive to the wind speed.Our method is feasible for the Bohai Sea operational ice thickness analyses during cold freezing seasons. 相似文献
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Numerical sea ice prediction in China 总被引：5，自引：2，他引：3
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