共查询到20条相似文献，搜索用时 406 毫秒
1.
北极海冰密集度动态系点值ASI反演算法研究 总被引：3，自引：0，他引：3
海冰密集度是极区海冰监测的重要因素,使用AMSR-E (Advanced Microwave Scanning Radiometer for Earth Observing System) 89GHz数据ASI反演算法得到的海冰密集度是目前能够获得的分辨率最高的微波数据.在以前的算法中往往使用固定的系点值,本研究实现了动态系点值ASI (the Arctic Radiation And Turbulence Interaction Study (ARTIST) Sea Ice)算法,更重要的是在统计开阔水系点值的时候消除了云对系点值的影响,使得纯水系点值更接近真实状况.得到2010年平均的开阔水和海冰的系点值分别为50.8K和7.8K,通过每天的系点值得到的反演方程在低密集度区增大了海冰密集度,在高密集冰区减小了海冰密集度,从而在一定程度上改善了微波数据的反演准确度.通过和北极区域选取40幅不受云影响的MODIS 500m分辨率宽频大气层顶反照率(broadband TOA albedo)计算的海冰密集度进行了比较验证.结果显示,40个个例中,95%本文的平均差异比使用固定系点值算法产品的小,而且75%的均方根差异比使用固定系点值算法产品的小. 相似文献
2.
基于AMSR-E数据的多年冰密集度反演算法研究 总被引：2，自引：1，他引：1
In recent years, the rapid decline of Arctic sea ice area(SIA) and sea ice extent(SIE), especially for the multiyear(MY) ice, has led to significant effect on climate change. The accurate retrieval of MY ice concentration retrieval is very important and challenging to understand the ongoing changes. Three MY ice concentration retrieval algorithms were systematically evaluated. A similar total ice concentration was yielded by these algorithms, while the retrieved MY sea ice concentrations differs from each other. The MY SIA derived from NASA TEAM algorithm is relatively stable. Other two algorithms created seasonal fluctuations of MY SIA, particularly in autumn and winter. In this paper, we proposed an ice concentration retrieval algorithm, which developed the NASA TEAM algorithm by adding to use AMSR-E 6.9 GHz brightness temperature data and sea ice concentration using 89.0GHz data. Comparison with the reference MY SIA from reference MY ice, indicates that the mean difference and root mean square(rms) difference of MY SIA derived from the algorithm of this study are 0.65×106 km2 and0.69×106 km2 during January to March, –0.06×106 km2 and 0.14×106 km2 during September to December respectively. Comparison with MY SIE obtained from weekly ice age data provided by University of Colorado show that, the mean difference and rms difference are 0.69×106 km2 and 0.84×106 km2, respectively. The developed algorithm proposed in this study has smaller difference compared with the reference MY ice and MY SIE from ice age data than the Wang's, Lomax' and NASA TEAM algorithms. 相似文献
3.
Sea-ice concentration is a key item in global climate change research.Recent progress in remotely sensed sea-ice concentration product has been stimulated by the use of a new sensor,advanced microwave scanning radiometer for EOS(AMSR-E),which offers a spatial resolution of 6 km×4 km at 89GHz.A new inversion algorithm named LASI(linear ASI) using AMSR-E 89GHz data was proposed and applied in the antarctic sea areas.And then comparisons between the LASI ice concentration products and those retrieved by the other two standard algorithms,ASI(arctic radiation and turbulence interaction study sea-ice algorithm) and bootstrap,were made.Both the spatial and temporal variability patterns of ice concentration differences,LASI minus ASI and LASI minus bootstrap,were investigated.Comparative data suggest a high result consistency,especially between LASI and ASI.On the other hand,in order to estimate the LASI ice concentration errors introduced by the tie-points uncertainties,a sensitivity analysis was carried out.Additionally an LASI algorithm error estimation based on the field measurements was also completed.The errors suggest that the moderate to high ice concentration areas(>70%) are less affected(never exceeding 10%) than those in the low ice concentration.LASI and ASI consume 75 and 112 s respectively when processing the same AMSR-E time series thourghout the year 2010.To conclude,by using the LASI algorithm,not only the seaice concentration can be retrieved with at least an equal quality as that of the two extensively demonstrated operational algorithms,ASI and bootstrap,but also in a more efficient way than ASI. 相似文献
4.
