Estimating satellite salinity errors for assimilation of Aquarius and SMOS data into climate models

Vinogradova, Nadya; Ponte, Rui M.; Fukumori, Ichiro; Wang, Ou

doi:10.1002/2014jc009906

Cited by 24 publications

(26 citation statements)

References 27 publications

(21 reference statements)

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“…The availability of satellite sea‐surface salinity (SSS) data since the launch of the European Space Agency's (ESA's) Soil Moisture and Ocean Salinity (SMOS) mission in 2009, and subsequent launches of the National Aeronautics and Space Administration's (NASA's) Aquarius and Soil Moisture Active Passive (SMAP) missions in 2011 and 2015 respectively, has allowed various efforts to use the data for validation of ocean forecasts (e.g. Vinogradova et al, ; Martin, ). Efforts to assimilate the satellite SSS data have also taken place.…”

Section: Introductionmentioning

confidence: 99%

Assimilating satellite sea‐surface salinity data from SMOS, Aquarius and SMAP into a global ocean forecasting system

Martin

King

While

et al. 2019

Quart J Royal Meteoro Soc

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Funding informationESA.Measuring Sea-Surface Salinity (SSS) from space is a relatively recent technique that relies on L-band radiometry that has evolved to a point where useful information is provided every few days. The impact of assimilating satellite SSS data is investigated using the global FOAM ocean forecasting system. This system assimilates daily satellite SSS products from the ESA Soil Moisture and Ocean Salinity (SMOS), NASA Aquarius and Soil Moisture Active Passive (SMAP) missions equatorward of 40 • N/S, in addition to other observing systems. The data are assimilated using a 3D-Var scheme that includes an observation bias correction scheme to estimate spatially and temporally varying bias estimates for each SSS satellite. The SSS assimilation is tested over 2 years and 3 months covering the 2015/2016 El Niño with assessment focussed on the tropical regions, particularly in the Pacific. Consistent reductions in root-mean-square errors are found in all tropical regions from each of the satellite SSS datasets. The largest improvements of up to about 8% are found in the tropical Pacific from assimilating both the SMOS and SMAP datasets together. The largest impact is in the intertropical convergence zone (ITCZ) in the central Pacific during 2015 and early 2016 where the surface salinity is reduced by about 0.03 pss on average, correcting for too little precipitation. A smaller magnitude, large-scale reduction in the SSS is also seen in the tropical Pacific that increases the modelled surface stratification and leads to reduced vertical mixing. Changes to the SSS in the ITCZ lead to changes in the meridional gradients of SSS that affects the sea surface height and surface currents, both of which are improved in the SMOS assimilation experiment compared to externally produced observation-based datasets. The results suggest that assimilation of SMOS and SMAP satellite data is now of an appropriate quality for operational implementation. 1Ocean Assimilation Model (FOAM) system (Blockley et al., 2014) produces daily analyses and 7-day forecasts of the three-dimensional ocean and sea-ice state, including temperature, salinity and velocity. The same system is also used to produce reanalyses over the satellite altimeter era in order to calibrate seasonal forecasts (MacLachlan et al., 2015).The observing systems routinely assimilated in FOAM include in situ and satellite sea-surface temperature (SST) This article is published with the permission of the Controller of HMSO and the Queen's Printer for Scotland.

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Section: Introductionmentioning

confidence: 99%

Assimilating satellite sea‐surface salinity data from SMOS, Aquarius and SMAP into a global ocean forecasting system

Martin

King

While

et al. 2019

Quart J Royal Meteoro Soc

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“…For examples, the data have been used to study tropical instability waves [Lee et al, 2012Yin et al, 2014], SSS associated with river plumes and marginal seas [e.g., Grodsky et al, 2012;Gierach et al, 2013;Zeng et al, 2014;Fournier et al, 2016], intraseasonal SSS variations associated with the Madden-Julian Oscillation [Grunseich et al, 2013;Guan et al, 2014;Li et al, 2015], mesoscale eddies [e.g., Reul et al, 2014b], Rossby waves [Menezes et al, 2014], and interannual variations associated with climate modes [e.g., Qu and Yu, 2014;Du and Zhang, 2015]. The data have also been used to improve ocean state estimation and seasonal climate prediction [e.g., Köhl et al, 2014;Vinogradova et al, 2014;Toyoda et al, 2015;Hackert et al, 2014]. SSS is being retrieved from NASA's Soil Moisture Active Passive (SMAP) to provide continuity of NASA's SSS measurements.…”

