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...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 le-4, and the RMSE is slightly larger than le-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.展开更多
The in situ sea surface salinity（SSS） measurements from a scientific cruise to the western zone of the southeast Indian Ocean covering 30°-60°S, 80°-120°E are used to assess the SSS retrieved fro...The in situ sea surface salinity（SSS） measurements from a scientific cruise to the western zone of the southeast Indian Ocean covering 30°-60°S, 80°-120°E are used to assess the SSS retrieved from Aquarius（Aquarius SSS）.Wind speed and sea surface temperature（SST） affect the SSS estimates based on passive microwave radiation within the mid- to low-latitude southeast Indian Ocean. The relationships among the in situ, Aquarius SSS and wind-SST corrections are used to adjust the Aquarius SSS. The adjusted Aquarius SSS are compared with the SSS data from My Ocean model. Results show that：（1） Before adjustment： compared with My Ocean SSS, the Aquarius SSS in most of the sea areas is higher; but lower in the low-temperature sea areas located at the south of 55°S and west of 98°E. The Aquarius SSS is generally higher by 0.42 on average for the southeast Indian Ocean.（2） After adjustment： the adjustment greatly counteracts the impact of high wind speeds and improves the overall accuracy of the retrieved salinity（the mean absolute error of the Zonal mean is improved by 0.06, and the mean error is-0.05 compared with My Ocean SSS）. Near the latitude 42°S, the adjusted SSS is well consistent with the My Ocean and the difference is approximately 0.004.展开更多
【目的】通过验证Aquarius海表盐度遥感产品数据在不同大洋和波束的反演精度,为其应用提供依据。【方法】基于自沉浮式剖面探测浮标Argo（Array for real-time geostrophic oceanography）盐度观测数据评估Aquarius卫星在重点海域（太...【目的】通过验证Aquarius海表盐度遥感产品数据在不同大洋和波束的反演精度,为其应用提供依据。【方法】基于自沉浮式剖面探测浮标Argo（Array for real-time geostrophic oceanography）盐度观测数据评估Aquarius卫星在重点海域（太平洋、大西洋、印度洋）和不同波束对应的海表盐度产品精度。【结果】相对于波束2和波束3,波束1海表盐度与Argo观测最为接近,偏差和均方根差分别为0.003psu和0.397psu。与大西洋和印度洋相比,太平洋反演精度最高。在中纬度地区,盐度偏差较小,约为0.1psu;在南北纬20°和高纬度区域,盐度偏差较大,约为0.2psu;低海温和高风速对盐度误差也有重要贡献,低海温对应的弱亮温信号和高风速下的不准确的海面粗糙度模型是导致盐度偏差的主要因素。此外,利用Argo月平均海表盐度观测数据评估了Aquarius卫星海表盐度三级产品,均方根差在0.27∽0.34psu之间,平均值为0.31psu。在二级和三级产品中,V3.0SSS_bias_adj的均方根差相比V3.0SSS均降低约0.04psu。【结论】与V2.0数据产品相比,V3.0二级产品精度有了的较大提高,三级产品无明显改善,升轨和降轨的偏差依然存在。海表温度校正能够提高盐度反演的精度,使得均方根误差下降0.04psu。展开更多
Subtropical sea surface salinity(SSS)maximum is formed in the subtropical South Indian Ocean(SIO)by excessive evaporation over precipitation and serves as the primary salt source of the SIO.Spaceborne SSS measurements...Subtropical sea surface salinity(SSS)maximum is formed in the subtropical South Indian Ocean(SIO)by excessive evaporation over precipitation and serves as the primary salt source of the SIO.Spaceborne SSS measurements by Aquarius satellite during September 2011-May 2015 detect three disconnected SSS maximum regions(>35.6)in the eastern(105°E-115°E,38°S-28°S),central(60°E-100°E,35°S-25°S),and western(25°E-40°E,38°S-20°S)parts of the subtropical SIO,respectively.Such structure is however not seen in gridded Argo data.Analysis of Argo profile data confirms the existence of the eastern maximum patch and also reveals SSS overestimations of Aquarius near the western and eastern boundaries.Although subjected to large uncertainties,a mixed-layer budget analysis is employed to explain the seasonal cycle of SSS.The eastern and central regions reach the highest salinity in February-March and lowest salinity in August-September,which can be well explained by surface freshwater forcing(SFF)term.SFF is however not controlled by evaporation(E)or precipitation(P).Instead,the large seasonal undulations of mixed layer depth(MLD)is the key factor.The shallow(deep)MLD in austral summer(winter)amplifies(attenuates)the forcing effect of local positive E-P and causes SSS rising(decreasing).Ocean dynamics also play a role.Particularly,activity of mesoscale eddies is a critical factor regulating SSS variability in the eastern and western regions.展开更多
