| MA Wentao,YANG Xiaofeng,YU Yang,LIU Guihong,LI Ziwei,JING Cheng. 2015. Impact of rain-induced sea surface roughness variations on salinity retrieval from the Aquarius/SAC-D satellite. Acta Oceanologica Sinica, 34(7):89－96 |
| Impact of rain-induced sea surface roughness variations on salinity retrieval from the Aquarius/SAC-D satellite |
| 降雨导致的海面粗糙度对Aquarius卫星盐度反演的影响研究 |
| Received:October 08, 2014 Revised:February 02, 2015 |
| DOI：10.1007/s13131-015-0660-5 |
| Key words:Aquarius salinity remote sensing rain L-band emissivity |
| 中文关键词: Aquarius 盐度遥感 降雨 L波段 发射率 |
| 基金项目: |
| Author Name | Affiliation | E-mail | | MA Wentao | College of Physical and Environmental Oceanography, Ocean University of China, Qingdao 266100, China
State Key Laboratory of Remote Sensing Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100101, China | | | YANG Xiaofeng | State Key Laboratory of Remote Sensing Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100101, China | yangxf@radi.ac.cn | | YU Yang | State Key Laboratory of Remote Sensing Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100101, China | | | LIU Guihong | State Key Laboratory of Remote Sensing Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100101, China | | | LI Ziwei | State Key Laboratory of Remote Sensing Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100101, China | | | JING Cheng | State Key Laboratory of Remote Sensing Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100101, China | |
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| Abstract: |
| Rainfall has two significant effects on the sea surface, including salinity decreasing and surface becoming rougher, which have further influence on L-band sea surface emissivity. Investigations using the Aquarius and TRMM 3B42 matchup dataset indicate that the retrieved sea surface salinity (SSS) is underestimated by the present Aquarius algorithm compared to numerical model outputs, especially in cases of a high rain rate. For example, the bias between satellite-observed SSS and numerical model SSS is approximately 2 when the rain rate is 25 mm/h. The bias can be eliminated by accounting for rain-induced roughness, which is usually modeled by rain-generated ring-wave spectrum. The rain spectrum will be input into the Small Slope Approximation (SSA) model for the simulation of sea surface emissivity influenced by rain. The comparison with theoretical model indicated that the empirical model of rain spectrumis more suitable to be used in the simulation. Further, the coefficients of the rain spectrum are modified by fitting the simulations with the observations of the 2-year Aquarius and TRMM matchup dataset. The calculations confirm that the sea surface emissivity increases with the wind speed and rain rate. The increase induced by the rain rate is rapid in the case of low rain rate and low wind speed. Finally, a modified model of sea surface emissivity including the rain spectrum is proposed and validated by using the matchup dataset in May 2014. Compared with observations, the bias of the rain-induced sea surface emissivity simulated by the modified modelis approximately 1e-4, and the RMSE is slightly larger than 1e-3. With using more matchup data, thebias between model retrieved sea surface salinities and observationsmay be further corrected, and the RMSE may be reduced to less than 1 in the cases of low rain rate and low wind speed. |
| 中文摘要: |
| 降雨引起海表面L波段发射率变化的原因主要有2种, 即表层海水淡化和海表粗糙度改变。通过对Aquarius与TRMM 3B42的匹配数据集研究发现, 与模式得到的盐度相比, Aquarius卫星反演得到的盐度在降雨发生, 尤其是高降雨率时明显降低。在降雨率为25 mm/hr时, 卫星反演的盐度比模式盐度平均低约2 psu。当不考虑海水淡化的影响时, 考虑海表面粗糙度的影响时可以消除反演得到的盐度与模式盐度的偏差。将降雨波谱引入小斜率近似模型, 可以模拟海表面粗糙度的改变, 进而得到降雨引起的海表面发射率改变。本文比较了经验的和理论的降雨波谱模型, 发现采用经验模型可以更好地模拟实测海面发射率增量, 然而经验模型的系数还需进一步改进。本文利用2年实测匹配数据对经验模型的系数进行拟合, 获得了降雨条件下海面发射率的改进模型。模型和实测数据表明, 海表发射率随风速和降雨率升高, 海表面发射率随降雨率的增长速度在低风速和低降雨率时较快, 而在高风速和大降雨率时较慢。改进模型得到的发射率增量与实测发射率增量的偏差在1e-4左右, 均方根误差略大于1e-3。最后, 本文利用2014年5月的匹配数据对模型进行了验证。结果表明, 采用改进模型反演得到的盐度与模式盐度的偏差得到了校正, 它们之间的均方根误差也减小了, 在低风速和低降雨率时均方根误差优于1 psu。 |
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