Soil Moisture And Ocean Salinity Sea Surface Salinity Research Articles

Impact of assimilating surface salinity from SMOS on ocean circulation estimates

AbstractIn a pilot attempt, the GECCO2 synthesis system is being used to investigate the impact of SMOS sea surface salinity (SSS) observations on estimates of SSS and freshwater fluxes. The paper focuses on the period 2010–2011, during which, in addition to traditional in situ and satellite observations, SMOS SSS is assimilated. A prior SMOS SSS error field is inferred through a comparison of the satellite data with in situ salinity data and reveals large biases (>1 g/kg) in the SMOS product near continents and in the Southern Ocean. Employing this error estimate in the assimilation procedure leads only to an insignificant impact of SMOS SSS on the estimated ocean state. However, when reducing the error artificially by a factor of 10, the SMOS data can be reproduced well in the interior ocean. In this case, the previously remaining positive model bias with respect to in situ salinity is changed to a negative bias while the misfit slightly increased. The clear freshening can be attributed to the SMOS bias with respect to in situ data. The associated increase in freshwater input in the tropical oceans enhances slightly the correspondence of the estimated fluxes to the independent satellite‐based estimate from HOAPS except for the South Pacific and South Atlantic. On short‐timescales, changes in the estimated surface salinity result primarily from changes in surface freshwater fluxes, while over longer periods ocean dynamics become increasingly more important for changing the near‐surface salinity.

Assessment of Two SMOS Sea Surface Salinity Level 3 Products Against Argo Upper Salinity Measurements

Since the launch of the Soil Moisture and Ocean Salinity (SMOS), different sea surface salinity (SSS) level 3 (L3) research products have been developed by several institutions, including the Barcelona Expert Centre (BEC) in Spain and the Centre Aval de Traitement des Donnees SMOS (CATDS) in France. This letter assesses the performances of BEC “Ocean Reprocessing Campaign 2012001” L3 data and CATDS Version 2 (V02) L3 data by comparing both of them with Argo upper salinity measurements (within 5-m depth). Twenty-month data from May 2010 to December 2011 are selected for the study. Through computing differences and root-mean-square errors of the differences between BEC and Argo data as well as between CATDS V02 and Argo data at each 1° ×1° grid point, it is observed that CATDS V02 data are of better quality along coasts and in high latitudes than BEC data owing to different-SSS-bias mitigation such as applying a correction with respect to the climatology in 5° ×5° boxes and using the same ocean target transformation for the whole processing. In open ocean, however, both SMOS data sets perform very well. Several regions are picked out to conduct the time series comparisons, and the results are similar.

New blending algorithm to synergize ocean variables: The case of SMOS sea surface salinity maps

Using the information of an ocean variable of a given kind to improve another variable of a different kind may be a challenging task, especially when they undergo different physical processes. Statistical methods and assimilation in numerical models had been so far the main ways to perform this type of blending, but these are relatively complicated methods that usually introduce other sources of error and uncertainty. In this paper, the existence of a multifractal hierarchy pervading the structure of all ocean scalars is exploited to introduce a new blending method. This method is not parametric and requires no knowledge about the physics governing the evolution of the scalars, provided that both scalars have the same multifractal structure. We have applied this methodology to SMOS SSS maps, using OI SST maps as template variables, observing not only a qualitative but also a significant quantitative improvement.

Errors in SMOS Sea Surface Salinity and their dependency on a priori wind speed

The wind speed (WS) provided by the European Centre for Medium-Range Weather Forecasts (ECMWF) is used to initialize the retrieval process of WS and Sea Surface Salinity (SSS) obtained by the Soil Moisture and Ocean Salinity (SMOS) mission. This process compensates for the lack of onboard instrument providing a measure of ocean surface WS independent of the L-band radiometer measurements. The SMOS-retrieved WS in the center of the swath (±300km) is adjusted regarding to its a priori estimate. The quality of the SMOS-retrieved SSS (SSSSMOS) is better at the center of the swath than at the edge of the swatch because the larger number of brightness temperature measurements available at the center of the swath reduces the effects of noise and because the greater variety of incidence angles provides more scope for adjusting the WS. This highlights the advantage of using a multi-parameter retrieval with respect to a SSS-only retrieval in which the WS would be entirely prescribed. Systematic inconsistencies between the atmospheric WS modeled using ECMWF and the WS sensed by radiometers are observed. These inconsistencies in the WS are reduced by the retrieval scheme but they still lead to residual biases in the SSSSMOS, especially in the eastern equatorial Pacific ocean if the ECMWF WS is used as an a priori estimate.

