Validating SMAP SSS with in situ measurements

Tang, Wenqing; Fore, Alexander; Yueh, Simon; Lee, T.; Hayashi, Atsushi; Sanchez-Franks, Alejandra; Baranowski, Dariusz B.

doi:10.1016/j.rse.2017.08.021

Cited by 88 publications

(83 citation statements)

References 25 publications

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“…Global Tropical Moored Buoy Array (McPhaden et al ., ) SSS, includes SSS observations from Research Moored Array for African‐Asian‐Australian Monsoon Analysis and Prediction (RAMA) in the Indian Ocean (McPhaden et al ., ), Tropical Atmosphere Ocean (TAO)/TRITON array in the Pacific (McPhaden, ; McPhaden et al ., ) and the Pilot Research Moored Array in the Tropical Atlantic (PIRATA: Servain et al ., ; Bourlès et al ., ). SSS observations from buoys are available at ∼1 m from the surface (Tang et al ., ). Recently, Remote Sensing Systems announced the latest version 3.0 SMAP SSS products (both 8‐day running mean and monthly mean) at 40 km and 70 km spatial resolutions which are available at http://podaac-opendap.jpl.nasa.gov/opendap/allData/smap.…”

Section: Methodology and Datamentioning

confidence: 65%

“…Similar to the results demonstrated by Tang et al . () and Meissner et al . (), the estimated SSS using the present retrieval technique demonstrates the potential in capturing daily SSS change in the global ocean.…”

Section: Validation Resultsmentioning

confidence: 99%

“…The validation on a monthly scale is carried out using Argo monthly mean SSS observations at 1° × 1° spatial resolution obtained from http://apdrc.soest.hawaii.edu/projects/Argo. These Argo monthly mean products are generated using float measurements within 5 and 2.5 m of the surface (Tang et al ., ). Global Tropical Moored Buoy Array (McPhaden et al ., ) SSS, includes SSS observations from Research Moored Array for African‐Asian‐Australian Monsoon Analysis and Prediction (RAMA) in the Indian Ocean (McPhaden et al ., ), Tropical Atmosphere Ocean (TAO)/TRITON array in the Pacific (McPhaden, ; McPhaden et al ., ) and the Pilot Research Moored Array in the Tropical Atlantic (PIRATA: Servain et al ., ; Bourlès et al ., ).…”

Section: Methodology and Datamentioning

confidence: 99%

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Retrieval of sea surface salinity from SMAP L‐band radiometer: A novel approach for wind speed correction

Sharma

2019

Quart J Royal Meteoro Soc

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The present article introduces a new sea surface salinity (SSS) retrieval technique from the Soil Moisture Active Passive (SMAP) L–band (1.4 GHz) microwave radiometer. The ocean surface wind speed (WS) correction, which is very critical in any of the SSS retrieval techniques, is accounted for with a different approach. A model function between ΔSSS (the difference between SSS of flat and rough ocean surfaces) and ocean surface WS is developed and used to correct the impact of ocean surface roughness due to the winds on SSS. In addition to WS correction, the retrieval technique appropriately accounts for the effects of atmospheric attenuation on L‐band frequency due to water vapour and oxygen, and ocean surface temperature on SSS. The performance of the estimations is statistically evaluated by comparing the estimations with the in situ Argo, buoy SSS, and SMAP version 3, 70 km resolution SSS observations in global oceans during 2016. The retrieved salinity well captured the global distribution patterns of SSS, including low and high values. Comparison of retrieved salinity with Argo and SMAP products show a root‐mean‐square error (RMSE) of 0.32 and 0.39 psu, a Pearson correlation coefficient of 0.94 and 0.93, and a scatter index of 0.0093 and 0.011 respectively on a monthly time‐scale. In addition, retrieved SSS promisingly captured day‐to‐day variability in SSS observed by tropical moored buoy SSS with RMSE of 0.36 psu and a Pearson correlation coefficient of 0.94. The validation confirms the potential ability of the new retrieval technique in capturing SSS changes on daily and monthly time‐scales.

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Section: Methodology and Datamentioning

confidence: 65%

Section: Validation Resultsmentioning

confidence: 99%

Section: Methodology and Datamentioning

confidence: 99%

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Retrieval of sea surface salinity from SMAP L‐band radiometer: A novel approach for wind speed correction

Sharma

2019

Quart J Royal Meteoro Soc

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“…This time variability is indeed present, but mostly for the higher latitude fresh bias regions where the magnitude increases during cold seasons (Figure 2b). In contrast, the tropical positive bias has only minor seasonal changes, in turn suggesting it may not be related to SST (Tang et al [12]). Also, note these bias estimates implicitly include any haline stratification that is present between the surface and the shallowest ARGO measurement level ~5 m, which is a factor that at present is poorly characterized (Grodsky et al [21]; Liu et al [22]; Song et al [23]).…”

