Surface Water And Ocean Topography Data Research Articles

Models of the Sea Surface Height Expression of the Internal-Wave Continuum

Abstract Several models are presented for the sea surface height (SSH) signature of the interior-ocean internal-wave continuum. Most are based on the Garrett–Munk internal-wave model. One is derived from the frequency spectrum of dynamic height from mooring observations. The different models are all plausibly consistent with accepted dynamical and semiempirical spectral descriptions of the climatological interval-wave field in the interior ocean, but they result in different proportionalities between interior and SSH spectral energy levels. The differences arise in part from differences in the treatment of near-surface stratification, and a major source of uncertainty for all the models comes from inadequately constrained assumptions about the energy in the low-vertical-mode internal-wave field. Most of these models suggest that the SSH signature of the internal-wave continuum will be visible in SSH measurements from the Surface Water and Ocean Topography (SWOT) wide-swath satellite altimeter. Temporal variability of internal-wave energy levels and the internal-wave directional spectrum are less well characterized but will also be consequential for the observability of internal-wave signals in SWOT data.

Fine-Scale Eddies Detected by SWOT in the Kuroshio Extension

Conventional altimetry has greatly advanced our understanding of mesoscale eddies but falls short in studying fine-scale eddies (<150 km). The newly launched Surface Water and Ocean Topography (SWOT) altimeter, however, with its unprecedented high-resolution capabilities, offers new opportunities to observe these fine-scale eddies. In this study, we use SWOT data to explore these previously elusive fine-scale eddies in the Kuroshio Extension. During SWOT’s fast sampling phase from 29 May 2023 to 10 July 2023, we identified an average of 4.5 fine-scale eddies within each 120 km wide swath. Cyclonic eddies, which are slightly more frequent than the anticyclonic ones (ratio of 1.16), have a similar mean radius of 23.4 km. However, cyclonic eddies exhibit higher amplitudes, averaging 3.5 cm compared to 2.8 cm for anticyclonic eddies. In contrast to the mesoscale eddies detected by conventional altimeters, the fine-scale eddies revealed by SWOT are characterized by smaller sizes and weaker amplitudes. This study offers a preliminary view of fine-scale eddy characteristics from space, highlighting SWOT’s potential to advance our understanding of these dynamic processes. Nonetheless, it also emphasizes the necessity for comprehensive analysis to fully exploit the satellite’s capabilities in monitoring and interpreting complex eddy behaviors.

Enhancing discharge estimation from SWOT satellite data in a tropical tidal river environment

The Surface Water and Ocean Topography (SWOT) mission aims to provide essential data on river width, height and slope in order to estimate worldwide river discharge accurately. This mission offers a powerful tool for monitoring river discharge in dynamic coastal areas, like the Saigon-Dongnai estuary in Southern Vietnam. However, estimating discharge of tidally-influenced rivers using SWOT measurements can be challenging when hydraulic variables have the same order of magnitude as SWOT measurement errors. In this paper we present a methodology to enhance discharge estimation accuracy from SWOT measurements based on simulated SWOT products at the 200 meter node resolution and varying river reach size. We assess measurement error variability and its impact on discharge estimation by employing a Monte Carlo analysis. Our approach significantly improved discharge estimation in the Saigon tidal river, reducing RMSE from 1400 m3/s to 180 m3/s and increasing R² from 0.31 to 0.95. Notably, the percentage of Monte Carlo particles meeting the 30% rRMSE threshold rose from 0% to 79%. This study underscores the feasibility of obtaining reliable discharge estimates from SWOT data in complex coastal areas where hydraulic variables are of the same order of magnitude as SWOT errors. Additionally, the proposed methodology to improve discharge estimation from SWOT measurements is widely adaptable as it can be applied to similar regions and can be combined with any discharge estimation method.

Expected Precision of Gravity Gradient Recovered from Ka-Band Radar Interferometer Observations and Impact of Instrument Errors