Assessment of Satellite-Derived Sea Surface Salinity in the Indian Ocean

The study has been motivated by the desire to assess the performance of sea surface salinity (SSS) from the Soil Moisture and Ocean Salinity (SMOS) satellite launched by the European Space Agency. Daily Level 3 product on a 0.25° × 0.25° grid for the year 2010 has been used for this assessment in the Indian Ocean. Various data sets, like the in situ data sets available from the Research Moored Array for African-Asian-Australian Monsoon Analysis and Prediction (RAMA) buoys and Argo floats and also the data sets from modular ocean model version 3 simulations, have been utilized for this purpose. Comparison made at two buoy locations suggests good quality of SMOS SSS with root-mean-square error of the order of 0.36 and 0.34 psu. The triple collocation method, which explicitly takes into account the error characteristics of the SMOS, Argo, and model data sets, has been used for further validation of the SMOS data. Since the Indian Ocean exhibits characteristically different patterns of SSS in its different subregions, the study area has been divided into different such subregions. The SMOS-derived SSS appears to be of very good quality in the equatorial Indian Ocean and southern Indian Ocean, while the data are of poorer quality in the Arabian Sea and the Bay of Bengal possibly because of the errors in SSS retrieval due to the land contamination and strong winds.

Sea surface freshening inferred from SMOS and ARGO salinity: impact of rain

Abstract. The sea surface salinity (SSS) measured from space by the Soil Moisture and Ocean Salinity (SMOS) mission has recently been revisited by the European Space Agency first campaign reprocessing. We show that, with respect to the previous version, biases close to land and ice greatly decrease. The accuracy of SMOS SSS averaged over 10 days, 100 × 100 km2 in the open ocean and estimated by comparison to ARGO (Array for Real-Time Geostrophic Oceanography) SSS is on the order of 0.3–0.4 in tropical and subtropical regions and 0.5 in a cold region. The averaged negative SSS bias (−0.1) observed in the tropical Pacific Ocean between 5° N and 15° N, relatively to other regions, is suppressed when SMOS observations concomitant with rain events, as detected from SSM/Is (Special Sensor Microwave Imager) rain rates, are removed from the SMOS–ARGO comparisons. The SMOS freshening is linearly correlated to SSM/Is rain rate with a slope estimated to −0.14 mm−1 h, after correction for rain atmospheric contribution. This tendency is the signature of the temporal SSS variability between the time of SMOS and ARGO measurements linked to rain variability and of the vertical salinity stratification between the first centimeter of the sea surface layer sampled by SMOS and the 5 m depth sampled by ARGO. However, given that the whole set of collocations includes situations with ARGO measurements concomitant with rain events collocated with SMOS measurements under no rain, the mean −0.1 bias and the negative skewness of the statistical distribution of SMOS minus ARGO SSS difference are very likely the mean signature of the vertical salinity stratification. In the future, the analysis of ongoing in situ salinity measurements in the top 50 cm of the sea surface and of Aquarius satellite SSS are expected to provide complementary information about the sea surface salinity stratification.