《Deep Sea Research Part I: Oceanographic Research Papers》2007,54(7):1189-1195
A new algorithm using a multivariate regression technique for retrieving sea surface specific humidity (Q) from remote sensing data from the Advanced Microwave Scanning Radiometer for EOS (AMSR-E) is proposed. Daily and monthly specific humidity data from the National Center for Environmental Prediction (NCEP) reanalysis dataset and data of sea surface temperature, atmospheric total water vapor, and wind speed from AMSR-E oceanographic products were used to derive the regression coefficients of the algorithm, and all the data for derivation are from the year 2003. An F-test was applied to the regression, and small P-values indicate that the regressions are significant to a high level of confidence. The derived coefficients have been validated using similar data from the year 2004. The root mean square (rms) error of the algorithm for daily retrieved Q over the global oceans is 1.05 g kg−1, and the rms error for monthly retrieved Q is 0.61 g kg−1. 相似文献
5.
渤海AVHRR多通道海冰密集度反演算法试验研究 总被引：2，自引：1，他引：1
为了得到更精确的渤海海冰密集度反演参数,采用辽东湾不同类型海冰ASD实测数据,在分析光谱特征的基础上,针对NOAA/AVHRR数据确定出合适海冰密集度反演算法阈值。继而,基于线性光谱混合模型的多通道反演算法进行了一系列算法试验。同时实现了基于LandSat5-TM数据的渤海海冰密集度场反演,并利用该结果与AVHRR单通道和多通道算法得到的海冰密集度反演结果进行比对分析。定量误差分析结果表明,当单通道算法或组合算法中包含1通道时,与Landsat5-TM反演结果的平均误差为正值,包含2通道且不包含1通道时,平均误差为负值;同时使用这两个通道较只包含其一的各种组合算法的平均误差明显偏小;在各种组合算法中,1245四个通道组合反演的海冰密集度结果误差最小,可应用于渤海AVHRR数据海冰密集度反演。 相似文献
6.
HY-2卫星扫描微波辐射计数据反演北极海冰漂移速度 总被引：1，自引：1，他引：0
本文基于最大互相关法,利用海洋二号（HY-2）卫星扫描微波辐射计37 GHz通道多时相垂直极化亮温数据,获取了北极海冰漂移速度。采用2012年和2013年国际北极浮标计划海冰现场观测数据,对利用微波辐射计亮温资料反演的冬季北极海冰漂移速度进行了定量验证,结果表明:流速和流向均方根误差分别为1.12 cm/s和16.37°,从一定程度上说明了HY-2卫星扫描微波辐射计亮温数据反演海冰漂移速度的可行性。此外,使用美国国防气象卫星F-17搭载的专用微波成像仪91 GHz通道垂直极化亮温,采用高斯拉普拉斯滤波方法进行处理,结合最大互相关法反演的海冰漂移速度,优于法国海洋开发研究院海冰漂移速度产品。 相似文献
7.
基于SMAP卫星雷达资料的海冰密集度反演技术研究 总被引：1，自引：0，他引：1
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。 相似文献
8.
北极多年冰在近几十年有明显的减少趋势,与北极海冰的厚度、体积和夏季最小海冰范围的减少密切相关。合成孔径雷达(synthetic aperture radar, SAR)具有全天时、全天候成像能力,基于SAR卫星影像的海冰分类对监测北极多年冰具有重要意义。基于深度学习U-Net模型,以SAR图像的双极化信息为模型输入,构建了像素级的海水、一年冰和多年冰多分类模型。与已有SAR图像海冰分类方法(支持向量机、随机森林和卷积神经网络)进行对比,基于双极化SAR图像的U-Net海冰分类模型的准确率、平均重叠度和Kappa系数,分别达到了90.73%、0.831和0.849,优于其他对比模型,分别提升了4.08%～19.04%, 0.063～0.321和0.111～0.335。此外,针对SAR图像水平-垂直极化(horizontal-vertical polarization, HV)有明显的条状热噪声和水平-水平极化(horizontal-horizontal polarization, HH)受入射角效应而亮度不均匀的特点,设计敏感性实验,研究HV噪声、入射角和灰度共生矩阵(gray leve... 相似文献
9.
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。 相似文献
10.
本文采用2003～2016年SSMI海冰密集度和NCEP气温、风场等数据,通过计算海冰覆盖率、增长期长度、冬季负积温和风拖曳力等参数,分析了巴伦支海海冰的变化特征及其与热力、动力影响因素之间的联系。结果显示,因西南部存在常年无冰区,巴伦支海14a平均的海冰覆盖率低于50%;覆盖率总体呈现下降趋势,冰情呈现"重—中等—轻"的变化过程,2012年后甚至出现夏季无冰的情况;增长期长度先增后减,起止时刻均有推迟;冬季负积温是影响巴伦支海冰情轻重的重要因素,与年平均海冰覆盖率距平和最大覆盖率的相关系数分别为-0.90和-0.89;风拖曳力的改变可在短期内引起海冰覆盖率急剧变化,是海冰边缘区产生流冰的主要原因,易对油气资源开发的海洋平台产生危害。 相似文献
11.
