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1.
Analysis of multi-dimensional SAR for determining the thickness of thin sea ice in the Bohai Sea 总被引：1，自引：0，他引：1
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. 相似文献
2.
TANG Zhi-li 《极地研究(英文版)》2008,19(2):149-158
An overview of the seasonal variation of sea-ice cover in Baffin Bay and the Labrador Sea is given. A coupled ice-ocean model, CECOM, has been developed to study the seasonal variation and associated ice-ocean processes. The sea-ice component of the model is a multi-category ice model in which mean concentration and thickness are expressed in terms of a thickness distribution function. Ten categories of ice thickness are specified in the model. Sea ice is coupled dynamically and thermodynamically to the Princeton Ocean Model. Selected results from the model including the seasonal variation of sea ice in Baffin Bay, the North Water polynya and ice growth and melt over the Labrador Shelf are presented. 相似文献
3.
Based on the field data acquired in the program of fast ice observation off Zhongshan Station, Prydz Bay, East Antarctica during the austral summer 2005/ 2006, physical properties evolution of fast ice during the ice ablation season is analyzed in detail. Results show that the annual maximum ice thickness in 2005 occurred in later November, and then ice started to reek, and the ablation duration was 62 days; sea water under the ice became warmer synchronously; corresponding to the warming sea ice temperature, a ＂relative cold mid-layer＂ appeared in sea ice; the fast ice marginal line recoiled back to the shore observably, and the recoil distance was 20.9 km from 18 December 2005 through 14 January 2006. In addition, based on the data of sea ice thickness survey along the investigation course of MV Xuelong on December 18 of 2005, the ice thickness distribution paten in the marginal ice zone have been described ： sea ice thickness increased, but the diversity of floe ice thick-ness decreased from open water to fast ice zone distinctly. 相似文献
4.
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。 相似文献
5.
利用CryoSat-2卫星测高数据反演波弗特海的海冰厚度，并利用2010~2013年10月份仰视声呐(ULS)和2011年冰桥计划（IceBridge）数据对结果进行精度评估。结果表明，测高反演的海冰吃水深度与ULS吃水深度差值的最大值和标准差分别为14 cm和4 cm；测高反演的海冰厚度与冰桥计划海冰厚度差值的平均值和标准差分别为2.7 cm和65.7 cm，优于Laxon(2013)研究结果（分别优化2.1 cm和6.6 cm）。在此基础上，研究2011~2017年波弗特海夏冬两季的海冰厚度变化，发现二者具有类似的分布特征，且冬季3月海冰覆盖范围更广，厚度更大；进一步分析2011~2017年3月份冬季海冰厚度年际变化，发现其呈整体下降趋势，且2012年最小，2014年最大。 相似文献
6.
Yan Jiao Fei Huang Qingrong Liu Ge Li Yaru Li Qingxi Yu Yiding Zhao 《中国海洋大学学报(英文版)》2020,19(2):272-280
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. 相似文献
7.
1 IntroductionSeaice ,asanimportantcomponentoftheArcticclimatesystem ,hasdrawnsignifi cantscientificinterest.Seaicethicknessanditsmorphologyhavedramaticimpactsono cean atmosphere iceinteractions(Wadhams 1 994;Barryetal.1 993 ;Dickson 1 999;PadhamsandNorman 2 0 0 0 ) ,whichdirectlyaffecttheexchangeprocessandspeedofheatandmassbetweentheoceanandtheatmosphere ,dominatethephysicalmechanicsfea turesofseaice ,andaffecttheseaicemovement&deformationaswellasicefreezing&meltingprocess(Hollandetal.1 99… 相似文献
8.
As an important part of global climate system, the Polar sea ice is effccting on global climate changes through ocean surface radiation balance, mass balance, energy balance as well as the circulating of sea water temperature and salinity. Sea ice research has a centuries - old history. The many correlative sea ice projects were established through the extensive international cooperation during the period from the primary research of intensity and the boaring capacity of sea ice to the development of sea/ice/air coupled model. Based on these reseamhes, the sea ice variety was combined with the global climate change. All research about sea ice includes： the physical properties and processes of sea ice and its snow cover, the ecosystem of sea ice regions, sea ice and upper snow albedo, mass balance of sea ice regions, sea ice and climate coupled model. The simulation suggests that the both of the area and volume of polar sea ice would be reduced in next century. With the developing of the sea ice research, more scientific issues are mentioned. Such as the interaction between sea ice and the other factors of global climate system, the seasonal and regional distribution of polar sea ice thickness, polar sea ice boundary and area variety trends, the growth and melt as well as their influencing factors, the role of the polynya and the sea/air interactions. We should give the best solutions to all of the issues in future sea ice studying. 相似文献
9.