Preliminary SMOS Salinity Measurements and Validation in the Indian Ocean

Global sea surface salinity (SSS) measurements retrieved from the European Space Agency's Soil Moisture and Ocean Salinity (SMOS) mission are the first highest resolution salinity data available from space. There are many challenges to measuring salinity from space and obtaining a targeted accuracy of 0.1 psu. Comparisons of Level 2 (L2) SMOS SSS data with the 1/12 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">°</sup> high resolution HYbrid Coordinate Ocean Model (HYCOM) simulations of SSS reveal large differences. These differences are minimized for an extent during the creation of Level 3 (L3) SMOS data through spatial and temporal averaging. Depending on the retrieval algorithm used, there are differences between ascending and descending passes with data collected during the descending pass exhibiting a bias toward lower SSS. It is challenging to process SMOS SSS data in the northern Indian Ocean due to radio frequency interference and large seasonal variability due to monsoonal circulation. Comparisons of SMOS L3 data with Argo float SSS and HYCOM SSS indicate the lowest discrepancies in SSS for these data sets occur in the southern tropical Indian Ocean and the largest differences between the compared salinity products are noticed in the Arabian Sea and Bay of Bengal with an erratic root mean square error in the latter region. Higher errors in SSS occurred in coastal areas compared to the open ocean. The accuracy of SMOS salinity measurements is increasing with the maturity of the data and new algorithms.

First Assessment of SMOS Data Over Open Ocean: Part II—Sea Surface Salinity

We validate Soil Moisture and Ocean Salinity (SMOS) sea surface salinity (SSS) retrieved during August 2010 from the European Space Agency SMOS processing. Biases appear close to land and ice and between ascending and descending orbits; they are linked to image reconstruction issues and instrument calibration and remain under study. We validate the SMOS SSS in conditions where these biases appear to be small. We compare SMOS and ARGO SSS over four regions far from land and ice using only ascending orbits. Four modelings of the impact of the wind on the sea surface emissivity have been tested. Results suggest that the L-band brightness temperature is not linearly related to the wind speed at high winds as expected in the presence of emissive foam, but that the foam effect is less than previously modeled. Given the large noise on individual SMOS measurements, a precision suitable for oceanographic studies can only be achieved after averaging SMOS SSS. Over selected regions and after mean bias removal, the precision on SSS retrieved from ascending orbits and averaged over 100 km <formula formulatype="inline" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex Notation="TeX">$ \times$</tex></formula> 100 km and 10 days is between 0.3 and 0.5 pss far from land and sea ice borders. These results have been obtained with forward models not fitted to satellite L-band measurements, and image reconstruction and instrument calibration are expected to improve. Hence, we anticipate that deducing, from SMOS measurements, SSS maps at 200 km <formula formulatype="inline" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex Notation="TeX">$\times$</tex></formula> 200 km, 10 days resolution with an accuracy of 0.2 pss at a global scale is not out of reach.

Validating SMOS Ocean Surface Salinity in the Atlantic With Argo and Operational Ocean Model Data

This paper provides an assessment of synoptic measurements of sea surface salinity (SSS) from the European Space Agency Soil Moisture and Ocean Salinity (SMOS) satellite. Due to the complex nature of the response of L-band signals to SSS, SMOS provides three values of SSS at each grid point from three different forward models. To meet oceanographic requirements for SSS retrieval accuracy, SMOS Level 2 SSS products are averaged over time and space. This paper reports on validation studies in the Atlantic based on monthly Level 3 products on a 1°×1° grid for September 2010. Outside coastal regions, large-scale SSS patterns from SMOS are in general agreement with climatology, Argo, and ocean model output. During September 2010, SSS from descending passes provides reasonable quantitative estimates, while SSS from ascending passes overestimates SSS by over 1 practical salinity unit (psu). The daily mean difference in SSS between ascending and descending passes varies during August-December 2010, reaching a maximum in September. Differences in SMOS SSS from the three models are an order of magnitude smaller than differences between ascending and descending passes. Gridded SMOS SSS data are compared against output from the U.K. Met Office Forecasting Ocean Assimilation Model (FOAM)-Nucleus for European Modelling of the Ocean (NEMO). Basic checks confirm that SSS from FOAM-NEMO is unbiased against Argo and that FOAM-NEMO SSS is a useful independent data source to validate and rapidly identify departures in SMOS SSS. Over the whole Atlantic, SMOS SSS variability against FOAM-NEMO is around 0.9 psu, decreasing to 0.5 psu over the subtropical North Atlantic.

