CryoSat-2卫星海冰区域波形识别及海冰干舷高确定

利用40%阈值法对CryoSat-2卫星波形数据进行重跟踪,将波形特征参数和海冰浓度相结合,对海冰和Lead(浮冰之间的开阔水域)进行有效识别。利用沿轨前后搜索算法计算海冰干舷高,并引用AWI结果,绘制2011~2013年北冰洋多年冰区域和一年冰区域平均海冰干舷高变化趋势图。比较本文结果与AWI结果的各年同期数据,验证本文结果的可靠性。     

基于MODIS数据的气溶胶光学厚度反演

气溶胶在大气污染事件中扮演重要角色。光学厚度作为大气气溶胶的一个重要光学属性,常作为其他相关科学研究的重要参数。本文以ENVI5.0为平台,利用经典的暗像元算法,对2011年8月5日北京地区的MODIS卫星遥感影像数据进行了气溶胶光学厚度(AOD)反演,并对其空间分布进行了分析,为环境监测部门提供了大气污染治理依据。     

利用Cryosat-2 SAR模式数据确定北冰洋海平面模型

围绕Cryosat-2卫星SAR模式测高数据处理方法展开研究,利用决策树算法确定冰间水道观测值提取的参数和阈值,进而建立北冰洋海冰覆盖海域的海平面模型UST-CS2。结果表明,UST-CS2与DTU21海平面模型具有较高的一致性。ICESat-2激光测高数据的验证结果表明,UST-CS2的标准差为10.72 cm,与DTU21等模型精度基本持平,证明该模型具有可靠性。     

基于热红外数据的花岗岩铀矿田地表SiO_2含量反演及其铀成矿要素识别

ASTER遥感成像仪发射以来,提供了廉价的多光谱热红外遥感数据,为热红外遥感技术在诸多领域的应用注入了新的活力。本文在分析热红外技术在岩矿领域应用潜力的基础上,将热红外遥感技术应用于花岗岩铀矿找矿中,并以华南某花岗岩铀矿田为例,基于ASTER热红外数据进行了影像镶嵌、辐射定标、大气校正、温度/发射率分离,及其不同波段比值组合模拟的反演算法,对发射率数据进行SiO₂含量的定量反演,在反演图上识别出了研究区内明显的硅化断裂带、酸性岩及红层分布信息,识别结果与地质事实相符,并在野外得到了验证。实践表明,热红外遥感技术在花岗岩铀成矿要素提取中效果良好,并将在铀矿找矿工作中发挥积极的指导作用。     

基于GPS、InSAR和强震数据联合反演2017年九寨沟MS7.0地震同震滑动分布

以多源观测为约束,利用近场GPS、InSAR同震位移和强震观测数据联合反演2017年九寨沟M_S7.0地震发震断层的几何形状和破裂模型,并初步分析其地震成因。结果表明,九寨沟地震的发震断层走向154°,倾角80°,以左旋走滑为主,兼有少量正断分量;地震破裂主要集中在2~12 km深度范围,最大滑移量为1.6 m,位于地下6 km深处,断层在近地表没有滑移;地震释放的地震矩为5.59×10¹⁸ Nm,矩震级为M_W6.44。结合余震分布认为,该地震的发震断层为隐伏的虎牙断裂北段。     

利用GNSS反射信号监测海面高度变化——基于法国BRST站2019～2021年数据

GNSS反射测量(GNSS-R)技术凭借其数据来源广泛、低成本、高时空分辨率等优势,在地表与海洋环境监测等方面展现巨大潜力,已成为海面高度(SSH)反演的重要技术途径。现有研究大多聚焦于3～6个月内的短期GPS潮位反演,难以反映海面高度的季节性变化及年际特征,且在动态海面改正时仅考虑了垂向速度的影响,忽视了海面波动的垂向加速度,导致低潮位与高潮位的反演精度较差。基于此,以法国某一岸基跟踪站——BRST站为例,利用其连续3年的BDS/GPS/GLONASS/Galileo四系统反射信号,通过Lomb-Scargle谱分析和二阶动态潮位改正模型,采取稳健回归策略反演海面高度,并将最终结果与验潮站观测值进行对比,分析潮位变化趋势。结果表明：GNSS-R技术反演结果与验潮站观测值具有较好的一致性,反演精度有逐年提升的趋势,均方根误差(RMSE)为7.57 cm,相关系数为0.935;海面高度的季节性变化特征明显,秋、冬季平均海面高度偏高,夏季平均海面高度偏低,且海面高度的季节性变化与温度的季节性变化存在着相反的趋势;M2、S2、K1、O1、N2、K2、P1、Q1、M4等9个分潮的振幅差为0.0...     

