This paper provides an overview of the Dragon 4 project dealing with operational monitoring of sea ice and sea surface salinity (SSS) and new product developments for altimetry data. To improve sea ...ice thickness retrieval, a new method was developed to match the Cryosat-2 radar waveform. Additionally, an automated sea ice drift detection scheme was developed and tested on Sentinel-1 data, and the sea ice drifty capability of Gaofen-4 geostationary optical data was evaluated. A second topic included implementation and validation of a prototype of a Fully-Focussed SAR processor adapted for Sentinel-3 and Sentinel-6 altimeters and evaluation of its performance with Sentinel-3 data over the Yellow Sea; the assessment of sea surface height (SSH), significant wave height (SWH), and wind speed measurements using different altimeters and CFOSAT SWIM; and the fusion of SSH measurements in mapping sea level anomaly (SLA) data to detect mesoscale eddies. Thirdly, the investigations on the retrieval of SSS include simulations to analyse the performances of the Chinese payload configurations of the Interferometric Microwave Radiometer and the Microwave Imager Combined Active and Passive, SSS retrieval under rain conditions, and the combination of active and passive microwave to study extreme winds.
Since 2010, the Soil Moisture and Ocean Salinity (SMOS) satellite mission monitors the earth emission at L-Band. It provides the longest time series of Sea Surface Salinity (SSS) from space over the ...global ocean. However, the SSS retrieval at high latitudes is a challenge because of the low sensitivity L-Band radiometric measurements to SSS in cold waters and to the contamination of SMOS measurements by the vicinity of continents, of sea ice and of Radio Frequency Interferences. In this paper, we assess the quality of weekly SSS fields derived from swath-ordered instantaneous SMOS SSS (so called Level 2) distributed by the European Space Agency. These products are filtered according to new criteria. We use the pseudo-dielectric constant retrieved from SMOS brightness temperatures to filter SSS pixels polluted by sea ice. We identify that the dielectric constant model and the sea surface temperature auxiliary parameter used as prior information in the SMOS SSS retrieval induce significant systematic errors at low temperatures. We propose a novel empirical correction to mitigate those sources of errors at high latitudes.
Comparisons with in-situ measurements ranging from 1 to 11 m depths spotlight huge vertical stratification in fresh regions. This emphasizes the need to consider in-situ salinity as close as possible to the sea surface when validating L-band radiometric SSS which are representative of the first top centimeter.
SSS Standard deviation of differences (STDD) between weekly SMOS SSS and in-situ near surface salinity significantly decrease after applying the SSS correction, from 1.46 pss to 1.28 pss. The correlation between new SMOS SSS and in-situ near surface salinity reaches 0.94. SMOS estimates better capture SSS variability in the Arctic Ocean in comparison to TOPAZ reanalysis (STDD between TOPAZ and in-situ SSS = 1.86 pss), particularly in river plumes with very large SSS spatial gradients.
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•Large signal to noise ratio of SMOS salinity in fresh and variable Arctic Seas.•Near surface vertical stratification critical for SMOS salinity validation.•Improved sea ice filtering using SMOS pseudo dielectric constant.•Improved SMOS salinity considering SST and dielectric constant uncertainties.
Earlier studies have pointed out systematic differences between sea surface salinity retrieved from L-band radiometric measurements and measured in situ , which depend on sea surface temperature ...(SST). We investigate how to cope with these differences given existing physically based radiative transfer models. In order to study differences coming from seawater dielectric constant parametrization, we consider the model of Somaraju and Trumpf (2006) (ST) which is built on sound physical bases and close to a single relaxation term Debye equation. While ST model uses fewer empirically adjusted parameters than other dielectric constant models currently used in salinity retrievals, ST dielectric constants are found close to those obtained using the Meissner and Wentz (2012) (MW) model. The ST parametrization is then slightly modified in order to achieve a better fit with seawater dielectric constant inferred from SMOS data. Upgraded dielectric constant model is intermediate between KS and MW models. Systematic differences between SMOS and in situ salinity are reduced to less than +/−0.2 above 0 °C and within +/−0.05 between 7 °C and 28 °C. Aquarius salinity becomes closer to in situ salinity, and within +/−0.1. The order of magnitude of remaining differences is very similar to the one achieved with the Aquarius version 5 empirical adjustment of wind model SST dependence. The upgraded parametrization is recommended for use in processing the SMOS data. Further assessment or improvement using new laboratory measurements should consider keeping the physics-based formulation by ST that has been shown here to be very efficient.