Section: Introductionmentioning

confidence: 99%

Consistency of Aquarius sea surface salinity with Argo products on various spatial and temporal scales

Lee

2016

Geophysical Research Letters

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Understanding the accuracies of satellite‐derived sea surface salinity measurements in depicting temporal changes and their dependence on spatiotemporal scales is important to applications, capability assessment, and future satellite mission design. This study quantifies the consistency between Aquarius Version 4 monthly gridded sea surface salinity (SSS) with two Argo‐based monthly gridded near‐surface salinity products for describing temporal changes on 1° × 1°, 3° × 3°, and 10° × 10° scales. Globally averaged standard deviation values for Aquarius‐Argo salinity differences on these three spatial scales are 0.16, 0.14, and 0.09 practical salinity unit (psu), compared to those between the two Argo products of 0.10, 0.09, and 0.04 psu. The consistency between Aquarius and Argo is similar to that between the two Argo products in the tropics for seasonal signals, and in the tropics and midlatitudes for nonseasonal signals. Therefore, the uncertainties of Argo products for various scales need to be considered in evaluating satellite SSS. Innovative satellite technologies are needed to improve high‐latitude satellite SSS measurements.

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“…Grunseich, Subrahmanyam, and Wang (2013) showed that Aquarius-based SSS can detect the propagation of Madden-Julian oscillation. SSS from the SMOS and Aquarius missions also showed a positive impact on an ocean general circulation model simulation (Chakraborty et al 2014;Vinogradova et al 2014).…”

Section: Introductionmentioning

confidence: 96%

Variability of sea surface salinity in the tropical Indian Ocean as inferred from Aquarius and in situ data sets

Momin

Mitra

Prakash

et al. 2015

International Journal of Remote Sensing

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Sea surface salinity (SSS) is one of the key components of the Earth's global water cycle. Reliable information on SSS is very important for ocean modelling, data assimilation, and ocean and climate research applications. In this study, SSS variability in the tropical Indian Ocean (TIO) was analysed using the Aquarius instrument on board the SAC-D satellite and in situ observations from the Research Moored Array for African-Asian-Australian Monsoon Analysis and Prediction (RAMA) buoys and Array for Real-Time Geostrophic Oceanography (ARGO) data sets for the period 2012-2013. Comparison of two recent versions (V2 and V3) of Aquarius-based SSS estimates to nine RAMA buoys on a daily timescale showed excellent mutual agreement. The systematic underestimation of SSS by satellite-based V2 products over the TIO shows a clear advantage for the new version product (V3). A larger root-meansquare error of the order of 0.50 psu in the satellite-based SSS was observed over the highly variable (larger standard deviation) Bay of Bengal region as compared with ARGO data sets. In the eastern equatorial Indian Ocean region, satellite-based SSS overestimated SSS below 34 psu and underestimated SSS of 34-35 psu as compared with ARGO data. However, the V3 SSS from Aquarius showed marginal improvement over V2 SSS. Monthly variation and fast Fourier analysis of the satellite-based SSS estimates are in reasonably good agreement with in situ observations which suggest that Aquarius is able to capture SSS variability in the TIO. The Aquarius-based V3 SSS showed a temporal autocorrelation of 0.6 over most parts of the TIO up to day 10, and decreased gradually with time. Overall analysis suggests that Aquarius-derived V3 SSS can detect variability in SSS satisfactorily in the TIO and is in reasonably good agreement with in situ observations.

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Estimating satellite salinity errors for assimilation of Aquarius and SMOS data into climate models

Cited by 24 publications

References 27 publications

Assimilating satellite sea‐surface salinity data from SMOS, Aquarius and SMAP into a global ocean forecasting system

Assimilating satellite sea‐surface salinity data from SMOS, Aquarius and SMAP into a global ocean forecasting system

Consistency of Aquarius sea surface salinity with Argo products on various spatial and temporal scales

Variability of sea surface salinity in the tropical Indian Ocean as inferred from Aquarius and in situ data sets

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