Roughness-induced emission from ocean surfaces is one of the main issues that affects the retrieval accuracy of sea surface salinity remote sensing.In previous studies,the correction of roughness effect mainly depende...Roughness-induced emission from ocean surfaces is one of the main issues that affects the retrieval accuracy of sea surface salinity remote sensing.In previous studies,the correction of roughness effect mainly depended on wind speeds retrieved from scatterometers or those provided by other means,which necessitates a high requirement for accuracy and synchronicity of wind-speed measurements.The aim of this study is to develop a novel roughness correction model of ocean emissivity for the salinity retrieval application.The combined active/passive observations of normalized radar cross-sections(NRCSs)and emissivities from ocean surfaces given by the L-band Aquarius/SAC-D mission,and the auxiliary wind directions collocated from the National Centers for Environmental Prediction(NCEP)dataset are used for model development.The model is validated against the observations and the Aquarius standard algorithms of roughness-induced emissivity correction.Comparisons between model computations and measurements indicate that the model has better accuracy in computing wind-induced brightness temperature in the upwind/downwind directions or for the surfaces with smaller NRCSs,which can be better than 0.3 K.However,for crosswind directions and larger NRCSs,the model accuracy is relatively low.A model using HH-polarized NRCSs yields better accuracy than that using VV-polarized ones.For a fair comparison to the Aquarius standard algorithms using wind speeds retrieved from multi-source data,the maximum likelihood estimation is employed to produce results combining our model calculations and those using other sources.Numerical simulations show that combined results basically have higher accuracy than the standard algorithms.展开更多
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 profi...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.展开更多
Scorpio 10.24-11.22 Sagittarius 11.23〜12.21 Capricorn 12.22-1.20 Aquarius 1.21-2.19 Scorpio season has arrived and,as always,just in time for Halloween.Crack out that ouija board and slide into the DMs of the ghosts i...Scorpio 10.24-11.22 Sagittarius 11.23〜12.21 Capricorn 12.22-1.20 Aquarius 1.21-2.19 Scorpio season has arrived and,as always,just in time for Halloween.Crack out that ouija board and slide into the DMs of the ghosts in your building.The occult awaits you.展开更多
基金The National Natural Science Foundation of China under contract No.41371355
文摘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 le-4, and the RMSE is slightly larger than le-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.
基金The National Natural Science Foundation of China under contract No.41371391the Innovative Youth Foundation of Ocean Telemetry Engineering and Technology Centre of State Oceanic Administration under contract No.201302the Program for the Specialized Research Fund for the Doctoral Program of Higher Education of China under contract No.20120091110017
文摘The in situ sea surface salinity（SSS） measurements from a scientific cruise to the western zone of the southeast Indian Ocean covering 30°-60°S, 80°-120°E are used to assess the SSS retrieved from Aquarius（Aquarius SSS）.Wind speed and sea surface temperature（SST） affect the SSS estimates based on passive microwave radiation within the mid- to low-latitude southeast Indian Ocean. The relationships among the in situ, Aquarius SSS and wind-SST corrections are used to adjust the Aquarius SSS. The adjusted Aquarius SSS are compared with the SSS data from My Ocean model. Results show that：（1） Before adjustment： compared with My Ocean SSS, the Aquarius SSS in most of the sea areas is higher; but lower in the low-temperature sea areas located at the south of 55°S and west of 98°E. The Aquarius SSS is generally higher by 0.42 on average for the southeast Indian Ocean.（2） After adjustment： the adjustment greatly counteracts the impact of high wind speeds and improves the overall accuracy of the retrieved salinity（the mean absolute error of the Zonal mean is improved by 0.06, and the mean error is-0.05 compared with My Ocean SSS）. Near the latitude 42°S, the adjusted SSS is well consistent with the My Ocean and the difference is approximately 0.004.
