A study on the dynamic tie points ASI algorithm in the Arctic Ocean

HAO Guanghua; SU Jie

doi:10.1007/s13131-015-0659-y

北极海冰密集度动态系点值ASI反演算法研究

doi: 10.1007/s13131-015-0659-y

郝光华,
苏洁

中国海洋大学物理海洋教育部重点实验室, 山东青岛 266100

基金项目: The Global Change Research Program of China under contract No. 2015CB953901; the National Natural Science Foundation of China under contract Nos 41330960 and 41276193.

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- 收稿日期: 2015-02-16
- 修回日期: 2015-04-16

A study on the dynamic tie points ASI algorithm in the Arctic Ocean

Key Laboratory of Physical Oceanography of Ministry of Education, Ocean University of China, Qingdao 266100, China

摘要

摘要: 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.
- 动态系点值ASI算法 /
- 海冰密集度 /
- AMSR-E /
- MODIS
Abstract: Sea ice concentration is an important parameter for polar sea ice monitoring. Based on 89 GHz AMSR-E (Advanced Microwave Scanning Radiometer for Earth Observing System) data, a gridded high-resolution passive microwave sea ice concentration product can be obtained using the ASI (the Arctic Radiation And Turbulence Interaction Study (ARTIST) Sea Ice) retrieval algorithm. Instead of using fixed-point values, we developed ASI algorithm based on daily changed tie points, called as the dynamic tie point ASI algorithm in this study. Here the tie points are expressed as the brightness temperature polarization difference of open water and 100% sea ice. In 2010, the yearly-averaged tie points of open water and sea ice in Arctic are estimated to be 50.8 K and 7.8 K, respectively. It is confirmed that the sea ice concentrations retrieved by the dynamic tie point ASI algorithm can increase (decrease) the sea ice concentrations in low-value (high-value) areas. This improved the sea ice concentrations by present retrieval algorithm from microwave data to some extent. Comparing with the products using fixed tie points, the sea ice concentrations retrieved from AMSR-E data by using the dynamic tie point ASI algorithm are closer to those obtained from MODIS (Moderate-resolution Imaging Spectroradiometer) data. In 40 selected cloud-free sample regions, 95% of our results have smaller mean differences and 75% of our results have lower root mean square (RMS) differences compare with those by the fixed tie points.
- dynamic tie points ASI algorithm /
- sea ice concentration /
- AMSR-E /
- MODIS

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参考文献(34)

Bi Haibo, Huang Haijun, Su Qiao, et al. 2014. An Arctic sea ice thickness variability revealed from satellite altimetric measurements. Acta Oceanologica Sinica, 33(11): 134-140

Cavalieri D J, Gloersen P, Campbell W J. 1984. Determination of seaice parameters with the Nimbus 7 SMMR. Journal of Geophysical Research, 89(D4): 5355-5369

Cavalieri D J, St Germain K M. 1995. Arctic sea ice research with satel-lite passive microwave radiometers. IEEE Geoscience and Re-mote Sensing Society Newsletter, 97(1): 6-12

Cavalieri D J, Markus T, Hall D K, et al. 2006. Assessment of EOS-AQUAAMSR-EArctic sea ice concentrations using landsat-7 and airborne microwave imagery. IEEE Transactions on Geoscience and Remote Sensing, 44(11): 3057-3069

Cavalieri D J, Markus T, Hall D K, et al. 2010. Assessment of AMSR-E Antarctic winter sea-ice concentrations using Aqua MODIS. IEEE Transactions on Geoscience and Remote Sensing, 48(9): 3331-3339

Comiso J C. 1995. SSM/I sea ice concentrations using the bootstrap algorithm. National Aeronautics and Space Administration. NASA RefPubl, RP 1380, 49

Comiso J C, Kwok R. 1996. Surface and radiative characteristics of the summer Arctic sea ice cover from multisensor satellite observa-tions. Journal of Geophysical Research, 101(C12): 28397-28416

Comiso J C, Cavalieri D J, Markus T. 2003. Sea ice concentration, ice temperature, and snow depth using AMSR-E data. IEEE Trans-actions on Geoscience and Remote Sensing, 41(2): 243-252

de Vernal A, Gersonde R, Goosse H, et al. 2013. Sea ice in the paleo-climate system: the challenge of reconstructing sea ice from proxies—an introduction. Quaternary Science Reviews, 79: 1-8

Eastwood S, Larsen K R, Lavergne T, et al. 2011. Global Sea Ice Con-centration Reprocessing Product User Manual, Version 1.3, EU-METSAT

Emery W J, Fowler C, Maslanik J. 1994. Arctic sea ice concentrations from special sensor microwave imager and advanced very high resolution radiometer satellite data. Journal of Geophysical Re-search, 99(C9): 18329-18342

Gloersen P, Cavalieri D J. 1986. Reduction of weather effects in the calculation of sea ice concentration from microwave radiances. Journal of Geophysical Research, 91(C3): 3913-3919

Han H, Lee H. 2007. Compartive study of sea ice concentration by us-ing DMSP SSM/I, Aqua AMSR-E and Kompsat-1 EOC. In: IEEE InternationalGeoscience and Remote Sensing Symposium, 2007. IGARSS 2007. Barcelona, Spain: IEEE, 4249-4252

