Advanced Search
Article Contents

Applications of AMSR-E Measurements for Tropical Cyclone Predictions Part I: Retrieval of Sea Surface Temperature and Wind Speed


doi: 10.1007/s00376-008-0227-x

  • Existing satellite microwave algorithms for retrieving Sea Surface Temperature (SST) and Wind (SSW) are applicable primarily for non-raining cloudy conditions. With the launch of the Earth Observing System (EOS) Aqua satellite in 2002, the Advanced Microwave Scanning Radiometer (AMSR-E) onboard provides some unique measurements at lower frequencies which are sensitive to ocean surface parameters under adverse weather conditions. In this study, a new algorithm is developed to derive SST and SSW for hurricane predictions such as hurricane vortex analysis from the AMSR-E measurements at 6.925 and 10.65 GHz. In the algorithm, the effects of precipitation emission and scattering on the measurements are properly taken into account. The algorithm performances are evaluated with buoy measurements and aircraft dropsonde data. It is found that the root mean square (RMS) errors for SST and SSW are about 1.8 K and 1.9 m s-1, respectively, when the results are compared with the buoy data over open oceans under precipitating clouds (e.g., its liquid water path is larger than 0.5 mm), while they are 1.1 K for SST and 2.0 m s-1 for SSW, respectively, when the retrievals are validated against the dropsonde measurements over warm oceans. These results indicate that our newly developed algorithm can provide some critical surface information for tropical cycle predictions. Currently, this newly developed algorithm has been implemented into the hybrid variational scheme for the hurricane vortex analysis to provide predictions of SST and SSW fields.
  • [1] Jincheng WANG, Xingwei JIANG, Xueshun SHEN, Youguang ZHANG, Xiaomin WAN, Wei HAN, Dan WANG, 2023: Assimilation of Ocean Surface Wind Data by the HY-2B Satellite in GRAPES: Impacts on Analyses and Forecasts, ADVANCES IN ATMOSPHERIC SCIENCES, 40, 44-61.  doi: 10.1007/s00376-022-1349-2
    [2] Yitong LIN, Yihe FANG, Chunyu ZHAO, Zhiqiang GONG, Siqi YANG, Yiqiu YU, 2023: The Coordinated Influence of Indian Ocean Sea Surface Temperature and Arctic Sea Ice on Anomalous Northeast China Cold Vortex Activities with Different Paths during Late Summer, ADVANCES IN ATMOSPHERIC SCIENCES, 40, 62-77.  doi: 10.1007/s00376-022-1415-9
    [3] YAN Li, WANG Panxing, YU Yongqiang, LI Lijuan, WANG Bin, 2010: Potential Predictability of Sea Surface Temperature in a Coupled Ocean--Atmosphere GCM, ADVANCES IN ATMOSPHERIC SCIENCES, 27, 921-936.  doi: 10.1007/s00376-009-9062-y
    [4] Yueliang CHEN, Changxiang YAN, Jiang ZHU, 2018: Assimilation of Sea Surface Temperature in a Global Hybrid Coordinate Ocean Model, ADVANCES IN ATMOSPHERIC SCIENCES, 35, 1291-1304.  doi: 10.1007/s00376-018-7284-6
    [5] Ji Zhengang, Chao Jiping, 1987: TELECONNECTIONS OF THE SEA SURFACE TEMPERATURE IN THE INDIAN OCEAN WTTH SEA SURFACE TEMPERATURE IN THE EASTERN EQUATORIAL PACIFIC, AND WITH THE 500 hPa GEOPOTENTIAL HEIGHT FIELD IN THE NORTHERN HEMISPHERE, ADVANCES IN ATMOSPHERIC SCIENCES, 4, 343-348.  doi: 10.1007/BF02663604
    [6] Jiang Hao, Wang Keli, 2001: Analysis of the Surface Temperature on the Tibetan Plateau from Satellite, ADVANCES IN ATMOSPHERIC SCIENCES, 18, 1215-1223.  doi: 10.1007/s00376-001-0035-z
    [7] Xiang LI, Tiejun LING, Yunfei ZHANG, Qian ZHOU, 2018: A 31-year Global Diurnal Sea Surface Temperature Dataset Created by an Ocean Mixed-Layer Model, ADVANCES IN ATMOSPHERIC SCIENCES, 35, 1443-1454.  doi: 10.1007/s00376-018-8016-7
    [8] Xiaoyong YU, Chengyan LIU, Xiaocun WANG, Jian CAO, Jihai DONG, Yu LIU, 2022: Evaluation of Arctic Sea Ice Drift and its Relationship with Near-surface Wind and Ocean Current in Nine CMIP6 Models from China, ADVANCES IN ATMOSPHERIC SCIENCES, 39, 903-926.  doi: 10.1007/s00376-021-1153-4
    [9] Jeong-Hyeong LEE, Byungsoo KIM, Keon-Tae SOHN, Won-Tae KOWN, Seung-Ki MIN, 2005: Climate Change Signal Analysis for Northeast Asian Surface Temperature, ADVANCES IN ATMOSPHERIC SCIENCES, 22, 159-171.  doi: 10.1007/BF02918506
    [10] Tao WANG, Qiang FU, Wenshou TIAN, Hongwen LIU, Yifeng PENG, Fei XIE, Hongying TIAN, Jiali LUO, 2022: The Influence of Meridional Variation in North Pacific Sea Surface Temperature Anomalies on the Arctic Stratospheric Polar Vortex, ADVANCES IN ATMOSPHERIC SCIENCES.  doi: 10.1007/s00376-022-2033-2
    [11] P.C. Chu, Roland W. Garwood, Jr., 1990: Thermodynamic Feedback between Clouds and the Ocean Surface Mixed Layer, ADVANCES IN ATMOSPHERIC SCIENCES, 7, 1-10.  doi: 10.1007/BF02919163
    [12] Chunlei LIU, Yazhu YANG, Xiaoqing LIAO, Ning CAO, Jimmy LIU, Niansen OU, Richard P. ALLAN, Liang JIN, Ni CHEN, Rong ZHENG, 2022: Discrepancies in Simulated Ocean Net Surface Heat Fluxes over the North Atlantic, ADVANCES IN ATMOSPHERIC SCIENCES, 39, 1941-1955.  doi: 10.1007/s00376-022-1360-7
    [13] ZHONG Linhao, FENG Shide, GAO Shouting, 2005: Wind-Driven Ocean Circulation in Shallow Water Lattice Boltzmann Model, ADVANCES IN ATMOSPHERIC SCIENCES, 22, 349-358.  doi: 10.1007/BF02918749
    [14] YANG Yang, REN Rongcai, Ming CAI, RAO Jian, 2015: Attributing Analysis on the Model Bias in Surface Temperature in the Climate System Model FGOALS-s2 through a Process-Based Decomposition Method, ADVANCES IN ATMOSPHERIC SCIENCES, 32, 457-469.  doi: 10.1007/s00376-014-4061-z
    [15] LI Tao, ZHENG Xiaogu, DAI Yongjiu, YANG Chi, CHEN Zhuoqi, ZHANG Shupeng, WU Guocan, WANG Zhonglei, HUANG Chengcheng, SHEN Yan, LIAO Rongwei, 2014: Mapping Near-surface Air Temperature, Pressure, Relative Humidity and Wind Speed over Mainland China with High Spatiotemporal Resolution, ADVANCES IN ATMOSPHERIC SCIENCES, 31, 1127-1135.  doi: 10.1007/s00376-014-3190-8
    [16] Peter C. Chu, Chen Yuchun, Lu Shihua, 2001: Evaluation of Haney-Type Surface Thermal Boundary Conditions Using a Coupled Atmosphere and Ocean Model, ADVANCES IN ATMOSPHERIC SCIENCES, 18, 355-375.  doi: 10.1007/BF02919315
    [17] Ting ZHANG, Jinbao SONG, 2018: Effects of Sea-Surface Waves and Ocean Spray on Air-Sea Momentum Fluxes, ADVANCES IN ATMOSPHERIC SCIENCES, 35, 469-478.  doi: 10.1007/s00376-017-7101-7
    [18] Ge SONG, Rongcai REN, 2023: The Subsurface and Surface Indian Ocean Dipoles and Their Association with ENSO in CMIP6 models, ADVANCES IN ATMOSPHERIC SCIENCES.  doi: 10.1007/s00376-022-2086-2
    [19] Shuanglin Li, 2010: A Comparison of Polar Vortex Response to Pacific and Indian Ocean Warming, ADVANCES IN ATMOSPHERIC SCIENCES, 27, 469-482.  doi: 10.1007/s00376-009-9116-1
    [20] Liu Shikuo, Peng Weihong, Huang Feng, Chi Dongyan, 2002: Effects of Turbulent Dispersion on the Wind Speed Profile in the Surface Layer, ADVANCES IN ATMOSPHERIC SCIENCES, 19, 794-806.  doi: 10.1007/s00376-002-0045-5