Abstract
Ten years of L-band radiometric measurements have proven the capability of satellite sea surface salinity (SSS) to resolve large-scale-to-mesoscale SSS features in tropical to subtropical ...ocean. In mid-to-high latitudes, L-band measurements still suffer from large-scale and time-varying errors. Here, a simple method is proposed to mitigate the large-scale and time-varying errors. First, an optimal interpolation using a large correlation scale (~500 km) is used to map independently Soil Moisture Ocean Salinity (SMOS) and Soil Moisture Active Passive (SMAP) level-3 (L3) data. The mapping is compared with the equivalent mapping of in situ observations to estimate the large-scale and seasonal biases. A second mapping is performed on adjusted SSS at the scale of SMOS/SMAP spatial resolution (~45 km). This procedure merges both products and increases the signal-to-noise ratio of the absolute SSS estimates, reducing the root-mean-square difference of in situ satellite products by about 26%–32% from mid- to high latitudes, respectively, in comparison with the existing SMOS and SMAP L3 products. However, in the Arctic Ocean, some issues on satellite retrieved SSS related to, for example, radio frequency interferences, land–sea contamination, and ice–sea contamination remain challenging to reduce given the low sensitivity of L-band radiometric measurements to SSS in cold water. Using the International Thermodynamic Equation Of Seawater—2010 (TEOS-10), the resulting level-4 SSS satellite product is combined with satellite-microwave SST products to estimate sea surface density, spiciness, and haline contraction and thermal expansion coefficients. For the first time, we illustrate how useful these satellite-derived parameters are to fully characterize the surface ocean water masses at large mesoscale.
Two L‐Band (1.4 GHz) microwave radiometer missions, Soil Moisture and Ocean Salinity (SMOS) and Soil Moisture Active and Passive (SMAP) currently provide sea‐surface salinity (SSS) measurements. At ...this frequency, salinity is measured in the first centimetre below the sea surface, which makes it very sensitive to the presence of fresh water lenses linked to rain events. A relationship between salinity anomaly (ΔS) and rain rate (RR) is derived in the Pacific intertropical convergence zone from SMOS SSS measurements and Special Sensor Microwave Imager/Sounder (SSMIS) RR. It is then used to develop an algorithm to estimate RR from SMOS SSS measurements. A heuristic function is developed to correct the SMOS‐estimated negative RR due to measurement noise. Correlation between SMOS and SSMIS RR and between SMOS and Integrated MultisatellitE Retrievals for GPM (IMERG) RR are high when SMOS and SSMIS passes are less than 15 min apart (r = 0.7 at 1° × 1° resolution), showing a good quality of SMOS RR retrievals. When the time shift between SMOS and SSMIS passes increases, the correlation between SMOS and IMERG RR diminishes. This suggests that L‐band radiometry can provide information complementary to GPM missions to improve RR products interpolated at high temporal resolution. The retrieval is successfully tested on SMAP SSS. We also check that our algorithm provides reliable estimates of RR when averaged at a monthly time‐scale.
Rain rate (RR) is derived in the Pacific intertropical convergence zone from SMOS SSS measurements. SMOS and SSMIS RR agree well when SMOS and SSMIS passes are less than 15 min apart (see an example on the figure below; r = 0.7 at 1° × 1° resolution). When the time shift between SMOS and SSMIS passes increases, the correlation between SMOS and IMERG RR diminishes. This suggests that L‐band radiometry can provide information to improve RR products based on morphing techniques.
