2010-2018年北极夏季中国北极科学考察航行期间被动微波遥感海冰密集度与船基目视观测资料的比较

为了更有效地将卫星数据应用于北极航行导航，被动微波（PM）产品的海冰密集度（SIC）与从中国北极科学考察中收集到的船基目视观测（OBS）资料进行了比较。在2010、2012、2014、2016和2018年的北极夏季总共收集了3667组目测数据。PM SIC取自基于SSMIS传感器的NASA-Team（NT）、Bootstrap（BT）以及Climate Data Record（CDR）算法和基于AMSR-E/AMSR-2传感器的BT、enhanced NT（NT2）以及ARTIST Sea Ice（ASI）算法。使用PM SIC的日算术平均值和OBS SIC的日加权平均值进行比较。比较了PM SIC和OBS SIC之间的相关系数，偏差和均方根偏差，包括总体趋势以及在轻度/普通/严重冰况下的情况。使用OBS数据，浮冰尺寸和冰厚对不同PM产品SIC反演的影响可以通过计算浮冰尺寸编码和冰厚的日加权平均值来评估。我们的结果显示相关系数的范围为0.89（AMSR-E/AMSR-2 NT2）到0.95（SSMIS NT），偏差的范围为-3.96%（SSMIS NT）到12.05%（AMSR-E/AMSR-2），均方根偏差的范围为10.81%（SSMIS NT）到20.15%（AMSR-E/AMSR-2 NT2）。浮冰尺寸对PM产品的SIC反演有显著的影响，大多数PM产品倾向于在小浮冰尺寸情况下低估SIC，而在大浮冰尺寸情况下高估SIC。超过30 cm的冰厚对于PM产品的SIC反演没有明显影响。总体来看，在北极夏季，SSMIS NT SIC与OBS SIC之间有着最好的一致性，而AMSR-E/AMSR-2 NT2 SIC与OBS SIC的一致性最差。     

两种准实时遥感海冰密集度产品在中国第五次北极考察期间的适用性评估

比较了AMSR2和SSMIS产品在2012年中国第五次北极考察期间的差异,并利用雪龙船在北极走航观测的海冰密集度资料初步评估了两种卫星产品在北极东北航道和高纬航道的适用性。结果表明:两种产品在海冰边缘区域反演的海冰密集度差异较大,且在高纬度区域AMSR2反演的密集度普遍大于SSMIS;两种产品对海冰外缘线的反演基本相同,说明两种算法对海冰和海水的区分基本一致;在去程低纬航线上分辨率较高的AMSR2数据的平均偏差为0.14±0.11,而分辨率较低的SSMIS数据为0.17±0.11;在回程高纬航线上AMSR2数据的平均偏差为0.11±0.10,而SSMIS数据为0.11±0.12。SSMIS数据在高值区明显的低估了海冰密集度值,说明其在高值区的反演上存在系统性偏差,AMSR2数据和走航观测数据更相符。SSMIS数据在高值区偏差大的原因可能与其反演算法对海冰表面出现的大量融池的辨别能力较差有关。     

基于19GHz修正91GHz频段改进的ASI海冰密集度算法

基于数据融合算法思想，利用低频修正高频微波数据提出改进的ASI海冰密集度反演算法，对北极海冰进行反演研究。目前用于整体海冰密集度反演的算法中，使用低频数据的算法受天气影响较弱，但空间分辨率相对较低；而使用高频数据的算法，空间分辨率相对较高，但受天气影响较大，虽然使用天气滤波器处理，能消除那些被误判成海冰的水点，但并没有改变冰点的密集度。改进的ASI算法，利用低频数据（19GHz）修正高频数据（85.5GHz），进而得到修正后的85.5GHz的极化差P''，将P带入ASI算法，最终得到以2008-2016年每年的1月3日SSMIS数据为例的北冰洋整体海冰密集度反演结果。结果表明，改进后的ASI算法得到的总体海冰面积介于ASI与NASA Team两个结果之间；在边缘海冰区，改进后的ASI算法结果与传统的ASI算法结果在海冰面积与平均海冰密集度上都有较大差异，且前者更接近NASA Team算法。因此改进后的ASI算法，在空间分辨率上优于NASA Team算法，在受天气影响程度上更弱于ASI算法，并且有效变了边缘海冰区像元的海冰密集度。     