Get Citation+

Export:  

Share Article

Manuscript History

Manuscript received: 10 March 2008
Manuscript revised: 10 March 2008
通讯作者: 陈斌, bchen63@163.com
  • 1. 

    沈阳化工大学材料科学与工程学院 沈阳 110142

  1. 本站搜索
  2. 百度学术搜索
  3. 万方数据库搜索
  4. CNKI搜索

Applications of AMSR-E Measurements for Tropical Cyclone Predictions Part I: Retrieval of Sea Surface Temperature and Wind Speed

  • 1. NOAA Joint Center for Satellite Data Assimilation, Camp Springs, MD, USA;NOAA/NESDIS/Center for Satellite Applications and Research, Camp Springs, MD, USA

Abstract: Existing satellite microwave algorithms for retrieving Sea Surface Temperature (SST) and Wind (SSW) are applicable primarily for non-raining cloudy conditions. With the launch of the Earth Observing System (EOS) Aqua satellite in 2002, the Advanced Microwave Scanning Radiometer (AMSR-E) onboard provides some unique measurements at lower frequencies which are sensitive to ocean surface parameters under adverse weather conditions. In this study, a new algorithm is developed to derive SST and SSW for hurricane predictions such as hurricane vortex analysis from the AMSR-E measurements at 6.925 and 10.65 GHz. In the algorithm, the effects of precipitation emission and scattering on the measurements are properly taken into account. The algorithm performances are evaluated with buoy measurements and aircraft dropsonde data. It is found that the root mean square (RMS) errors for SST and SSW are about 1.8 K and 1.9 m s-1, respectively, when the results are compared with the buoy data over open oceans under precipitating clouds (e.g., its liquid water path is larger than 0.5 mm), while they are 1.1 K for SST and 2.0 m s-1 for SSW, respectively, when the retrievals are validated against the dropsonde measurements over warm oceans. These results indicate that our newly developed algorithm can provide some critical surface information for tropical cycle predictions. Currently, this newly developed algorithm has been implemented into the hybrid variational scheme for the hurricane vortex analysis to provide predictions of SST and SSW fields.

Catalog

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return