文摘【目的】通过验证Aquarius海表盐度遥感产品数据在不同大洋和波束的反演精度,为其应用提供依据。【方法】基于自沉浮式剖面探测浮标Argo（Array for real-time geostrophic oceanography）盐度观测数据评估Aquarius卫星在重点海域（太平洋、大西洋、印度洋）和不同波束对应的海表盐度产品精度。【结果】相对于波束2和波束3,波束1海表盐度与Argo观测最为接近,偏差和均方根差分别为0.003psu和0.397psu。与大西洋和印度洋相比,太平洋反演精度最高。在中纬度地区,盐度偏差较小,约为0.1psu;在南北纬20°和高纬度区域,盐度偏差较大,约为0.2psu;低海温和高风速对盐度误差也有重要贡献,低海温对应的弱亮温信号和高风速下的不准确的海面粗糙度模型是导致盐度偏差的主要因素。此外,利用Argo月平均海表盐度观测数据评估了Aquarius卫星海表盐度三级产品,均方根差在0.27∽0.34psu之间,平均值为0.31psu。在二级和三级产品中,V3.0SSS_bias_adj的均方根差相比V3.0SSS均降低约0.04psu。【结论】与V2.0数据产品相比,V3.0二级产品精度有了的较大提高,三级产品无明显改善,升轨和降轨的偏差依然存在。海表温度校正能够提高盐度反演的精度,使得均方根误差下降0.04psu。
基金Supported by the National Natural Science Foundation of China(Nos.41776001,41806001)the National Key R&D Program of China(No.2016YFC0301103)
文摘Subtropical sea surface salinity(SSS)maximum is formed in the subtropical South Indian Ocean(SIO)by excessive evaporation over precipitation and serves as the primary salt source of the SIO.Spaceborne SSS measurements by Aquarius satellite during September 2011-May 2015 detect three disconnected SSS maximum regions(>35.6)in the eastern(105°E-115°E,38°S-28°S),central(60°E-100°E,35°S-25°S),and western(25°E-40°E,38°S-20°S)parts of the subtropical SIO,respectively.Such structure is however not seen in gridded Argo data.Analysis of Argo profile data confirms the existence of the eastern maximum patch and also reveals SSS overestimations of Aquarius near the western and eastern boundaries.Although subjected to large uncertainties,a mixed-layer budget analysis is employed to explain the seasonal cycle of SSS.The eastern and central regions reach the highest salinity in February-March and lowest salinity in August-September,which can be well explained by surface freshwater forcing(SFF)term.SFF is however not controlled by evaporation(E)or precipitation(P).Instead,the large seasonal undulations of mixed layer depth(MLD)is the key factor.The shallow(deep)MLD in austral summer(winter)amplifies(attenuates)the forcing effect of local positive E-P and causes SSS rising(decreasing).Ocean dynamics also play a role.Particularly,activity of mesoscale eddies is a critical factor regulating SSS variability in the eastern and western regions.
基金The National Key R&D Program of China under contract Nos 2018YFA0605403 and 2016YFB0500204the Hainan Provincial Natural Science Foundation of China under contract No.418QN301the National Natural Science Foundation of China under contract No.41801238。
文摘Roughness-induced emission from ocean surfaces is one of the main issues that affects the retrieval accuracy of sea surface salinity remote sensing.In previous studies,the correction of roughness effect mainly depended on wind speeds retrieved from scatterometers or those provided by other means,which necessitates a high requirement for accuracy and synchronicity of wind-speed measurements.The aim of this study is to develop a novel roughness correction model of ocean emissivity for the salinity retrieval application.The combined active/passive observations of normalized radar cross-sections(NRCSs)and emissivities from ocean surfaces given by the L-band Aquarius/SAC-D mission,and the auxiliary wind directions collocated from the National Centers for Environmental Prediction(NCEP)dataset are used for model development.The model is validated against the observations and the Aquarius standard algorithms of roughness-induced emissivity correction.Comparisons between model computations and measurements indicate that the model has better accuracy in computing wind-induced brightness temperature in the upwind/downwind directions or for the surfaces with smaller NRCSs,which can be better than 0.3 K.However,for crosswind directions and larger NRCSs,the model accuracy is relatively low.A model using HH-polarized NRCSs yields better accuracy than that using VV-polarized ones.For a fair comparison to the Aquarius standard algorithms using wind speeds retrieved from multi-source data,the maximum likelihood estimation is employed to produce results combining our model calculations and those using other sources.Numerical simulations show that combined results basically have higher accuracy than the standard algorithms.
基金The National Natural Science Foundation of China under contract No.41276088
文摘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.
文摘Scorpio 10.24-11.22 Sagittarius 11.23〜12.21 Capricorn 12.22-1.20 Aquarius 1.21-2.19 Scorpio season has arrived and,as always,just in time for Halloween.Crack out that ouija board and slide into the DMs of the ghosts in your building.The occult awaits you.