Polar Sea Ice Identification and Classification Based on HY-2A/SCAT Data

In this paper,a Bayesian sea ice detection algorithm is first used based on the HY-2A/SCAT data,and a backpropagation(BP)neural network is used to classify the Arctic sea ice type.During the implementation of the Bayesian sea ice detection algorithm,linear sea ice model parameters and the backscatter variance suitable for HY-2A/SCAT were proposed.The sea ice extent obtained by the Bayesian sea ice detection algorithm was projected on a 12.5 km grid sea ice map and validated by the Advanced Microwave Scanning Radiometer 2(AMSR2)15%sea ice concentration data.The sea ice extent obtained by the Bayesian sea ice detection al-gorithm was found to be in good agreement with that of the AMSR2 during the ice growth season.Meanwhile,the Bayesian sea ice detection algorithm gave a wider ice edge than the AMSR2 during the ice melting season.For the sea ice type classification,the BP neural network was used to classify the Arctic sea ice type(multi-year and first-year ice)from January to May and October to De-cember in 2014.Comparison results between the HY-2A/SCAT sea ice type and Equal-Area Scalable Earth Grid(EASE-Grid)sea ice age data showed that the HY-2A/SCAT multi-year ice extent variation had the same trend as the EASE-Grid data.Classification errors,defined as the ratio of the mismatched sea ice type points between HY-2A/SCAT and EASE-Grid to the total sea ice points,were less than 12%,and the average classification error was 8.6%for the study period,which indicated that the BP neural network classification was a feasible algorithm for HY-2A/SCAT sea ice type classification.     

Definition of Arctic and Antarctic Sea Ice Variation Index

1　Introduction Itiswellknownthatseaiceinthepolarregionplaysanimportantroleintheglobal climatechangesasapartofclimatesystem(Carleton1989;YuanandMartinson2000, 2001;ChengandBian2002;LiuandMartinson2002;LiuandZhang2004;Gigorand Wallace2002etal).Infact,numerousmodelingstudiessuggestanimportantinfluence throughtheseaicefieldsalone(Grumbine1994,Meehl1990,Rindetal.1995).Inor dertounderstandthevariabilityofArcticandAntarcticseaicealongwiththepossiblecon nectionswithclimaticanomaliesindetail…     

Interannual Variability and Scenarios Projection of Sea Ice in Bohai Sea Part I: Variation Characteristics and Interannual Hindcast

The Bohai Sea is one of the southernmost areas for sea ice formation in the northern hemisphere.Sea ice disasters in this body of water severely affect marine activities and the safety of coastal residents.In this study,we analyze the variation characteristics of the sea ice in the Bohai Sea and establish an annual regression model based on predictable mode analysis method.The results show the following:1)From 1970 to 2018,the average ice grade is(2.6±0.8),with a maximum of 4.5 and a minimum of 1.0.Liaodong Bay(LDB)has the heaviest ice conditions in the Bohai Sea,followed by Bohai Bay(BHB)and Laizhou Bay(LZB).Interannual variation is obvious in all three bays,but the linear decreasing trend is significant only in BHB.2)Three modes are obtained from empirical orthogonal function analysis,namely,single polarity mode with the same sign of anomaly in all of the three bays and strong interannual variability(82.0%),the north–south dipole mode with BHB and LZB showing an opposite sign of anomalies to that in LDB and strong decadal variations(14.5%),and a linear trend mode(3.5%).Critical factors are analyzed and regression equations are established for all the principal components,and then an annual hindcast model is established by synthesizing the results of the three modes.This model provides an annual spatial prediction of the sea ice in the Bohai Sea for the first time,and meets the demand of operational sea ice forecasting.     

2015—2020年辽东湾海冰冰情时空特征及其影响因素

受冬季强寒潮侵袭,辽东湾会出现大范围结冰现象。为了分析2015—2020年辽东湾海冰冰情的变化规律与影响因素,本文选取Sentinel-1A/B数据开展辽东湾海冰监测。首先,采用巴氏距离选择最优纹理特征组合,再利用最大似然方法实现海冰分类;然后,根据上述海冰分类结果,分析海冰冰情等级、海冰外缘线、海冰面积、海冰类型和海冰结冰概率等冰情特征的变化规律;最后,研究海水深度、海温、气温和风速与海冰冰情的关系。主要结论如下：① 采用不同纹理特征组合方法和本文方法对2020年2月1日Sentinel-1B影像进行实验,结果表明本文方法的总体分类精度和Kappa系数分别为93.16%和0.85,分类精度最高。② 11月末到12月海冰类型以初生冰为主,间有灰冰;1月到2月中上旬以灰冰为主,间有初生冰和白冰;2月下旬到3月上旬的海冰类型以灰冰和初生冰为主。辽东湾内部结冰概率存在差异,北部沿岸结冰概率高于南部,东部结冰概率高于西部。辽东湾海冰冰情受海水深度、海温和气温影响明显,受风速影响较小。     

Modeling seasonal variations of ocean and sea ice circulation in the Beaufort and Chukchi Seas： A model-data fusion study

A 3.8-kin Coupled Ice-Ocean Model （C1OM） was implemented to successfully reproduce many observed phenomena in the Beaufort and Chukchi seas, including the Bering-inflow-originated coastal current that splits into three branches： Alaska Coastal Water （ACW） , Central Channel, and Herald Valley branches. Other modeled phenomena include the Beaufort Slope Current （BSC） , the Beaufort Gyre, the East Siberian Current （ ESC）, mesoscale eddies, seasonal landfast ice, sea ice ridging, shear, and deformation. Many of these downscaling processes can only be captured by using a high-resolution CIOM, nested in a global climate model. The seasonal cycles for sea ice concentration, thickness, velocity, and other variables are well reproduced with Solid validation by satellite measurements. The seasonal cycles for upper ocean dynamics and thermodynamics are also well reproduced, which include the formation of the cold saline layer due to the injection of salt during sea ice formation, the BSC, and the subsurface upwelling in winter that brings up warm, even more saline Atlantic Water along the shelfbreak and shelf along the Beaufort coast.     