Section: Global Distribution Of Smap Bias From Comparisons With Argomentioning

confidence: 99%

“…Satellite SSS is subject to an SST-dependent bias that is, at present, negative and amplified in winter and early spring due to the SST-related drop in microwave sensor sensitivity (e.g., Köhler et al [10]; Meissner et al [11]). On top of that, satellite SSS is subject to a land contamination bias (e.g., Tang et al [12]). The two bias components have pronounced seasonal signatures due to seasonal changes in SSTrelated sensor sensitivity and land emissivity.…”

Section: Introductionmentioning

confidence: 99%

Assessing Coastal SMAP Surface Salinity Accuracy and Its Application to Monitoring Gulf of Maine Circulation Dynamics

2018

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Abstract:Monitoring the cold and productive waters of the Gulf of Maine and their interactions with the nearby northwestern (NW) Atlantic shelf is important but challenging. Although remotely sensed sea surface temperature (SST), ocean color, and sea level have become routine, much of the water exchange physics is reflected in salinity fields. The recent invention of satellite salinity sensors, including the Soil Moisture Active Passive (SMAP) radiometer, opens new prospects in regional shelf studies. However, local sea surface salinity (SSS) retrieval is challenging due to both cold SST limiting salinity sensor sensitivity and proximity to land. For the NW Atlantic, our analysis shows that SMAP SSS is subject to an SST-dependent bias that is negative and amplifies in winter and early spring due to the SST-related drop in SMAP sensor sensitivity. On top of that, SMAP SSS is subject to a land contamination bias. The latter bias becomes noticeable and negative when the antenna land contamination factor (LC) exceeds 0.2%, and attains maximum negative values at LC = 0.4%. Coastward of LC = 0.5%, a significant positive land contamination bias in absolute SMAP SSS is evident. SST and land contamination bias components are seasonally dependent due to seasonal changes in SST/winds and terrestrial microwave properties. Fortunately, it is shown that SSS anomalies computed relative to a satellite SSS climatology can effectively remove such seasonal biases along with the real seasonal cycle. SMAP monthly SSS anomalies have sufficient accuracy and applicability to extend nearer to the coasts. They are used to examine the Gulf of Maine water inflow, which displayed important water intrusions in between Georges Banks and Nova Scotia in the winters of 2016/17 and 2017/18. Water intrusion patterns observed by SMAP are generally consistent with independent measurements from the European Soil Moisture Ocean Salinity (SMOS) mission. Circulation dynamics related to the 2016/2017 period and enhanced wind-driven Scotian Shelf transport into the Gulf of Maine are discussed.

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Northward Pathway Across the Tropical North Pacific Ocean Revealed by Surface Salinity: How do El Niño Anomalies Reach Hawaii?

et al. 2018

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Using the unprecedented 7 year monitoring of sea surface salinity (SSS) from the Soil Moisture Ocean Salinity (SMOS) satellite mission, an unexpected large‐scale anomaly at 20°N is studied in the tropical Pacific Ocean following the 2015‐2016 extreme El Niño event. This basin‐wide negative anomaly (below −0.3) is present in October 2015 between 15 and 25°N, reaching the Hawaiian archipelago. It has not been previously observed during El Niño events. It is accompanied by a negative equatorial SSS anomaly at the dateline (below −0.5) which has been previously described as an El Niño‐associated SSS anomaly. A wide range of observations (in situ and space‐borne) and a state‐of‐the‐art ocean model simulation are used together to characterize and understand the mechanisms leading to this singular SSS signal. The extra‐equatorial negative SSS anomaly is found to be a superposition of a persisting SSS anomaly due to the 2014 weak El Niño and of the larger 2015‐2016 El Niño SSS anomaly. Both were advected northward in the tropical current system by the mean Ekman currents and hypothetically by instabilities in the zonal currents patterns. An analysis of analogous structures in the past 20 years shows that this northward displacement of SSS anomalies is not El Niño specific, even if their advection is enhanced during El Niño events. This study shows that when surface freshwater fluxes are weak SSS, unlike sea surface temperature, can be used to trace water mass displacement for up to 20 months.

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Validating SMAP SSS with in situ measurements

Cited by 88 publications

References 25 publications

Retrieval of sea surface salinity from SMAP L‐band radiometer: A novel approach for wind speed correction

Retrieval of sea surface salinity from SMAP L‐band radiometer: A novel approach for wind speed correction

Assessing Coastal SMAP Surface Salinity Accuracy and Its Application to Monitoring Gulf of Maine Circulation Dynamics

Northward Pathway Across the Tropical North Pacific Ocean Revealed by Surface Salinity: How do El Niño Anomalies Reach Hawaii?

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