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. 相似文献
10.
Clara Deal 《极地研究(英文版)》2008,19(2):218-229
Primary production in the Bering and Chukchi Seas is strongly influenced by the annual cycle of sea ice. Here pelagic and sea ice algal ecosystems coexist and interact with each other. Ecosystem modeling of sea ice associated phytoplankton blooms has been understudied compared to open water ecosystem model applications. This study introduces a general coupled ice-ocean ecosystem model with equations and parameters for 1-D and 3-D applications that is based on 1-D coupled ice-ocean ecosystem model development in the landfast ice in the Chukchi Sea and marginal ice zone of Bering Sea. The biological model includes both pelagic and sea ice algal habitats with 10 compartments： three phytoplankton （pelagic diatom, flagellates and ice algae： D, F, and Ai） , three zooplankton （copepods, large zooplankton, and microzooplankton ： ZS, ZL, ZP） , three nutrients （ nitrate ＋ nitrite, ammonium, silicon ： NO3 , NH4, Si） and detritus （Det）. The coupling of the biological models with physical ocean models is straightforward with just the addition of the advection and diffusion terms to the ecosystem model. The coupling with a multi-category sea ice model requires the same calculation of the sea ice ecosystem model in each ice thickness category and the redistribution between categories caused by both dynamic and thermodynamic forcing as in the physical model. Phytoplankton and ice algal self-shading effect is the sole feedback from the ecosystem model to the physical model. 相似文献
11.
Snow and sea ice thermodynamics in the Arctic： Model validation and sensitivity study against SHEBA data 总被引：2，自引：0，他引：2
Timo Vihma 《极地研究(英文版)》2008,19(2):108-122
Evolution of the Arctic sea ice and its snow cover during the SHEBA year were simulated by applying a high-resolution thermodynamic snow/ice model （HIGHTSI）. Attention was paid to the impact of albedo on snow and sea ice mass balance, effect of snow on total ice mass balance, and the model vertical resolution. The SHEBA annual simulation was made applying the best possible external forcing data set created by the Sea Ice Model Intercomparison Project. The HIGHTSI control run reasonably reproduced the observed snow and ice thickness. A number of albedo schemes were incorporated into HIGHTSI to study the feedback processes between the albedo and snow and ice thickness. The snow thickness turned out to be an essential variable in the albedo parameterization. Albedo schemes dependent on the surface temperature were liable to excessive positive feedback effects generated by errors in the modelled surface temperature. The superimposed ice formation should be taken into account for the annual Arctic sea ice mass balance. 相似文献
12.
The maximum entropy distribution, which consists of various recognized theoretical distributions, is a better curve to estimate the design thickness of sea ice. Method of moment and empirical curve fitting method are common-used parameter estimation methods for maximum entropy distribution. In this study, we propose to use the particle swarm optimization method as a new parameter estimation method for the maximum entropy distribution, which has the advantage to avoid deviation introduced by simplifications made in other methods. We conducted a case study to fit the hindcasted thickness of the sea ice in the Liaodong Bay of Bohai Sea using these three parameter-estimation methods for the maximum entropy distribution. All methods implemented in this study pass the K-S tests at 0.05 significant level. In terms of the average sum of deviation squares, the empirical curve fitting method provides the best fit for the original data, while the method of moment provides the worst. Among all three methods, the particle swarm optimization method predicts the largest thickness of the sea ice for a same return period. As a result, we recommend using the particle swarm optimization method for the maximum entropy distribution for offshore structures mainly influenced by the sea ice in winter, but using the empirical curve fitting method to reduce the cost in the design of temporary and economic buildings. 相似文献
13.
The sea ice community plays an important role in the Arctic marine ecosystem. Because of the predicted environmental changes in the Arctic environment and specifically related to sea ice, the Arctic pack ice biota has received more attention in recent years using modem ice-breaking research vessels. Studies show that the Arctic pack ice contains a diverse biota and besides ice algae, the bacterial and protozoan biomasses can be high. Surprisingly high primary production values were observed in the pack ice of the central Arctic Ocean. Occasionally biomass maximum were discovered in the interior of the ice floes, a habitat that had been ignored in most Arctic studies. Many scientific questions, which deserve special attention, remained unsolved due to logistic limitations and the sea ice characteristics. Little is know about the pack ice community in the central Arctic Ocean. Almost no data exists from the pack ice zone for the winter season. Concerning the abundance of bacteria and protozoa, more studies are needed to understand the microbial network within the ice and its role in material and energy flows. The response of the sea ice biota to global change will impact the entire Arctic marine ecosystem and a long-term monitoring program is needed. The techniques, that are applied to study the sea ice biota and the sea ice ecology, should be improved. 相似文献
14.