We investigate the Chukchi and the Beaufort seas in the Arctic Ocean, where salty and warm Pacific Water flows in through the Bering Strait and interacts with the sea ice, contributing to its summer ...melt. Thanks to in situ measurements recorded by two saildrones deployed during summer 2019 and to refined sea ice filtering in satellite L‐band radiometric data, we demonstrate the ability of satellite sea surface salinity (SSS) observed by Soil Moisture and Ocean Salinity and Soil Moisture Active and Passive to capture SSS freshening induced by sea ice melt. We refer to these freshening events as meltwater lenses (MWL). The largest MWL observed by the saildrones during this period occupied a large part of the Chukchi shelf, with a SSS freshening reaching −5 practical salinity scale, persisting for up to 1 month. This MWL restricted the transfer of air‐sea momentum to the upper ocean, as illustrated by measured wind speed and vertical profiles of currents. With satellite‐based sea surface temperature, satellite SSS provides a monitoring of the different water masses encountered in the region during summer 2019. Using sea ice concentration and estimated Ekman transport, we analyze the spatial variability of sea surface properties after the sea ice edge retreat over the Chukchi and the Beaufort seas. The two MWL captured by the saildrones and the satellite measurements resulted from different dynamics. Over the Beaufort Sea, the MWL evolution followed the meridional sea ice retreat whereas, in the Chukchi Sea, a large persisting MWL was generated by advection and subsequent melting of a sea ice filament.
Plain Language Summary
The Arctic Ocean is an area of large variations in salinity. Salinity is a main driver of ocean circulation as it determines (with seawater temperature) the seawater density. However, very little is known about salinity variations there, due to the paucity of measurements near ice and in river plumes where surface water is freshest. Here, we use surface salinity measurements from two autonomous vehicles, named saildrones, to show that satellite measurements can identify the evolution of freshwater lenses that result from sea ice melt. Over the Chukchi and the Beaufort seas, sea surface salinity exhibits large seasonal changes, partly because of the sea ice melting. In this region, water from the North Pacific Ocean enters the Arctic Ocean, resulting in large gradients of salinity and temperature. During summer 2019, the saildrones measured the surface salinity and temperature variability as the sea ice retreated northwards. By comparing these data with the measurements from satellites, we showed that satellites can detect these pools of fresh surface water in the Arctic Ocean, increasing the field of application of satellites to understand changes in conditions that determine the Arctic's role in climate change.
Key Points
Saildrones and L‐band radiometers detect large sea surface salinity variability induced by sea ice over the Chukchi and the Beaufort Sea
Low surface salinity due to sea ice melting decreases the vertical extent of momentum transfer, inhibiting it beyond 10 m depth
Meltwater lenses may persist more than 1 month and reach a surface salinity 5 pss fresher than surrounding waters
Sea Surface Salinity (SSS) is an increasingly used Essential Ocean and Climate Variable. The Soil Moisture and Ocean Salinity (SMOS), Aquarius, and Soil Moisture Active Passive (SMAP) satellite ...missions all provide SSS measurements, with very different instrumental features leading to specific measurement characteristics. The Climate Change Initiative Salinity project (CCI + SSS) aims to produce a SSS Climate Data Record (CDR) that addresses well‐established user needs based on those satellite measurements. To generate a homogeneous CDR, instrumental differences are carefully adjusted based on in‐depth analysis of the measurements themselves, together with some limited use of independent reference data. An optimal interpolation in the time domain without temporal relaxation to reference data or spatial smoothing is applied. This allows preserving the original datasets variability. SSS CCI fields are well suited for monitoring weekly to interannual signals, at spatial scales ranging from 50 km to the basin scale. They display large year‐to‐year seasonal variations over the 2010–2019 decade, sometimes by more than ±0.4 over large regions. The robust standard deviation of the monthly CCI SSS minus in situ Argo salinities is 0.15 globally, while it is at least 0.20 with individual satellite SSS fields. r2 is 0.97, similar or better than with original datasets. The correlation with independent ship thermosalinographs SSS further highlights the CCI data set excellent performance, especially near land areas. During the SMOS‐Aquarius period, when the representativity uncertainties are the largest, r2 is 0.84 with CCI while it is 0.48 with the Aquarius original data set. SSS CCI data are freely available and will be updated and extended as more satellite data become available.