Practical Model of Sea Ice Thickness of Bohai Sea Based on MODIS Data

Sea ice thickness is one of the most important input parameters in the studies on sea ice disaster prevention and mitigation. It is also the most important content in remote sensing monitoring of sea ice. In this study, a practical model of sea ice thickness (PMSIT) was proposed based on the Moderate Resolution Imaging Spectroradiometer (MODIS) data. In the proposed model, the MODIS data of the first band were used to estimate sea ice thickness and the difference between the second-band reflectance and the fifth-band reflectance in the MODIS data was calculated to obtain the difference attenuation index (DAI) of each pixel. The obtained DAI was used to estimate the integrated attenuation coefficient of the first band of the MODIS at the pixel level. Then the model was used to estimate sea ice thickness in the Bohai Sea with the MODIS data and then validated with the actual sea ice survey data. The validation results showed that the proposed model and corresponding parameterization scheme could largely avoid the estimation error of sea ice thickness caused by the spatial and temporal heterogeneity of sea ice extinction and allowed the error of 18.7% compared with the measured sea ice thickness.     

Analysis of multi-dimensional SAR for determining the thickness of thin sea ice in the Bohai Sea

Flat thin ice (<30 cm thick) is a common ice type in the Bohai Sea, China. Ice thickness detection is important to offshore exploration and marine transport in winter. Synthetic aperture radar (SAR) can be used to acquire sea ice data in all weather conditions, and it is a useful tool for monitoring sea ice conditions. In this paper, we combine a multi-layered sea ice electromagnetic (EM) scattering model with a sea ice thermodynamic model to assess the determination of the thickness of flat thin ice in the Bohai Sea using SAR at different frequencies, polarization, and incidence angles. Our modeling studies suggest that co-polarization backscattering coefficients and the co-polarized ratio can be used to retrieve the thickness of flat thin ice from C- and X-band SAR, while the co-polarized correlation coefficient can be used to retrieve flat thin ice thickness from L-, C-, and X-band SAR. Importantly, small or moderate incidence angles should be chosen to avoid the effect of speckle noise.     

Improvements in Long-Lead Prediction of Early-Summer Subtropical Frontal Rainfall Based on Arctic Sea Ice

Seasonal prediction of East Asia(EA) summer rainfall, especially with a longer-lead time, is in great demand, but still very challenging. The present study aims to make long-lead prediction of EA subtropical frontal rainfall(SFR) during early summer(May-June mean, MJ) by considering Arctic sea ice(ASI) variability as a new potential predictor. A MJ SFR index(SFRI), the leading principle component of the empirical orthogonal function(EOF) analysis applied to the MJ precipitation anomaly over EA, is defined as the predictand. Analysis of 38-year observations(1979-2016) revealed three physically consequential predictors. A stronger SFRI is preceded by dipolar ASI anomaly in the previous autumn, a sea level pressure(SLP) dipole in the Eurasian continent, and a sea surface temperature anomaly tripole pattern in the tropical Pacific in the previous winter. These precursors foreshadow an enhanced Okhotsk High, lower local SLP over EA, and a strengthened western Pacific subtropical high. These factors are controlling circulation features for a positive SFRI. A physical-empirical model was established to predict SFRI by combining the three predictors. Hindcasting was performed for the 1979-2016 period, which showed a hindcast prediction skill that was, unexpectedly, substantially higher than that of a four-dynamical models’ ensemble prediction for the 1979-2010 period(0.72 versus 0.47). Note that ASI variation is a new predictor compared with signals originating from the tropics to mid-latitudes. The long-lead hindcast skill was notably lower without the ASI signals included, implying the high practical value of ASI variation in terms of long-lead seasonal prediction of MJ EA rainfall.     

利用ICESat/GLAS数据获取北极海冰出水高度的研究

利用ICESat激光测高卫星数据获取2003-10～2008-03北冰洋秋季与冬季海冰出水高度,结果与国外相关研究基本一致。出水高度数据的拟合结果表明,北极海冰出水高度以每年约2.3 cm的速度递减,该速度比此前的相关研究结果更快。在不同作者利用ICESat数据计算的北极海冰出水高度的对比中,不同方法得到的结果之间存在明显的系统误差。对引起系统误差的原因进行深入分析表明,高程滤波的窗口长度、确定海面高的范围以及海面观测值的选取方式都会使出水高度的计算产生cm级的误差。