基于卫星观测的2003-2013年北极海冰体积变化估算

北极海冰正处于快速减退时期，北极海冰体积变化是全球气候变化的重要指示因子。本文利用两种卫星高度计数据（ICESat和CryoSat-2）反演得到的海冰厚度数据，结合星载辐射计提取的海冰密集度数据以及海冰年龄数据，估算了近期的北极海冰体积以及一年冰和多年冰体积变化。CryoSat-2观测时段（2011-2013年）与ICESat观测时段（2003-2008年）相比，北极海冰体积在秋季（10-11月）和冬季（2-3月）分别减少了1 426 km3和412 km3。其中，秋季和冬季的一年冰的体积增加了702 km3和2 975 km3。相反，多年冰分别减少了2 108 km3和3 206 km3。多年冰的大量流失是造成北极海冰净储量下降的主要原因。     

基于SMAP卫星雷达资料的海冰检测技术

利用SMAP卫星雷达资料与美国国家冰雪数据中心(NSIDC)发布的近实时逐日极区网格化海冰密集度数据建立匹配数据集,分析了海冰和海水的L波段雷达后向散射特性差异,建立了基于线性判别分析算法的海冰检测算法。选择Sentinel-1A SAR极地地区的海冰影像对SMAP卫星雷达资料海冰检测产品进行实验验证,结果显示二者的海冰边缘线一致,说明SMAP海冰检测算法具有较高的精度。利用SMAP卫星雷达资料制作了北极和南极地区海冰覆盖图,计算了海冰覆盖面积,通过与美国国家冰雪数据中心(NSIDC)海冰覆盖面积比较发现,SMAP检测的北极地区海冰面积略大于NSIDC,相对偏差为3.3%,SMAP检测的南极地区海冰面积略小于NSIDC,相对偏差为1.8%,表明二者的覆盖面积基本一致,证实了SMAP海冰检测算法的精度。     

国内外7种常用南极被动微波海冰密集度产品的比较与评估

围绕国内外机构发布的南极被动微波海冰密集度产品（PM-SIC）的差异和精度问题，应用MODIS和Sentinel-1反演的海冰密集度，对德国不莱梅大学（产品UB-AMSR2/ASI）、美国冰雪数据中心（产品NSIDC-SSMIS/NT、NSIDC-SSMIS/CDR、NSIDC-AMSR2/NT2）、欧洲气象卫星应用组织海洋与海冰卫星应用中心（产品OSI-SAF/BR-BST）、国家卫星海洋应用中心（产品NSOAS-SMR/NT）和国家卫星气象中心（产品NSMC-MWRI/NT2）发布的7种南极海冰密集度产品进行比较与评估。结果表明：（1）NSIDC-SSMIS/NT与NSIDC-SSMIS/CDR海冰密集度具有较高的一致性（平均偏差为-0.08%，相关系数为0.99）,NSOAS-SMR/NT与NSIDC-AMSR2/NT2间的差异最大（平均偏差为-14.41%，相关系数为0.81）;(2)7种PM-SIC的变化趋势一致，NSOAS-SMR/NT和NSMC-MWRI/NT2与其他PM-SIC的偏差具有明显的季节性差异；（3）NSOAS-SMR/NT和NSMC-MWRI/NT2与其他P...     

基于SMAP卫星雷达资料的海冰密集度反演技术研究

SMAP是美国于2015年初发射的一颗卫星，搭载了L波段的雷达。它采用圆锥扫描方式，具有固定的入射角、较大的幅宽和千米级的分辨率，在海冰监测方面具有独特的优势。本文利用SMAP卫星雷达资料分别与德国Bremen大学海冰密集度产品和美国国家冰雪数据中心（NSIDC）海冰密集度产品建立3.125 km和25 km匹配数据集，分析了L波段雷达后向散射系数、极化比和归一化极化差与海冰密集度之间相关性，建立基于人工神经网络的海冰密集度反演算法。为了验证SMAP卫星雷达资料反演海冰密集度的精度，本文选择德国Bremen大学和美国冰雪数据中心发布的海冰密集度产品分别与SMAP海冰密集度产品进行对比分析，SMAP海冰密集度与Bremen海冰密集度的偏差为0.07、均方根误差为0.14；与NSIDC海冰密集度的偏差为0.04、均方根误差为0.18，这表明SMAP海冰密集度产品与现有业务化海冰密集度产品具有很好的一致性。     