Full tensor of gravity gradients contains extremely large amounts of information, which is one of the most important sources for research on recovery seafloor topography and underwater matching navigation. The calculation and accuracy of the full tensor of gravity gradients are worth studying. The Ka-band interferometric radar altimeter (KaRIn) of surface water and ocean topography (SWOT) mission enables high spatial resolution of sea surface height (SSH), which would be beneficial for the calculation of gravity gradients. However, there are no clear accuracy results for the gravity gradients (the gravity gradient tensor represents the second-order derivative of the gravity potential) recovered based on SWOT data. This study evaluated the possible precision of gravity gradients using the discretization method based on simulated SWOT wide-swath data and investigated the impact of instrument errors. The data are simulated based on the sea level anomaly data provided by the European Space Agency. The instrument errors are simulated based on the power spectrum data provided in the SWOT error budget document. Firstly, the full tensor of gravity gradients (SWOT_GGT) is calculated based on deflections of the vertical and gravity anomaly. The distinctions of instrument errors on the ascending and descending orbits are also taken into account in the calculation. The precision of the Tzz component is evaluated by the vertical gravity gradient model provided by the Scripps Institution of Oceanography. All components of SWOT_GGT are validated by the gravity gradients model, which is calculated by the open-source software GrafLab based on spherical harmonic. The Tzz component has the poorest precision among all the components. The reason for the worst accuracy of the Tzz component may be that it is derived by Txx and Tyy, Tzz would have a larger error than Txx and Tyy. The precision of all components is better than 6 E. Among the various errors, the effect of phase error and KaRIn error (random error caused by interferometric radar) on the results is greater than 2 E. The effect of the other four errors on the results is about 0.5 E. Utilizing multi-cycle data for the full tensor of gravity gradients recovery can suppress the effect of errors.

Steric heights of submesoscale processes and internal gravity waves in the subtropical northwestern Pacific and northern South China Sea as revealed by moored observations

The Surface Water and Ocean Topography (SWOT) altimeter mission provides a good opportunity to globally detect submesoscale processes (submesoscales) from high-resolution sea surface height (SSH) measurements. However, in addition to the balanced mesoscale eddies and submesoscales, the measured SSH also contains signals of unbalanced internal gravity waves (IGWs) including internal tides (ITs) and supertidal IGWs (IGWs with frequencies of 3–8 cycles per day). Therefore, a quantitative understanding of the contributions of the above multiscale dynamic processes to the SSH is a prerequisite for exploring submesoscales from the SWOT-derived SSH data. Here, this issue was investigated through analyzing one-year moored observation-derived near-surface steric heights (SHs) in the subtropical northwestern Pacific (NWP) and northern South China Sea (SCS) which have different dynamic backgrounds. In the sense of annual mean, the root-mean-squared (RMS) SHs of submesoscales (defined as processes with periods of 2–15 days here) and IGWs in the SCS are 1.30 and 5.54 cm, respectively, while they are only 0.70 and 3.19 cm in the NWP. Seasonally, the RMS SH of submesoscales is larger in winter than summer in both regions; with respect to the IGWs, their RMS SH is larger in summer than winter in the SCS but it is opposite in the NWP. Generally, the SHs of diurnal and semidiurnal ITs are dominated by the stationary component in both regions but the proportion of the nonstationary component is much larger in the NWP than the SCS. The sum of the SHs by nonstationary diurnal and semidiurnal ITs exceeds that of submesoscales except in winter in the SCS. Furthermore, we also found that the SHs of supertidal IGWs are always larger than those of submesoscales in both regions and both seasons. By decomposing the IGWs with frequencies of 1–8 cpd (i.e., cycles per day) into different baroclinic modes, we found that for all of the IGWs bands, the SHs are overwhelmingly attributed to the first mode. The results in this study will provide useful information for the application of SWOT data in regions with active IGWs.

The Tikhonov-L-curve regularization method for determining the best geoid gradients from SWOT altimetry

The Surface Water and Ocean Topography (SWOT) mission generates dense altimetry data that, when used in geoid gradient component estimations through least-squares collocation (LSC), lead to an ill-conditioned problem. Such problems also arise in geodetic network designs. This study introduces the Tikhonov-L-curve regularization to effectively address this challenge. By pinpointing the maximum curvatures of the L-curve, we discern optimal regularization parameters, countering issues stemming from the dense data of SWOT and the resulting ill-conditioned covariance matrices. Our approach not only stabilizes LSC solutions but also achieves gradient accuracies at 1-microrad levels compared to theoretical values. Additionally, we experimented with a strategic removal process that selectively eliminates adjacent geoid gradients. This technique considerably improves geoid gradient component determinations, especially evident at a threshold distance of 0.755 km within an 8′× 8′ data selection window. While our findings are rooted in simulated SWOT data, they are pivotal for future research intending to employ real SWOT data, anticipated by late 2023. This work serves as a precursor for marine gravity field determinations, emphasizing the importance of stabilized LSC solutions to avoid misleading seafloor signatures due to data compactness.

How Does Wind Influence Near-Nadir and Low-Incidence Ka-Band Radar Backscatter and Coherence from Small Inland Water Bodies?

