Precipitation Estimates from SMOS Sea‐Surface Salinity

Supply, Alexandre; Boutin, Jacqueline; Vergely, Jean‐Luc; Martin, Nicolas; Hasson, Audrey; Reverdin, Gilles; Mallet, Cécile; Viltard, Nicolas

doi:10.1002/qj.3110

Cited by 26 publications

(30 citation statements)

References 40 publications

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“…Global ocean coverage is then achieved after about 5 days. Individual Tbs are very noisy (1.6-3.2 K) and lead to a typical noise on SSS of the order of 0.6 pss in tropical and subtropical regions on pixel-wise SSS retrievals (Hernandez et al, 2015;Supply et al, 2017). However, owing to the very good spatio-temporal coverage of SMOS, averaging SMOS SSS over typically one month and 100x100 km 2 results in an accuracy close to 0.2 pss in the open ocean, after removing a climatological mean of SMOS systematic errors .…”

Section: Smos Data and Processing Methodologymentioning

confidence: 99%

New SMOS Sea Surface Salinity with reduced systematic errors and improved variability

Boutin

Vergely

Marchand

et al. 2018

Remote Sensing of Environment

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149

125

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Salinity observing satellites have the potential to monitor river freshwater plumes mesoscale spatiotemporal variations better than any other observing system. In the case of the Soil Moisture and Ocean Salinity (SMOS) satellite mission, this capacity was hampered due to the contamination of SMOS data processing by strong land-sea emissivity contrasts. Kolodziejczyk et al. (2016) (hereafter K2016) developed a methodology to mitigate SMOS systematic errors in the vicinity of continents, that greatly improved the quality of the SMOS Sea Surface Salinity (SSS). Here, we find that SSS variability, however, often remained underestimated, such as near major river mouths. We revise the K2016 methodology with: a) a less stringent filtering of measurements in regions with high SSS natural variability (inferred from SMOS measurements) and b) a correction for seasonally-varying latitudinal systematic errors. With this new mitigation, SMOS SSS becomes more consistent with the independent SMAP SSS close to land, for instance capturing consistent spatio-temporal variations of low salinity waters in the Bay of Bengal and Gulf of Mexico. The standard deviation of the differences between SMOS and SMAP weekly SSS is <0.3 pss in most of the open ocean. The standard deviation of the differences between 18-day SMOS SSS and 100-km averaged ship SSS is 0.20 pss (0.24 pss before correction) in the open ocean. Even if this standard deviation of the differences increases closer to land, the larger SSS variability yields a more favorable signal-to-noise ratio, with r2 between SMOS and SMAP SSS larger than 0.8. The correction also reduces systematic biases associated with man-made Radio Frequency Interferences (RFI), although SMOS SSS remains more impacted by RFI than SMAP SSS. This newly-processed dataset will allow the analysis of SSS variability over a larger than 8 years period in regions previously heavily influenced by land-sea contamination, such as the Bay of Bengal or the Gulf of Mexico. Highlights ► Improved SMOS salinity systematic error correction from Kolodziejczyk et al. (2016) ► Refined variability of sea surface salinity near e.g. major river mouths ► Consistent mesoscale patterns observed by SMOS and SMAP satellite missions

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Section: Smos Data and Processing Methodologymentioning

confidence: 99%

New SMOS Sea Surface Salinity with reduced systematic errors and improved variability

Boutin

Vergely

Marchand

et al. 2018

Remote Sensing of Environment

Self Cite

149

125

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“…1b indicate the loca-tions of valid SSS observations obtained from BGEP. The in situ dataset used in this study is obtained from the GO-SHIP (the Global Ocean Ship-based Hydrographic Investigations Program; Talley et al, 2017) database under the Climate Variability and Predictability Experiment (CLIVAR). The SSS observations in the Beaufort Sea are extracted from CLIVAR/GO-SHIP data with EXPOCODE (33HQ20111003 and 33HQ20121005, Mathis and Monacci, 2014), which are available from https://cdiac.ess-dive.lbl.gov/ftp/oceans/ CARINA/Healy/ (last access: 18 December 2018).…”

Section: Surface Salinity From In Situ Datamentioning

confidence: 99%

“…As a result of the efforts of the national agencies in France and Spain, two Level 3 (L3) data products of SSS are freely available, which are independently developed by the Ocean Salinity Expertise Center of the Centre Aval de Traitement des Données SMOS at The French Research Institute for Exploitation of the Sea (IFREMER) and the Barcelona Expert Centre. These two SMOS products have successfully resolved the Agulhas salinity front (D'Addezio et al, 2016) and proven useful for estimating precipitation (Supply et al, 2018). The work of Olmedo et al (2018) quantitatively evaluates the accuracy of the SMOS Arctic and sub-Arctic SSS to less than 0.35 psu, but this evaluation against Argo data was limited by the lack of data in the Arctic proper.…”

Section: Introductionmentioning

confidence: 99%

Evaluation of Arctic Ocean surface salinities from the Soil Moisture and Ocean Salinity (SMOS) mission against a regional reanalysis and in situ data

Xie¹,

Raj

Bertino

et al. 2019

Ocean Sci.