北极海冰密集度数值预报及验证评估研究

本文系统地评估了国家海洋环境预报中心于我国第七次北极科学考察期间开展的北极海冰密集度数值预报结果。该预报系统基于麻省理工大学通用环流模式，并采用牛顿松弛逼近（Nudging）资料同化方法，计算输出未来1~5 d的北极海冰密集度预报产品。本文将数值预报结果同卫星观测的海冰密集度、再分析资料和"雪龙"号第七次北极考察期间观测的海冰密集度数据进行了对比分析。结果表明，预报的北极海冰密集度小于卫星观测值，24 h、72 h和120 h预报结果的偏差分别为-2.7%、-3.1%和-3.2%；数值产品的预报技巧好于气候态结果和惯性预报，但是在海冰出现快速融化或冻结时，基于Nudging同化的数值预报技巧仍有不足。另外，相比船测数据，数值预报结果在海冰边缘区的偏差相对较大，24 h、72 h和120 h预报结果的偏差分别为8.8%、12.0%和14.5%。     

巴芬湾西部和格陵兰岛东部大气经向热量输送协同变化对夏季北极海冰的影响

利用欧洲中心气候再分析资料和美国国家冰雪数据中心北极海冰面积资料，分析了夏季北极海冰面积与前期大气经向热量输送年际变化的联系。结果表明：6月北半球中高纬大气的经向热量输送以瞬变热量形式为主，其中巴芬湾西部（B区）和格陵兰岛东部（G区）是瞬变热量向极区传输的两个通道，二者之间存在反位相的协同变化，且这种协同变化与夏季北极海冰面积变化密切相关。可能的机制为：6月，AD、AO和NAO三种北极大气环流型能够引起巴芬湾西部和格陵兰岛东部瞬变热量输送的协同变化，这种协同变化通过涡旋动力作用激发夏季极区大气表现为AD异常，同时影响途经区域的气温，从而通过热动力作用影响夏季北极海冰。将向极区输送的热量称为暖输送，从极区输出的热量为冷输送，则上述两个区域的瞬变热量协同输送可分为三种情况：B暖G冷、B冷G暖、B和G均冷，而B和G均暖的情况十分罕见。当B区向极区输入、G区输出热量时，有利于太平洋扇区和喀拉海的海冰偏少；当G区输入、B区输出热量时，利于喀拉海和拉普捷夫海海冰偏少；当B区和G区均输出热量时，利于波佛特海南部、喀拉海和拉普捷夫海海冰偏多，反之则相反。     

基于CryoSat-2卫星测高数据的北极海冰体积估算方法

近30年来,北极海冰正发生着剧烈的变化。海冰体积是量化海冰变化的重要指标之一。本文以2015年CryoSat-2卫星测高数据和OSI SAF海冰类型产品为基础。提取了浮冰出水高度、积雪深度、海冰密集度、海冰类型等属性信息,通过数据内插、投影变换、栅格转换、空间重采样等工作将海冰属性信息统一为25 km×25 km分辨率的栅格数据集。根据流体静力学平衡原理,逐个估算栅格像元对应的海冰厚度值,将其与对应的海冰面积相乘,估算了北极海冰密集度大于75%海域的海冰体积,并分析了海冰厚度和体积的月变化和季节变化特征。用NASA IceBridge海冰厚度产品对反演的海冰厚度进行验证。结果表明二者相关系数为0.72,有较高的一致性。北极海冰平均厚度春季最大,夏季最小,分别约为2.99 m和1.77 m,最厚的海冰集中在格陵兰沿岸北部和埃尔斯米尔半岛以北海域。多年冰平均厚度大于一年冰。冬季海冰体积最大,约为23.30×103 km3,经过夏季的融化,减少了近70%。一年冰体积季节波动较大,而多年冰体积相对稳定,季节变化不明显。     

Comparisons of passive microwave remote sensing sea ice concentrations with ship-based visual observations during the CHINARE Arctic summer cruises of 2010–2018

