Impact of rain-induced sea surface roughness variations on salinity retrieval from the Aquarius/SAC-D satellite
-
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。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.
-
Key words:
- Aquarius /
- salinity remote sensing /
- rain /
- L-band /
- emissivity
-
Bliven L F, Sobieski P W, Craeye C. 1997. Rain generated ring-waves: measurements and modelling for remote sensing. IntJRemote Sens, 18(1): 221-228 Boutin J, Martin N, Reverdin G, et al. 2013. Sea surface freshening inferred from SMOS and ARGO salinity: impact of rain. Ocean Sci, 9(1): 183-192 Boutin J, Martin N, Reverdin G, et al. 2014. Sea surface salinity under rain cells: SMOS satellite and in situ drifters observations. Journal of Geophysical Research: Oceans, 119(8): 5533-5545 Boutin J, Martin N, Yin Xiaobin, et al. 2012. First assessment of SMOS data over open ocean: Part II-Sea surface salinity. IEEE Trans- Geosci Remote Sens, 50(5): 1662-1675 Chassignet E P, Hurlburt H E, Metzger E J, et al. 2009. US GODAE: global ocean prediction with the HYbrid coordinate ocean model (HYCOM). Oceanography, 22(2): 64-75 Contreras R F, Plant W J. 2006. Surface effect of rain on microwave backscatter from the ocean: Measurements and modeling. Journal of Geophysical Research: Oceans (1978-2012), 111(C8): C08019 Craeye C, Sobieski P W, Bliven L F. 1997. Scattering by artificial wind and rain roughened water surfaces at oblique incidences. IntJRemote Sens, 18(10): 2241-2246 Durden S L, Vesecky J F. 1985. A physical radar cross-section model for a wind-driven sea with swell. IEEE JOceanic Eng, 10(4): 445-451 Felton C S, Subrahmanyam B, Murty V S N, et al. 2014. Estimation of the barrier layer thickness in the Indian Ocean using Aquarius Salinity. Journal of Geophysical Research: Oceans, 119(7): 4200-4213 Font J, Camps A, Borges A, et al. 2010. SMOS: The challenging sea surface salinity measurement from space. Proc IEEE, 98(5): 649-665 Johnson J T, Zhang Min. 1999. Theoretical study of the small slope approximation for ocean polarimetric thermal emission. IEEE Trans Geosci Remote Sens, 37(5): 2305-2316 Kerr Y H, Waldteufel P, Wigneron J-P, et al. 2010. The SMOS mission: New tool for monitoring key elements ofthe global water cycle. Proc IEEE, 98(5): 666-687 Lagerloef G, Colomb F R, Le Vine D, et al. 2008. The Aquarius/SAC-D mission: Designed to meet the salinity remote-sensing challenge. Oceanography, 21(1): 68-81 Le Vine D M, Lagerloef G S E, Torrusio S E. 2010. Aquarius and remote sensing of sea surface salinity from space. Proc IEEE, 98(5): 688-703 Ma Wentao, Yang Xiaofeng, Liu Guihong, et al. 2014. An Improved Model for L-Band Brightness Temperature Estimation Over Foam-Covered Seas Under Low and Moderate Winds. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 7(9): 3784-3793 Meissner T, Wentz F J. 2004. The complex dielectric constant of pure and sea water from microwave satellite observations. IEEE Trans Geosci Remote Sens, 42(9): 1836-1849 Qu Tangdong, Song Y T, Maes C. 2014. Sea surface salinity and barrier layer variability in the equatorial Pacific as seen from Aquarius and Argo. Journal of Geophysical Research: Oceans, 119(1): 15-29 Reynolds R W, Smith T M, Liu Chunying, et al. 2007. Daily high-resolution- blended analyses for sea surface temperature. Journal of Climate, 20(22): 5473-5496 Sobieski P, Craeye C, Bliven L F. 2009. A relationship between rain radar reflectivity and height elevation variance of ringwaves due to the impact of rain on the sea surface. Radio Science, 44(3): CiteID RS3005 Tang Wenqing, Yueh S, Fore A, et al. 2013. The rain effect on Aquarius' L-band sea surface brightness temperature and radar backscatter. Remote Sens Environ, 137: 147-157 Tang Wenqing, Yueh S H, Fore A G, et al. 2014. Uncertainty of Aquarius sea surface salinity retrieved under rainy conditions and its implication on the water cycle study. Journal of Geophysical Research: Oceans, 119(8): 4821-4839 Terray L, Corre L, Cravatte S, et al. 2012. Near-surface salinity as nature's rain gauge to detect human influence on the tropical water cycle. Journal of Climate, 25(3): 958-977 Wentz F J. 2005. The effect of clouds and rain on Aquarius salinity retrieval. Remote Sensing System Technical Memorandum, 3031805 Wentz F J, Le Vine David M. 2013. Aquarius Salinity Retrieval Algorithm. Algorithm Theoretical Basis Document Yin Xiaobin, Boutin J, Martin N, et al. 2012a. Optimization of L-band sea surface emissivity models deduced from SMOS data. IEEE Trans Geosci Remote Sens, 50(5): 1414-1426 Yin Xiaobin, Boutin J, Spurgeon P. 2012b. First assessment of SMOS data over open ocean: Part I—Pacific Ocean. IEEE Trans Geosci Remote Sens, 50(5): 1648-1661 Yueh S H, Dinardo S J, Fore A G, et al. 2010. Passive and active L-band microwave observations and modeling of ocean surface winds. IEEE Trans Geosci Remote Sens, 48(8): 3087-3100 Yueh S H, Tang Wenqing, Fore A G, et al. 2013. L-band passive and active microwave geophysical model functions of ocean surface winds and applications to Aquarius retrieval. IEEE Trans Geosci Remote Sens, 51(9): 4619-4632
点击查看大图
计量
- 文章访问数: 1316
- HTML全文浏览量: 48
- PDF下载量: 1158
- 被引次数: 0