基于19GHz修正91GHz频段改进的ASI海冰密集度算法 总被引：1，自引：1，他引：0
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。 相似文献
12.
中国第四次北极科学考察期间北极点附近海冰和融池的航空遥感观测 总被引：1，自引：1，他引：0
An aerial photography has been used to provide validation data on sea ice near the North Pole where most polar orbiting satellites cannot cover. This kind of data can also be used as a supplement for missing data and for reducing the uncertainty of data interpolation. The aerial photos are analyzed near the North Pole collected during the Chinese national arctic research expedition in the summer of 2010(CHINARE2010). The result shows that the average fraction of open water increases from the ice camp at approximately 87°N to the North Pole, resulting in the decrease in the sea ice. The average sea ice concentration is only 62.0% for the two flights(16 and 19 August 2010). The average albedo(0.42) estimated from the area ratios among snow-covered ice,melt pond and water is slightly lower than the 0.49 of HOTRAX 2005. The data on 19 August 2010 shows that the albedo decreases from the ice camp at approximately 87°N to the North Pole, primarily due to the decrease in the fraction of snow-covered ice and the increase in fractions of melt-pond and open-water. The ice concentration from the aerial photos and AMSR-E(The Advanced Microwave Scanning Radiometer-Earth Observing System) images at 87.0°–87.5°N exhibits similar spatial patterns, although the AMSR-E concentration is approximately 18.0%(on average) higher than aerial photos. This can be attributed to the 6.25 km resolution of AMSR-E, which cannot separate melt ponds/submerged ice from ice and cannot detect the small leads between floes. Thus, the aerial photos would play an important role in providing high-resolution independent estimates of the ice concentration and the fraction of melt pond cover to validate and/or supplement space-borne remote sensing products near the North Pole. 相似文献
13.
Akira Shibata 《Journal of Oceanography》2006,62(3):351-359
A wind speed retrieval algorithm was developed using 6 and 10 GHz h-pol (6H and 10H) data of the Advanced Microwave Scanning
Radiometer (AMSR) aboard the Advanced Earth Observation Satellite-II (ADEOS-II) and AMSR-E aboard AQUA, for the purpose of
retrieving wind speed inside rainstorms, primarily hurricanes and typhoons. The h-pol was used rather than the v-pol, because
the brightness temperature sensitivity to the ocean wind at h-pol is larger than v-pol. The microwave emission change of 6H
and 10H corresponding to ocean wind was evaluated in no-rain areas by combining AMSR and SeaWinds data aboard the ADEOS-II
(SeaWinds was NASA’s scatterometer), and it was found that the ratio of the two 6H to 10H increments due to ocean wind is
0.9. Assuming that this result also holds with higher wind speeds and under rainy conditions, the brightness temperatures
at 6H and 10H were simulated using a microwave radiative transfer model. A parameter W6 (unit; Kelvin) was then defined, representing
an increment at 6H due to ocean wind. W6 is applicable to rainy areas, and to all ranges of sea surface temperature. W6 was
compared with wind speed reported by the National Hurricanes Center for several hurricanes in the Western Atlantic Ocean during
three years (2002 to 2004). W6 averaged around centers of hurricanes was found to exhibit a sensitivity to wind speed, such
as increasing from 22 K to 65 K as the wind speed rose from 65 to 140 knots (33 to 72 m/s), and an empirical relationship
relating the averaged W6 to wind speed in hurricanes was derived. 相似文献
14.
夏季北极密集冰区范围确定及其时空变化研究 总被引：3，自引：3，他引：0
研究夏季北极密集冰区的范围变化是了解北极海冰融化过程的重要手段。密集冰区与海冰边缘区之间没有明确的分界线, 海冰密集度在两者之间平滑过渡, 确定密集冰区范围就需确定一个密集度阈值。文中依据分辨率为6.25 km的AMSR-E遥感数据, 发现不同密集度阈值所围范围在密集冰区边缘处的减小存在由快变慢的过程, 同时与周围格点的密集度差异变化在该处最为显著, 对这两个特征进行统计分析, 获得的阈值同为89%, 具有明确的物理意义和合理性。以此为基础, 运用腐蚀算法剔除海冰边缘区, 同时结合连通域法排除小范围密集冰的影响, 进而确定密集冰区的范围。结果表明, 2002-2006年密集冰区覆盖范围较大, 年际变化较小, 2007年以后明显减小, 2010年与2011年相继出现最小值, 其中2011年的范围最小值仅为2006年的64%。密集冰区范围的变化不同于海冰覆盖范围, 是具有独立特性的海冰变化参数, 反映出高密集度海冰区域的变化特征。海冰的融化与海冰边缘区的变化是导致密集冰区范围发生变化的两个主要因素, 受动力学因素的影响, 海冰边缘区发生伸展或收缩, 发生密集冰区与海冰边缘区互相转化。本文提出了一种研究北极海冰变化的新思路, 密集冰区覆盖范围的减小表明北极中央区域高密集度海冰正持续减少。 相似文献
15.