Overview of the First SMOS Sea Surface Salinity Products. Part I: Quality Assessment for the Second Half of 2010

Multi-angular images of the brightness temperature ( <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">T</i> <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">B</sub> ) of the Earth at 1.4 GHz are reconstructed from the Soil Moisture and Ocean Salinity (SMOS) satellite sensor data since end 2009. Sea surface salinity (SSS) products remote sensing from space is being attempted using these data over the world oceans. The quality of the first version of the European Space Agency operational Level 2 (L2) SSS swath products is assessed in this paper, using satellite/in situ SSS data match-ups that were collected over the second half of 2010. This database reveals that 95% of the SMOS L2 products show a global error standard deviation on the order of ~ 1.3 practical salinity scale. Simple spatiotemporal aggregation of the L2 products to generate monthly SSS maps at 1° ×1° spatial resolution reduces the error down to about 0.6 globally and 0.4 in the tropics for 90% of the data. Several major problems are, however, detected in the products. Systematically, SMOS SSS data are biased within a ~ 1500 km wide belt along the world coasts and sea ice edges, with a contamination intensity and spread varying from ascending to descending passes. Numerous world ocean areas are permanently or intermittently contaminated by radio-frequency interferences, particularly in the northern high latitudes and following Asia coastlines. Moreover, temporal drifts in the retrieved SSS fields are found with varying signatures in ascending and descending passes. In descending passes, a time-dependent strong latitudinal bias is found, with maximum amplitude reached at the end of the year. Errors in the forward modeling of the wind-induced emissivity and of the sea surface scattered galactic sources are as well identified, biasing the sss retrievals at high and low winds and when the galactic equator sources are reflected toward the sensor.

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

Sea Surface Salinity (SSS) measured from the future Soil Moisture and Ocean Salinity (SMOS) satellite has to be considered as a new type of data. This paper addresses the impact of assimilating two different simulated SSS data types (raw track observations versus a gridded processed product) in an ocean forecasting system through an Observing System Simulation Experiment (OSSE). The OSSE consists of hindcast experiments assimilating an operational dataset of Sea Surface Temperature (SST), in-situ profiles of temperature and salinity and Sea Level Anomalies (SLA) plus various simulated SMOS SSS data. These assimilation experiments use an eddy permitting model (1/38) covering the North Atlantic from 208S to 708N and a multivariate assimilation system referred to as SAM2v1. This assimilation scheme is a Reduced Order Kalman Filter using a 3D multivariate modal decomposition of the forecast error covariance. The OSSE enables an illustration of the impact of SSS assimilation on a Mercator Ocean regional forecasting system. Several conclusions can be highlighted, such as the importance of the consistency of space/time scales between the data products and the ocean prediction systems used. This study should be viewed as a preliminary step for assimilation of SSS measured from space in an ocean forecasting system

Expected impact of the future SMOS and Aquarius Ocean surface salinity missions in the Mercator Ocean operational systems: New perspectives to monitor ocean circulation

Sea Surface Salinity (SSS) has never been observed from space. The SSS from planned satellite missions such as Soil Moisture and Ocean Salinity (SMOS) and Aquarius is a key to better understanding how ocean circulation is related to water cycle and how both these systems are changing through time. The Observing System Simulation Experiments (OSSEs) presented in this paper has been carried out with an ocean forecasting system developed within the French oceanographic Mercator Ocean context. They consist in hindcast experiments assimilating an operational dataset (Sea Surface Temperature (SST), in-situ profiles of temperature and salinity and Sea Level Anomalies (SLA)) and various simulated SMOS and Aquarius Sea Surface Salinity (SSS) data. These experiments use an eddy permitting model (1/3°) covering the North Atlantic from 20°S to 70°N. The new generation of fully multivariate assimilation system referred to as SAM2v1 which is being developed from the SEEK (Singular Evolutive Extended Kalman) algorithm is used. This scheme is a Reduced Order Kalman Filter using a 3D multivariate modal decomposition of the forecast error covariance. The OSSEs enabled us to show the positive impact of SSS assimilation on the Mercator Ocean operational forecasting system. These experiments particularly show the importance to specify appropriated observation errors and the impact of having and/or combining different observing system. Several conclusions can be highlighted such as the importance of the space/time scales consistency between the data products and our ocean prediction systems. This study has to be considered as an important step for assimilation of SSS measured from space. Further studies have to be conducted with other simulated data, other oceanic configurations and other improved assimilation schemes.