While many studies have been conducted regarding wind-driven Ka-band scattering on the ocean and sea surfaces, few have identified the impacts of Ka-band scattering on small inland water bodies, and fewer have identified the influence of wind on coherence over water. These previous studies have been limited in spatial scale, covering only large water bodies >25 km2. The recently launched Surface Water and Ocean Topography (SWOT) mission is the first Ka-band InSAR satellite designed for mapping water surface elevations and open water areas for rivers as narrow as 100 m and lakes as small as 0.0625 km2. Because measurements of these types are novel, there remains some uncertainty about expected backscatter amplitudes given wind-driven water surface roughness variability. A previous study using the airborne complement to SWOT, AirSWOT, found that low backscatter and low coherence values were indicative of higher errors in the water surface elevation products, recommending minimum thresholds for backscatter and coherence for filtering the data to increase the accuracy of averaged data for lakes and rivers. We determined that the global average wind speed over lakes is 4 m/s, and after comparing AirSWOT backscatter and coherence data with ERA-5 wind speeds, we found that the minimum required speed to retrieve high backscatter and coherence is 3 m/s. We examined 11,072 lakes across Canada and Alaska, with sizes ranging from 350 m2 to 156 km2, significantly smaller than what could be measured with previous Ka-band instruments in orbit. We found that small lakes (0.0625–0.25 km2) have significantly lower backscatter (3–5 dB) and 0.20–0.25 lower coherence than larger lakes (>1 km2). These results suggest that approximately 75% of SWOT observable lake areas around the globe will have consistently high-accuracy water surface elevations, though seasonal wind variability should remain an important consideration. Despite very small lakes presenting lower average backscatter and coherence, this study asserts that SWOT will be able to accurately resolve the water surface elevations and water surface extents for significantly smaller water bodies than have been previously recorded from satellite altimeters. This study additionally lays the foundation for future high-resolution inland water wind speed studies using SWOT data, when the data become available, as the relationships between wind speed and Ka-band backscatter reflect those of traditional scatterometers designed for oceanic studies.

Quantifying Multifrequency Ocean Altimeter Wind Speed Error Due to Sea Surface Temperature and Resulting Impacts on Satellite Sea Level Measurements

Surface wind speed measurements from a satellite radar altimeter are used to adjust altimeter sea level measurements via sea state bias range correction. We focus here on previously neglected ocean radar backscatter and subsequent wind speed variations due to sea surface temperature (SST) change that may impact these sea level estimates. The expected error depends on the radar operating frequency and may be significant at the Ka band (36 GHz) frequency chosen for the new Surface Water and Ocean Topography (SWOT) satellite launched in December 2022. SWOT is expected to revolutionize oceanography by providing wide-swath Ka band observations and enhanced spatial resolution compared to conventional Ku band (14 GHz) altimetry. The change to the Ka band suggests a reconsideration of SST impact on wind and sea level estimates, and we investigate this in advance of SWOT using existing long-term Ku and Ka band satellite altimeter datasets. This study finds errors up to 1.5 m/s in wind speed estimation and 1.0 cm in sea level for AltiKa altimeter data. Future SWOT data analyses may require consideration of this dependence prior to using its radar backscatter data in its sea level estimation.

Turning Lakes Into River Gauges Using the LakeFlow Algorithm

AbstractRivers and lakes are intrinsically connected waterbodies yet they are rarely used to hydrologically constrain one another with remote sensing. Here we begin to bridge the gap between river and lake hydrology with the introduction of the LakeFlow algorithm. LakeFlow uses river‐lake mass conservation and observations from the Surface Water and Ocean Topography (SWOT) satellite to provide river discharge estimates of lake and reservoir inflows and outflows. We test LakeFlow performance at three lakes using a synthetic SWOT data set assuming the maximum measurement errors defined by the mission science requirements, and we include modeled lateral inflow and lake evaporation data to further constrain the mass balance. We find that LakeFlow produces promising discharge estimates (median Nash‐Sutcliffe efficiency = 0.88, relative bias = 14%). LakeFlow can inform water resources management by providing global lake inflow and outflow estimates, highlighting a path for recognizing rivers and lakes as an interconnected system.

Recommendations for the design of in situ sampling strategies to reconstruct fine-scale ocean currents in the context of SWOT satellite mission

