Data assimilation of simulated SSS SMOS products in an ocean forecasting system

Tranchant, B.; Testut, Charles-Emmanuel; Ferry, Nicolas; Renault, Lionel; Obligis, E.; Boone, C.; Larnicol, Gilles

doi:10.1080/1755876x.2008.11020099

Cited by 9 publications

(7 citation statements)

References 32 publications

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“…Moreover, during the recent climate slowdown, the sea surface salinity took on a distinctive pattern, implying the importance of salinity in climate variability [ Chen and Tung , ; Cheng et al ., ]. Therefore, it is crucial to get better simulations of ocean salinity fields for ocean and climate models [ Hackert et al ., ; Hasson et al ., ; Tranchant et al ., ].…”

Section: Introductionmentioning

confidence: 99%

The complementary role of SMOS sea surface salinity observations for estimating global ocean salinity state

Cheng

Zhu

et al. 2016

JGR Oceans

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Salinity is a key ocean state property, changes in which reveal the variation of the water cycle and the ocean thermohaline circulation. However, prior to this century, in situ salinity observations were extremely sparse, which decreased the reliability of simulations of ocean general circulation by ocean and climate models. In 2009, sea surface salinity (SSS) observations covered the global ocean via the European Space Agency's Soil Moisture and Ocean Salinity (SMOS) mission, and several versions of global SSS products were subsequently released. How can these data benefit model performance? Previous studies found contradictory results. In this work, we assimilated SMOS-SSS data into the LASG/IAP Climate system Ocean Model (LICOM) using the Ensemble Optimum Interpolation (EnOI) assimilation scheme. To assess and quantify the contribution of SMOS-SSS data to model performance, several tests were conducted. The results indicate that the CECOS/CATDS 2010.V02 SMOS-SSS product can significantly improve model simulations of sea surface/subsurface salinity fields. This study provides the basis for the future assimilation of SMOS-SSS data for short-range climate forecasting. Key Points: SMOS sea surface salinity observations were assimilated into the LICOM ocean model SMOS-SSS data play a complementary role in model salinity simulations Observation error covariances and the choice of SMOS product products are important (2016), The complementary role of SMOS sea surface salinity observations for estimating global ocean salinity state,

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Section: Introductionmentioning

confidence: 99%

The complementary role of SMOS sea surface salinity observations for estimating global ocean salinity state

Cheng

Zhu

et al. 2016

JGR Oceans

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“…Comparing to Equation (14), this means T e f f equals to the soil temperature at τ = 1, with the linear assumption of soil moisture and soil temperature profile. The same normalization as in Equation (16) with T e f f in Equation (12), we have:…”

Section: Characteristic Expression Of T E F Fmentioning

confidence: 99%

“…Both the Soil Moisture and Ocean Salinity (SMOS) [8] and the Soil Moisture Active Passive (SMAP) [9] missions operate at L-band for providing the brightness temperature and soil moisture data products. With the efforts from SMOS and SMAP missions, abundant data have been produced and applied in various studies [10][11][12][13][14].…”

Section: Introductionmentioning

confidence: 99%

Estimation of Penetration Depth from Soil Effective Temperature in Microwave Radiometry

Zeng

Wen

et al. 2018

Remote Sensing

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Abstract:Soil moisture is an essential variable in Earth surface modeling. Two dedicated satellite missions, the Soil Moisture and Ocean Salinity (SMOS) and the Soil Moisture Active Passive (SMAP), are currently in operation to map the global distribution of soil moisture. However, at the longer L-band wavelength of these satellites, the emitting behavior of the land becomes very complex due to the unknown deeper penetration depth. This complexity leads to more uncertainty in calibration and validation of satellite soil moisture product and their applications. In the framework of zeroth-order incoherent microwave radiative transfer model, the soil effective temperature is the only component that contains depth information and thus provides the necessary link to quantify the penetration depth. By means of the multi-layer soil effective temperature (Lv's T e f f ) scheme, we have determined the relationship between the penetration depth and soil effective temperature and verified it against field observations at the Maqu Network. The key findings are that the penetration depth can be estimated according to Lv's T e f f scheme with the assumption of linear soil temperature gradient along the optical depth; and conversely, the soil temperature at the penetration depth should be equal to the soil effective temperature with the same linear assumption. The accuracy of this inference depends on to what extent the assumption of linear soil temperature gradient is satisfied. The result of this study is expected to advance understanding of the soil moisture products retrieved by SMOS and SMAP and improve the techniques in data assimilation and climate research.

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“…The atmospheric forcing for the real time production is based on daily averages of the atmospheric variables or flux provided by the ECMWF real time forecasting system. The assimilation scheme (Tranchant et al, 2008) used in both configurations is based on the singular evolutive extended Kalman (SEEK) filter which allows assimilation of the sea level along-track satellite observations delivered by the MyOcean Sea Level Thematic Assembly Centre, the temperature and salinity profiles from the MyOcean In Situ Thematic Assembly Centre and the RTG (Real-Time-Global) sea surface temperature (http://polar.ncep.noaa.gov/sst/oper/ Welcome.html). The model outputs used in this study are based on the "best analysis", which is performed every week with a one week delay in time to assimilate the most of observations over a one week assimilation window.…”

Section: Model Datamentioning

confidence: 99%

Towards high resolution mapping of 3-D mesoscale dynamics from observations

et al. 2012

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Abstract. The MyOcean R&D project MESCLA (MEsoSCaLe dynamical Analysis through combined model, satellite and in situ data) was devoted to the high resolution 3-D retrieval of tracer and velocity fields in the oceans, based on the combination of in situ and satellite observations and quasi-geostrophic dynamical models. The retrieval techniques were also tested and compared with the output of a primitive equation model, with particular attention to the accuracy of the vertical velocity field as estimated through the Q vector formulation of the omega equation. The project focused on a test case, covering the region where the Gulf Stream separates from the US East Coast. This work demonstrated that innovative methods for the high resolution mapping of 3-D mesoscale dynamics from observations can be used to build the next generations of operational observation-based products.

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Data assimilation of simulated SSS SMOS products in an ocean forecasting system

Cited by 9 publications

References 32 publications

The complementary role of SMOS sea surface salinity observations for estimating global ocean salinity state

The complementary role of SMOS sea surface salinity observations for estimating global ocean salinity state

Estimation of Penetration Depth from Soil Effective Temperature in Microwave Radiometry

Towards high resolution mapping of 3-D mesoscale dynamics from observations

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