Surface Salinity Retrieved from SMOS Measurements over the Global Ocean: Imprecisions Due to Sea Surface Roughness and Temperature Uncertainties

The Soil Moisture and Ocean Salinity (SMOS) mission recently led by the European Space Agency (ESA) intends to monitor soil moisture and sea surface salinity (SSS). Since the sensitivity of radiometric L-band signal to SSS is weak, measuring SSS with an acceptable accuracy is challenging: it requires both a very stable instrument and very precise corrections of other geophysical signals than the SSS affecting the L-band signal. Concentration is on the sea surface roughness and temperature (SST) effects and the extent to which they need to be corrected to optimize both SSS precision and retrieval complexity. In addition to uncertainties regarding SST and wind speed (W), realistic noise on the SMOS brightness temperatures (Tb's) are considered and possible consequences of Tb biases are examined. In most oceanic regions, random noise in W, SST, and Tb should not hamper the SMOS SSS retrieval within the Global Ocean Data Assimilation Experiment (GODAE) requirements (a precision better than 0.1 pss over 200 km 3 200 km and 10 days). However, minimizing systematic bias errors over the time scale at which the SSS products will be averaged is critical: the GODAE requirement will not be met if Tb's or W is biased in warm waters (258C) by 0.07 K and 0.3 m s 21 , respectively, and in cold waters (58C) by 0.03 K and 0.15 m s 21 , respectively, or if no a priori information on W is available. In order to minimize errors coming from the W natural variability, it is essential to use high-temporal-resolution wind data. The use of the first Stokes parameter instead of bipolarized Tb degrades the SSS precision by less than 10% in most regions, showing that Faraday rotation should not hamper SMOS SSS retrieval.

The WISE 2000 and 2001 field experiments in support of the SMOS mission: sea surface L-band brightness temperature observations and their application to sea surface salinity retrieval

Soil Moisture and Ocean Salinity (SMOS) is an Earth Explorer Opportunity Mission from the European Space Agency with a launch date in 2007. Its goal is to produce global maps of soil moisture and ocean salinity variables for climatic studies using a new dual-polarization L-band (1400-1427 MHz) radiometer Microwave Imaging Radiometer by Aperture Synthesis (MIRAS). SMOS will have multiangular observation capability and can be optionally operated in full-polarimetric mode. At this frequency the sensitivity of the brightness temperature (T/sub B/) to the sea surface salinity (SSS) is low: 0.5 K/psu for a sea surface temperature (SST) of 20/spl deg/C, decreasing to 0.25 K/psu for a SST of 0/spl deg/C. Since other variables than SSS influence the T/sub B/ signal (sea surface temperature, surface roughness and foam), the accuracy of the SSS measurement will degrade unless these effects are properly accounted for. The main objective of the ESA-sponsored Wind and Salinity Experiment (WISE) field experiments has been the improvement of our understanding of the sea state effects on T/sub B/ at different incidence angles and polarizations. This understanding will help to develop and improve sea surface emissivity models to be used in the SMOS SSS retrieval algorithms. This paper summarizes the main results of the WISE field experiments on sea surface emissivity at L-band and its application to a performance study of multiangular sea surface salinity retrieval algorithms. The processing of the data reveals a sensitivity of T/sub B/ to wind speed extrapolated at nadir of /spl sim/0.23-0.25 K/(m/s), increasing at horizontal (H) polarization up to /spl sim/0.5 K/(m/s), and decreasing at vertical (V) polarization down to /spl sim/-0.2 K/(m/s) at 65/spl deg/ incidence angle. The sensitivity of T/sub B/ to significant wave height extrapolated to nadir is /spl sim/1 K/m, increasing at H-polarization up to /spl sim/1.5 K/m, and decreasing at V-polarization down to -0.5 K/m at 65/spl deg/. A modulation of the instantaneous brightness temperature T/sub B/(t) is found to be correlated with the measured sea surface slope spectra. Peaks in T/sub B/(t) are due to foam, which has allowed estimates of the foam brightness temperature and, taking into account the fractional foam coverage, the foam impact on the sea surface brightness temperature. It is suspected that a small azimuthal modulation /spl sim/0.2-0.3 K exists for low to moderate wind speeds. However, much larger values (4-5 K peak-to-peak) were registered during a strong storm, which could be due to increased foam. These sensitivities are satisfactorily compared to numerical models, and multiangular T/sub B/ data have been successfully used to retrieve sea surface salinity.