Kaleschke L, Lüpkes C, Vihma T, et al. 2001. SSM/I sea ice remote sensing for mesoscale ocean-atmosphere interaction analysis: Ice and icebergs. Canadian Journal of Remote Sensing, 27(5): 526-537

Kern S. 2001. A new algorithm to retrieve the sea ice concentration using weather-corrected 85GHz SSM/I measurements. Logos-Verlag, Institute of Environmental Physics, Bremen, Germany

Kern S, Heygster G. 2001. Sea-ice concentration retrieval in the Ant-arctic based on the SSM/I 85. 5 GHz polarization. Annals of Glaciology, 33(1): 109-114

KernS, Kaleschke L, Clausi D A. 2003. A comparison of two 85-GHz SSM/I ice concentration algorithms with AVHRR and ERS-2 SAR imagery. IEEE Transactions on Geoscience and Remote Sensing, 41(10): 2294-2306

Kern S. 2004. A new method for medium-resolution sea ice analysis using weather-influence corrected Special Sensor Microwave/Imager 85 GHz data. International Journal of Re-mote Sensing, 25(21): 4555-4582

Liang Shunlin, Strahler A, Walthall C. 1998. Retrieval of land surface Albedo from satellite observations: A simulation study. In:IEEE International Geoscience and Remote Sensing Symposium Proceedings. Seattle, WA: IEEE, 1286-1288

Liu Jiping, Curry J A, Martinson D G. 2004. Interpretation of recent Antarctic sea ice variability. Geophysical Research Letters, 31(2)

Markus T, Cavalieri D J. 2000. An enhancement of the NASA Team sea ice algorithm. IEEE Transactions on Geoscience and Re-mote Sensing, 38(3): 1387-1398

Meier W N. 2005. Comparison of passive microwave ice concentra-tion algorithm retrievals with AVHRR imagery in Arctic peri-pheral seas. IEEE Transactions on Geoscience and Remote Sensing, 43(6): 1324-1337

Rind D, Healy R, ParkinsonC, et al. 1995. The role of seaice in 2×CO₂ climate model sensitivity. Part I: The total influence of sea ice thickness and extent. Journal of Climate, 8(3): 449-463

Serreze M C, Maslanik J A, Scambos T A, et al. 2003. A record minim-um arctic sea ice extent and area in 2002. Geophysical Re-search Letters, 30(3): 1110

Spreen G. 2004. Meereisfernerkundung Mit Dem Satellitengestützten Mikrowellenradiometer AMSR (-E)—Bestimmung der Eiskonzentration und Eiskante unter Verwendung-der-89-GHz-Kan.le, Diplomarbeit [dissertation] (in German). Ham-burg, Germany: Universityof Hamburg

Spreen G, Kaleschke L, Heygster G. 2008. Sea ice remote sensing us-ing AMSR-E 89-GHz channels. Journal of Geophysical Re-search, 113(C2)

Steffen K, Schweiger A. 1991. NASA team algorithm for sea ice con-centration retrieval from Defense Meteorological Satellite Pro-gram special sensor microwave imager: Comparison with Landsat satellite imagery. Journal of Geophysical Research, 96(C12): 21971-21987

Svendsen E, Matzler C, Grenfell T C. 1987. A model for retrieving total sea ice concentration from a spaceborne dual-polarized pass-ive microwave instrument operating near 90 GHz. Internation-al Journal of Remote Sensing, 8(10): 1479-1487

Su Jie, Hao Guanghua, Ye Xinxin, et al. 2013. The experiment and val-idation of sea ice concentration AMSR-E retrieval algorithm in polar region. Journal of Remote Sensing (in Chinese), 173(3): 495-513

Vavrus S, Harrison S P.2003. The impact of sea-ice dynamics on the Arctic climate system. Climate Dynamics, 20(7-8): 741-757

Wiebe H, Heygster G, Markus T.2009. Comparison of the ASI ice con-centration algorithm with Landsat-7 ETM+ and SAR imagery. IEEE Transactions on Geoscience and Remote Sensing, 47(9): 3008-3015

Ye Xinxin, Su Jie, Wang Yang, et al. 2011.Assessment of AMSR-E sea ice concentration in ice margin zone using MODIS data. In: 2011 International Conference onRemote Sensing, Environ-ment and Transportation Engineering (RSETE). Nanjing, China: IEEE, 3869-3873

Zhang S G. 2012. Sea ice concentration algorithm and study on the physical process about sea ice and melt-pond change in cent-ral Arctic [dissertation]. Qingdao, China: Ocean University of China

Zhang Shugang, Zhao Jinping, Frey K, et al. 2013. Dual-polarized ra-tio algorithm for retrieving Arctic sea ice concentration from passive microwave brightness temperature. Journal of Oceano-graphy, 69(2): 215-227

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北极海冰密集度动态系点值ASI反演算法研究

doi: 10.1007/s13131-015-0659-y

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A study on the dynamic tie points ASI algorithm in the Arctic Ocean

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