One of sea ice core samples was taken from Arctic by the First Chinese National Arctic Research Expedition Team in 1999. 20 vertical and 2 horizontal ice sections were cut out of the ice core sample 2.22 m in length, which covered the ice sheet from surface to bottom except losses for during sampling and section cutting. From the observation and analysis of the fabrics and crystals along the depth of the ice core sample, followings were found. Whole ice sheet consists of columnar, refrozen clastic pieces, granular, columnar, refrozen clastic pieces, granular, columnar and refrozen clastic pieces. This indicates that the ice core sample was 3-year old, and the ice sheet surface thawed and the melt water flowed into ice sheet during summer. Hence, the annual energy balance in Arctic can be determined by the ice sheet surface thawing in summer, and bottom growth in winter. The thickness of the ice sheet is kept constantly at a certain position based on the corresponding climate and ocean conditions; A new 相似文献
15.
北极海冰范围时空变化及其与海温气温间的数值分析 总被引：1，自引：0，他引：1
本文利用美国国家冰雪中心提供的1989-2014年海冰范围资料,分析了北极海冰范围的年际变化和季节变化规律。分析发现,北极海冰范围呈减少趋势,每年减小
16.
Remote sensing data from passive microwave and satellite-based altimeters, associated with the data measured underway, were used to characterize seasonal and spatial changes in sea ice conditions along... 相似文献
17.
In this study, we used Landsat images and meteorological data to examine the spatiotemporal distribution and variability of sea ice in Jiaozhou Bay(JZB) between 1986 and 2016. The results show that JZB is not always covered by sea ice in winter, but in some extreme cases, sea ice has covered more than one-third of the sea area of the bay. Sea ice in JZB has generally formed between January 1 and February 5, primarily along the coast, and gradually expanding to the central area of the bay. Both meteorological and artificial factors have played important roles in modulating the sea ice distribution. We found sea ice coverage to have been strongly correlated with the accumulated freezing-degree days nine days before the occurrence of sea ice(R2 = 0.767). North-northwest surface winds have dominated the freezing period of sea water in the JZB, and wind speed has exerted a more significant influence on the formation of sea ice when the sea ice coverage has been generally small. Additionally, artificial factors began to affect the expansion of sea ice in JZB since 2007. The construction of the Jiao-Zhou-Bay Bridge(JZBB) is believed to have retarded water flow and reduced the tidal prism, thereby leading to the formation of an ice bridge along the JZBB, which effectively prevents the southward expansion of sea ice. 相似文献
18.
《中国海洋湖沼学报》2019,(6)
Sea ice disaster is one of the principal natural hazards that affect some coastal areas of China, and the formation of ice cover in a wave field has important characteristics. However, analysis of the mechanism in which waves affect the thermodynamic process of sea ice is lacking, and the influence of waves is not taken into consideration in numerical models of sea ice, largely because of a lack of simultaneous observations of waves and sea ice. Using observational data of the sea ice cycle in the coastal waters of Liaodong Bay(China), we analyzed the characteristics of hydrology, meteorology, and sea ice thickness during the formation of sea ice, and explored the changes in the interrelationships among heat fluxes, waves, and sea ice under actual sea conditions. The results could provide a decision-making support as a reference to the establishment and improvement of China's early warning system to sea ice disasters, and the protection of ice drilling operations and production platform safety. 相似文献
19.
On small-meso scale, the sea ice dynamic characteristics are quite different from that on large scale. To model the sea ice dynamics on small-meso scale, a new elastic-viscous-plastic （EVP） constitutive model and a hybrid Lagrangian- Eulerian （HLE） numerical method are developed based on continuum theory. While a modified discrete element model （DEM） is introduced to model the ice cover at discrete state. With the EVP constitutive model, the numerical simulation for ice ridging in an idealized rectangular basin is carried out and the results are comparable with the analytical solution of jam theory. Adopting the HLE numerical model, the sea ice dynamic process is simulated in a vortex wind field. The furthering application of DEM is discussed in details for modeling the discrete distribution of sea ice. With this study, the mechanical and numerical models for sea ice dynamics can be improved with high precision and computational efficiency. 相似文献
20.
????????GPS??GRACE??????????????????????????????????????е????????ICE-5G and ICE-3G???????????????????????????????????????????е??????????????????????????????????????????????????仯????????????GRACE?????????????????ICE-5G????????????????????????????????????????????С~20%??????????????????????~40%?????????????????????α???????仯??GPS??GRACE???????????????????????????????????????????????????????Ч??? (~150 km??80%?????) ?????????????????????????~90 km); ????μ?????Ч??????????3.7 × 1020?? Pas ??1.9 × 1021?? Pas (90%?????)???????????????????????????С~20%?? 相似文献