Determination of sea surface salinity and wind speed by L-band microwave radiometry from a fixed platform

In May 1999, the European Space Agency (ESA) selected SMOS (Soil Moisture and Ocean Salinity) as an Earth Explorer Opportunity mission. One of its goals is the generation of global sea surface salinity (SSS) maps. The satellite sensor is an L-band interferometric radiometer with full-polarimetric capability called MIRAS. The retrieval of SSS from microwave measurements is based on the fact that the brightness temperature (TB ) of seawater is a function of the dielectric constant, temperature and sea surface state (roughness, foam…). The sensitivity of TB to SSS is maximum at L-band, but it is necessary to quantify the other effects to have reliable SSS retrieval. In order to improve the present understanding of these effects on TB , ESA sponsored the Wind and Salinity Experiment (WISE) 2000 and 2001 field campaigns. These experimental results are of great importance for the development of sea surface emissivity models that will be used in the future SMOS SSS retrieval algorithms. This paper presents the influence of the emissivity models on the derived SSS from the data obtained in both campaigns. It also presents the impact on the retrieved SSS of using in situ measured or satellite derived wind information, or even simultaneously estimating the wind speed from the measured multi-angular TB .

L-band sea surface emissivity: Preliminary results of the WISE-2000 campaign and its application to salinity retrieval in the SMOS mission

[1] Soil moisture and ocean salinity at surface level can be measured by passive microwave remote sensing at L-band. To provide global coverage data of soil moisture and ocean salinity with three-day revisit time, the Earth Explorer Opportunity Mission SMOS (Soil Moisture and Ocean Salinity) was selected by ESA (European Space Agency) in May 1999. SMOS' single payload is a Y-shaped 2-D aperture synthesis interferometric radiometer called MIRAS (Microwave Imaging Radiometer by Aperture Synthesis). SMOS presents some particular imaging peculiarities: variation of incidence and azimuth angles, different radiometric sensitivity and accuracy at each direction (pixels), and geometric polarization mixing. Therefore, the accuracy of the geophysical parameter retrieval depends on the knowledge of the angular dependence of the emissivity over a wide range of incidence and azimuth angles. The accuracy of the sea surface salinity retrievals depends on our capability to correct the wind-induced variation of the brightness temperatures. To better understand wind effects, ESA sponsored the WInd and Salinity Experiment 2000 (WISE-2000) from November 15, 2000, to January 16, 2001, in the Casablanca oil rig, at 40 km off the coast of Tarragona (Spain). This paper is divided into two parts. First, it presents the derived sensitivities of the brightness temperatures at vertical and horizontal polarizations with wind speed, and compares to Hollinger's measurements and numerical simulations. Second, these results are applied to the SMOS sea surface salinity (SSS) retrieval problem for different tracks within the swath. It is shown that, except for low SSS and sea surface temperature (SST), the retrieved SSS has a RMS error of approximately 1 psu in one satellite pass.