In order to apply satellite data to guiding navigation in the Arctic more effectively, the sea ice concentrations(SIC)derived from passive microwave(PM) products were compared with ship-based visual observations(OBS)collected during the Chinese National Arctic Research Expeditions(CHINARE). A total of 3 667 observations were collected in the Arctic summers of 2010, 2012, 2014, 2016, and 2018. PM SIC were derived from the NASA-Team(NT), Bootstrap(BT) and Climate Data Record(CDR) algorithms based on the SSMIS sensor, as well as the BT,enhanced NASA-Team(NT2) and ARTIST Sea Ice(ASI) algorithms based on AMSR-E/AMSR-2 sensors. The daily arithmetic average of PM SIC values and the daily weighted average of OBS SIC values were used for the comparisons. The correlation coefficients(CC), biases and root mean square deviations(RMSD) between PM SIC and OBS SIC were compared in terms of the overall trend, and under mild/normal/severe ice conditions. Using the OBS data, the influences of floe size and ice thickness on the SIC retrieval of different PM products were evaluated by calculating the daily weighted average of floe size code and ice thickness. Our results show that CC values range from 0.89(AMSR-E/AMSR-2 NT2) to 0.95(SSMIS NT), biases range from-3.96%(SSMIS NT) to 12.05%(AMSR-E/AMSR-2 NT2), and RMSD values range from 10.81%(SSMIS NT) to 20.15%(AMSR-E/AMSR-2 NT2). Floe size has a significant influence on the SIC retrievals of the PM products, and most of the PM products tend to underestimate SIC under smaller floe size conditions and overestimate SIC under larger floe size conditions. Ice thickness thicker than 30 cm does not have a significant influence on the SIC retrieval of PM products. Overall, the best(worst) agreement occurs between OBS SIC and SSMIS NT(AMSR-E/AMSR-2 NT2) SIC in the Arctic summer.     

Arctic sea ice concentration retrieval using the DT-ASI algorithm based on FY-3B/MWRI data

Sea ice concentration (SIC) is one of the most important indicators when monitoring climate changes in the polar region. With the development of the Chinese satellite technology, the FengYun (FY) series has been applied to retrieve the sea ice parameters in the polar region. In this paper, to improve the SIC retrieval accuracy from the passive microwave (PM) data of the Microwave Radiation Imager (MWRI) aboard on the FengYun-3B (FY-3B) Satellite, the dynamic tie-point (DT) Arctic Radiation and Turbulence Interaction Study (ARTIST) Sea Ice (ASI) (DT-ASI) SIC retrieval algorithm is applied and obtained Arctic SIC data for nearly 10 a (from November 18, 2010 to August 19, 2019). Also, by applying a land spillover correction scheme, the erroneous sea ice along coastlines in melt season is removed. The results of FY-3B/DT-ASI are obviously improved compared to that of FY-3B/NT2 (NASA-Team2) in both SIC and sea ice extent (SIE), and are highly consistent with the results of similar products of AMSR2 (Advanced Microwave Scanning Radiometer 2)/ASI and AMSR2/DT-ASI. Compared with the annual average SIC of FY-3B/NT2, our result is reduced by 2.31％. The annual average SIE difference between the two FY- 3Bs is 1.65×106 km2, of which the DT-ASI algorithm contributes 87.9％ and the land spillover method contributes 12.1％. We further select 58 MODIS (Moderate-resolution Imaging Spectroradiometer) cloud-free samples in the Arctic region and use the tie-point method to retrieve SIC to verify the accuracy of these SIC products. The root mean square difference (RMSD) and mean absolute difference (MAD) of the FY-3B/DT-ASI and MODIS results are 17.2％ and 12.7％, which is close to those of two AMSR2 products with 6.25 km resolution and decreased 8％ and 7.2％ compared with FY-3B/NT2. Further, FY-3B/DT-ASI has the most significant improvement where the SIC is lower than 60％. A high-quality SIC product can be obtained by using the DT-ASI algorithm and our work will be beneficial to promote the application of FengYun Satellite.     

基于FY-3C微波辐射计数据的极区海冰密集度反演方法研究

极区海冰影响大气和海洋环流，对全球气候变化起着重要的作用。海冰密集度是表征海冰时空变化特征的重要参数之一。本文研究了利用FY-3C微波扫描辐射计亮温数据反演极区海冰密集度的方法。经过时空匹配、线性回归，修正了FY-3C微波辐射计亮温数据。使用两种天气滤波器和海冰掩模滤除了大气影响所造成的开阔海域虚假海冰；使用最小密集度模板去除陆地污染效应。通过计算2016年、2017年极区海冰面积及范围两个参数，对得到的海冰密集度产品进行了验证，两年的海冰范围和面积趋势基本与NSIDC产品一致，平均差异小于3%。本研究结果为发布我国自主卫星的极区海冰密集度业务化产品奠定了基础，制作的产品可保障面临中断的40多年极区海冰记录的连续性。     