基于2017年4月、2018年4月和2019年4月的CryoSat-2 L1B数据,比较分析了UCL13、DTU10、DTU13、DTU15和DTU18 5种不同平均海表面高度(MSS)模型及其反演的北极海冰干舷的多时空尺度差异。以UCL13为基准,对比分析不同MSS模型的差异和所反演的海冰干舷的差异,实验结果表明,不同MSS模型之间的平均绝对偏差范围为0.19～0.26 m,标准差范围为0.55～0.57 m,其中DTU18与UCL13的差异最小。以UCL13为基准,其他4种MSS模型反演的海冰干舷的平均绝对偏差为0.50～0.79 cm,标准差范围为1.17～1.74 cm。通过与冰桥计划(Operation IceBridge,OIB)机载数据相比,5种MSS模型反演的海冰干舷的相关系数范围为0.70～0.71,均方根误差范围为7.7～7.8 cm。故不同MSS模型之间的偏差对整个北极地区的海冰干舷反演的影响较小,偏差以相同的方式影响冰间水道和浮冰高度测量,因此相互抵消,但在冰间水道分布稀疏的区域,如加拿大群岛北部和拉普捷夫海区域,不同MSS模型反演的海冰干舷差异较大。 相似文献
16.
一种改进海面风速反演的分类神经网络方法 总被引：1，自引：0，他引：1
为了提高使用SSM/I资料反演全球海面风速的精度,发展了一个新型的神经网络方法.在这个方法中,使用高风速、中、低风速状态和天气状态分类的方法分别训练神经网络,然后根据其类别的不同使用不同的神经网络计算风速.此方法较好地去除了由于高风速和云天天气状态下训练样本数据的缺少所产生的误差,改进了在高风速状态下反演风速值比实际风速偏低的情况,使得反演的高风速值被校正到了正常位置.本方法反演海面风速的值与浮标实测风速值之间的均方根误差达到1.60m/s. 相似文献
17.
Several remotely sensed sea surface salinity(SSS) retrievals with various resolutions from the soil moisture and ocean salinity(SMOS) and Aquarius/SAC-D missions are applied as inputs for retrieving salinity profiles(S) using multilinear regressions. The performance is evaluated using a total root mean square(RMS) error, different error sources, and the feature resolutions of the retrieved S fields. In the mixed layer of the salinity, the SSS-S regression coefficients are uniformly large. The SSS inputs yield smaller RMS errors in the retrieved S with respect to Argo profiles as their spatial or temporal resolution decreases. The projected SSS errors are dominant, and the retrieved S values are more accurate than those of climatology in the tropics except for the tropical Atlantic, where the regression errors are abnormally large. Below that level, because of the influence of a sea level anomaly, the areas of high-accuracy S values shift to higher latitudes except in the high-latitude southern oceans, where the projected SSS errors are abnormally large. A spectral analysis suggests that the CATDS-0.25° results are much noisier and that the BEC-L4-0.25° results are much smoother than those of the other retrievals. Aquarius-CAP-1° generates the smallest RMS errors, and Aquarius-V2-1° performs well in depicting large-scale phenomena. BEC-L3-0.25°,which has small RMS errors and remarkable mesoscale energy, is the best fit for portraying mesoscale features in the SSS and retrieved S fields. The current priority for retrieving S is to improve the reliability of satellite SSS especially at middle and high latitudes, by developing advanced algorithms, combining both sensors, or weighing between accuracy and resolutions. 相似文献
18.
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
19.
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
20.
The sea surface wind speed (SSWS) derived by a microwave radiometer can be contaminated by changes of the brightness temperature
owing to the angle between the sensor azimuth and the wind direction (Relative Wind Direction effect: RWD effect). We attempt
to apply the method proposed by Konda and Shibata (2004) to the SSWS derived by Advanced Microwave Scanning Radiometer (AMSR)
on Advanced Earth Observing Satellite II (ADEOS-II), in order to correct for the RWD effect. The improvement of accuracy of
the SSWS estimation amounts to roughly 60% of the error caused by the RWD effect. Comparison with in situ observation at the
Tropical Atmosphere Ocean (TAO) array shows that the root mean square error of the corrected SSWS is 1.1 ms−1. It is found that the impact of the RWD effect on the estimation of the latent heat flux can amount to about 30 Wm−2 on average. We applied the method to the SSWS derived by AMSR for Earth Observing System (AMSR-E) and obtained a similar
result. 相似文献