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Abstract. Recently two gridded sea surface salinity (SSS) products that cover the Arctic Ocean have been derived from the European Space Agency (ESA)'s Soil Moisture and Ocean Salinity (SMOS) mission: one developed by the Barcelona Expert Centre (BEC) and the other developed by the Ocean Salinity Expertise Center of the Centre Aval de Traitement des Données SMOS at IFREMER (The French Research Institute for Exploitation of the Sea) (CEC). The uncertainties of these two SSS products are quantified during the period of 2011–2013 against other SSS products: one data assimilative regional reanalysis; one data-driven reprocessing in the framework of the Copernicus Marine Environment Monitoring Services (CMEMS); two climatologies – the 2013 World Ocean Atlas (WOA) and the Polar science center Hydrographic Climatology (PHC); and in situ datasets, both assimilated and independent. The CMEMS reanalysis comes from the TOPAZ4 system, which assimilates a large set of ocean and sea-ice observations using an ensemble Kalman filter (EnKF). Another CMEMS product is the Multi-OBservations reprocessing (MOB), a multivariate objective analysis combining in situ data with satellite SSS. The monthly root mean squared deviations (RMSD) of both SMOS products, compared to the TOPAZ4 reanalysis, reach 1.5 psu in the Arctic summer, while in the winter months the BEC SSS is closer to TOPAZ4 with a deviation of 0.5 psu. The comparison of CEC satellite SSS against in situ data shows Atlantic Water that is too fresh in the Barents Sea, the Nordic Seas, and in the northern North Atlantic Ocean, consistent with the abnormally fresh deviations from TOPAZ4. When compared to independent in situ data in the Beaufort Sea, the BEC product shows the smallest bias (< 0.1 psu) in summer and the smallest RMSD (1.8 psu). The results also show that all six SSS products share a common challenge: representing freshwater masses (< 24 psu) in the central Arctic. Along the Norwegian coast and at the southwestern coast of Greenland, the BEC SSS shows smaller errors than TOPAZ4 and indicates the potential value of assimilating the satellite-derived salinity in this system.

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“…First, we assume that SSS represents the average mixed layer salinity. Although this assumption is inaccurate in rainy environments such as the ETPac, which has strong shallow stratification under low wind conditions, most of the fresh anomalies associated with heavy rains disappear from the SMOS SSS fields after a few hours (Supply et al, 2017). ETPac eddies are long lived, and it is assumed that on longer timescales the salinity in the mixed layer within the eddies is vertically well mixed.…”

Section: Sss and Sla Variability At 10°nmentioning

confidence: 99%

“…Satellite missions have revealed that SSS anomalies associated with eddies can be monitored for months near river outflows (Fournier, Vandemark, et al, 2017;Fournier, Vialard, et al, 2017) and large anomalies can be seen in the tropical Pacific Ocean following El Niño and La Niña events (Hasson et al, 2014. Studies have also shown that remotely sensed SSS in the tropical Pacific Ocean be used to trace mesoscale features such as tropical instability waves (Lee et al, 2012;Melnichenko et al, 2017;Yin et al, 2014) and heavy rainfall associated with large convective cells in the ITCZ (Supply et al, 2017).…”

Section: Introductionmentioning

confidence: 99%

Intraseasonal Variability of Surface Salinity in the Eastern Tropical Pacific Associated With Mesoscale Eddies

et al. 2019

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Strong variability in sea surface salinity (SSS) in the Eastern Tropical Pacific (ETPac) on intraseasonal to interannual timescales was studied using data from the Soil Moisture and Ocean Salinity, Soil Moisture Active Passive, and Aquarius satellite missions. A zonal wave number‐frequency spectral analysis of SSS reveals a dominant timescale of 50–180 days and spatial scale of 8°–20° of longitude with a distinct seasonal cycle and interannual variability. This intraseasonal SSS signal is detailed in the study of 19 individual ETPac eddies over 2010–2016 identified by their sea level anomalies, propagating westward at a speed of about 17 cm/s. ETPac eddies trap and advect water in their core westward up to 40° of longitude away from the coast. The SSS signatures of these eddies, with an average anomaly of 0.5‐pss magnitude difference from ambient values, enable the study of their dynamics and the mixing of their core waters with the surroundings. Three categories of eddies were identified according to the location where they were first tracked: (1) in the Gulf of Tehuantepec, (2) in the Gulf of Papagayo, and (3) in the open ocean near 100°W–12°N. They all traveled westward near 10°N latitude. Category 3 is of particular interest, as eddies seeded in the Gulf of Tehuantepec grew substantially in the vicinity of the Clipperton Fracture Zone rise and in a region where the mean zonal currents have anticyclonic shear. The evolution of the SSS signature associated with the eddies indicates the importance of mixing to their dissipation.

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Precipitation Estimates from SMOS Sea‐Surface Salinity

Cited by 26 publications

References 40 publications

New SMOS Sea Surface Salinity with reduced systematic errors and improved variability

New SMOS Sea Surface Salinity with reduced systematic errors and improved variability

Evaluation of Arctic Ocean surface salinities from the Soil Moisture and Ocean Salinity (SMOS) mission against a regional reanalysis and in situ data

Intraseasonal Variability of Surface Salinity in the Eastern Tropical Pacific Associated With Mesoscale Eddies

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