Plain Language Summary
Salinity measures the mass of dissolved salts in seawater. Together with temperature and pressure, it determines the seawater density, which is crucial in driving oceanic motions. Low sea surface salinity (SSS) can be the result of freshwater inputs such as rain, river runoffs, and ice melt. In contrast, high SSS often characterize regions of strong evaporation. The salinity imprint of these processes is then carried by ocean currents over long distances and long periods of time. SSS also impacts ocean circulation through its effect on density, and modulates ocean‐atmosphere exchanges of heat and gases. SSS is a hence key variable for ocean and climate studies, both as a passive tracer and as an important actor of oceanic processes. Since 2010, three satellite missions have monitored SSS with an unprecedented spatial and temporal resolution. For the first time, data from these satellites are combined, taking each instrument specific features into account. The resulting data set enables global SSS to be monitored and studied with unprecedented accuracy over the 2010–2019 period, at a 50 km, weekly or monthly resolution. It reveals large SSS signals related to phenomena affecting climate in various parts of the world ocean.
Key Points
2010–2019 Sea Surface Salinity fields built from three satellite missions data sets
Monitors sea surface salinity variability at large meso‐ to basin scale with unprecedented accuracy and spatio‐temporal coverage
Answers the need for Sea Surface Salinity global fields at higher resolution than 1 month, 1°
ISSI Science Team Meeting Report on Development of a Reference-Quality Model For Ocean Surface Emissivity and Backscatter from the Microwave to the Infrared What: Sixteen members of an International ...Space Science Institute team—from America, Asia, and Europe and with backgrounds in radiative transfer modeling, data assimilation, field campaigns, space agencies, and instrumentation—met to provide a reference-quality model for ocean surface emission and backscatter. Two scale models have achieved considerable success, but there remain areas of uncertainty, including cases where the wind and waves are aligned differently (the so-called horseshoe pattern), cases where there is high wind speed with low wave height, breaking waves and allowing for ocean currents. Since the earliest studies, it has been clear that foam has a significant role (Nordberg et al. 1969; Webster et al. 1976). Practical models Operational weather prediction systems need to assimilate millions of observations in just a few minutes, making many models too slow to be practical. ...a range of simplified or parametric models have been developed. The RSS ocean emissivity model uses a double Debye dielectric constant of seawater from Meissner and Wentz (2004, 2012) and is valid for a sea surface salinity (SSS) range between 0 and 40 psu and SST range between −2° and 32°C. The wind induced component covers the frequency range of 6–90 GHz (Meissner and Wentz 2012) with a special version for L-band (Meissner et al. 2014, 2018).
During the SPURS‐2 2017 tropical Pacific cruise, two drifters were deployed on November 9. The drifters measured temperature and salinity in the top 36 cm, wave spectra, and the noise of rain drops. ...During a short nearly‐circular survey with a 1.8‐km radius around the drifters, the R/V Revelle measured air‐sea fluxes, as well as temperature and salinity stratification in the top 1 m from a towed surface salinity profiler (SSP). A C‐band weather radar measuring rain rate within 1 to 100 km range of the ship observed discrete rain cells organized in a system moving from the southeast to the northwest. Some of the intense rain cells were small‐scale (1 km in diameter or less) with short lifetimes, yet dropped more than 5 cm of water in half an hour near the drifters, whereas the ship measured short rain episodes totaling 1.3 cm of rainfall mostly accompanied by very low wind. The data indicate a large spatial heterogeneity in temperature and salinity, with near‐surface freshening of up to 9 psu measured at different times by the two drifters (separated by less than 500 m) and by the SSP. The drifters indicate deepening of the fresh and cool surface layer during the rain which then thinned during the following 40 minutes with very low wind speed (<2 m/s). Patchy surface‐trapped cold and fresh layers were also observed by the SSP east of the drifters. The high spatial and temporal variability of rainfall and surface‐trapped fresh pools is discussed.
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