利用2012年中国北极考察现场观测数据和卫星遥感资料评估海洋2号卫星扫描辐射计数据反演北极海冰密集度的研究

A retrieval algorithm of arctic sea ice concentration(SIC) based on the brightness temperature data of "HY-2" scanning microwave radiometer has been constructed. The tie points of the brightness temperature were selected based on the statistical analysis of a polarization gradient ratio and a spectral gradient ratio over open water(OW), first-year ice(FYI), and multiyear ice(MYI) in arctic. The thresholds from two weather filters were used to reduce atmospheric effects over the open ocean. SIC retrievals from the "HY-2" radiometer data for idealized OW, FYI, and MYI agreed well with theoretical values. The 2012 annual SIC was calculated and compared with two reference operational products from the National Snow and Ice Data Center(NSIDC) and the University of Bremen. The total ice-covered area yielded by the "HY-2" SIC was consistent with the results from the reference products. The assessment of SIC with the aerial photography from the fifth Chinese national arctic research expedition(CHINARE) and six synthetic aperture radar(SAR) images from the National Ice Service was carried out. The "HY-2" SIC product was 16% higher than the values derived from the aerial photography in the central arctic. The root-mean-square(RMS) values of SIC between "HY-2" and SAR were comparable with those between the reference products and SAR, varying from 8.57% to 12.34%. The "HY-2" SIC is a promising product that can be used for operational services.     

一次新的厄尔尼诺事件即将形成

一次新的厄尔尼诺事件即将形成宋家喜（国家海洋环境预报中心，北京）在１９９１～１９９５年的五年中，赤道东太平洋共发生了三大厄尔尼诺事件，即１９９１年５月至１９９２年６月，１９９３年３月至１０月，１９９４年１０月至１９９５年２月。也有人认为是一次长厄尔尼...     

基于HY-2A散射计数据的极区海冰范围反演方法及其评估

A sea ice extent retrieval algorithm over the polar area based on scatterometer data of HY-2A satellite has been established.Four parameters are used for distinguishing between sea ice and ocean with Fisher's linear discriminant analysis method.The method is used to generate polar sea ice extent maps of the Arctic and Antarctic regions of the full 2013–2014 from the scatterometer aboard HY-2A(HY-2A-SCAT) backscatter data.The time series of the ice mapped imagery shows ice edge evolution and indicates a similar seasonal change trend with total ice area from DMSP-F17 Special Sensor Microwave Imager/Sounder(SSMIS) sea ice concentration data.For both hemispheres,the HY-2A-SCAT extent correlates very well with SSMIS 15% extent for the whole year period.Compared with Synthetic Aperture Radar(SAR) imagery,the HY-2A-SCAT ice extent shows good correlation with the Sentinel-1 SAR ice edge.Over some ice edge area,the difference is very evident because sea ice edges can be very dynamic and move several kilometers in a single day.     

Recent satellite-derived sea ice volume flux through the Fram Strait: 2011–2015

The Fram Strait(FS) is the primary region of sea ice export from the Arctic Ocean and thus plays an important role in regulating the amount of sea ice and fresh water entering the North Atlantic seas. A 5 a(2011–2015) sea ice thickness record retrieved from Cryo Sat-2 observations is used to derive a sea ice volume flux via the FS. Over this period, a mean winter accumulative volume flux(WAVF) based on sea ice drift data derived from passivemicrowave measurements, which are provided by the National Snow and Ice Data Center(NSIDC) and the Institut Francais de Recherche pour d'Exploitation de la Mer(IFREMER), amounts to 1 029 km~3(NSIDC) and1 463 km~3(IFREMER), respectively. For this period, a mean monthly volume flux(area flux) difference between the estimates derived from the NSIDC and IFREMER drift data is –62 km~3 per month(–18×10~6 km~2 per month).Analysis reveals that this negative bias is mainly attributable to faster IFREMER drift speeds in comparison with slower NSIDC drift data. NSIDC-based sea ice volume flux estimates are compared with the results from the University of Bremen(UB), and the two products agree relatively well with a mean monthly bias of(5.7±45.9) km~3 per month for the period from January 2011 to August 2013. IFREMER-based volume flux is also in good agreement with previous results of the 1990 s. Compared with P1(1990/1991–1993/1994) and P2(2003/2004–2007/2008), the WAVF estimates indicate a decline of more than 600 km~3 in P3(2011/2012–2014/2015). Over the three periods, the variability and the decline in the sea ice volume flux are mainly attributable to sea ice motion changes, and second to sea ice thickness changes, and the least to sea ice concentration variations.