The new Surface Water and Ocean Topography (SWOT) satellite mission aims to provide sea surface height (SSH) measurements in two dimensions along a wide-swath altimeter track with an expected effective resolution down to 15–30 km. In this context our goal is to optimize the design of in situ experiments aimed to reconstruct fine-scale ocean currents (~20 km), such as those that will be conducted to validate the first available tranche of SWOT data. A set of Observing System Simulation Experiments are developed to evaluate different sampling strategies and their impact on the reconstruction of fine-scale sea level and surface ocean velocities. The analysis focuses (i) within a swath of SWOT on the western Mediterranean Sea and (ii) within a SWOT crossover on the subpolar northwest Atlantic. From this evaluation we provide recommendations for the design of in situ experiments that share the same objective. In both regions of study distinct strategies provide reconstructions similar to the ocean truth, especially those consisting of rosette Conductivity Temperature Depth (CTD) casts down to 1000 m and separated by a range of distances between 5 and 15 km. A good compromise considering the advantages of each configuration is the reference design, consisting of CTD casts down to 1000 m and 10 km apart. Faster alternative strategies in the Mediterranean comprise: (i) CTD casts down to 500 m and separated by 10 km and (ii) an underway CTD with a horizontal spacing between profiles of 6 km and a vertical extension of 500 m. In the Atlantic, the geostrophic velocities reconstructed from strategies that only sample the upper 500 m depth have a maximum magnitude ~50% smaller than the ocean truth. A configuration not appropriate for our objective in both regions is the strategy consisting of an underway CTD sampling one profile every 2.5 km and down to 200 m. This suggests that the thermocline and halocline need to be sampled to reconstruct the geostrophic flow at the upper layer. Concerning seasonality, the reference configuration is a design that provides reconstructions similar to the ocean truth in both regions for the period evaluated in summer and also in winter in the Mediterranean.

Applicability of SWOT data in calibrating WRF-Hydro hydrological model over the Tawa River basin

Freshwater is considered an essential natural resource that must be monitored effectively. The Surface Water and Ocean Topography (SWOT) satellite mission scheduled for launch in December 2022 will enable river discharge estimation from surface water extents, elevations, and slopes. However, the SWOT-based discharges will be inconsistent due to infrequent temporal sampling (TS) of SWOT orbit. This study explores the potential of unique TS of SWOT to determine whether it influences the calibration of hydrological model. The Weather Research and Forecast Hydrological system (WRF-Hydro) has been used to simulate river discharge. Further WRF-Hydro model has been calibrated using proxy SWOT data based on a dynamically dimensioned search algorithm. Results show that more than 90% of parameter iterations have KGE values with less than 20% absolute percent difference when only SWOT TS alone has been considered. The results suggest that model calibration using SWOT-based discharge performs similarly to daily in-situ discharge.

Remotely Sensing River Greenhouse Gas Exchange Velocity Using the SWOT Satellite

AbstractTwo decades of research has shown that the global river network emits significant amounts of greenhouse gas. Despite much progress, there is still large uncertainty in the temporal dynamics of gas exchange and thus carbon emissions to the atmosphere. Much of this uncertainty stems from a lack of existing tools for studying the spatiotemporal dynamics of gas exchange velocity (the rate of this diffusive transport). We propose that the NASA/CNES/UKSA/CSA Surface Water and Ocean Topography (SWOT) satellite can provide new insights to fluvial gas exchange modeling upon launch and subsequent data collection in 2022. Here, we exploit the distinct geomorphology of SWOT‐observable rivers (>50 m wide) to develop a physical model of gas exchange that is remotely sensible and explains 50% of log‐transformed variation across 166 field measurements of . We then couple this model with established inversion techniques to develop BIKER, the “Bayesian Inference of the Exchange Rate” algorithm. We validate BIKER on 47 SWOT‐simulated rivers without an in‐situ calibration, yielding an algorithm that predicts the timeseries solely from SWOT observations with a by‐river median Kling‐Gupta Efficiency of 0.21. BIKER is better at inferring the temporal variation of gas exchange (median correlation coefficient of 0.91), than reproducing the absolute rates of exchange (median normalized RMSE of 51%). Finally, BIKER is robust to measurement errors implicit in the SWOT data. We suggest that BIKER will be useful in mapping global‐scale fluvial gas exchange and improving emissions estimates when coupled with river models.

Flood mapping from proxy surface water and ocean topography (SWOT) satellite mission data over India

The Surface Water and Ocean Topographic (SWOT) mission is aimed to improve flood mapping by providing high-resolution data of water surface elevation, slope and water extent for rivers wider than 100 m and water bodies as small as 62500 m2. Although, SWOT’s data latency and huge data size of rawest point cloud data make it impracticable for real or near-real-time flood forecasting but the SWOT data will provide new insight in the first-hand information of flood mapping. In this study, we explore the potential of SWOT mission for flood mapping over India. It is observed that the spatio-temporal resolution of SWOT allows the observation of 0.67%, 15.79%, 29.24%, 45.54%, and 8.06% of Indian districts with one, two, three, four and more than four observations per cycle of SWOT respectively. It is observed that SWOT would have observed 49.6% of flood events at least once if the mission were launched earlier. The SWOT could also observe 30.6% of small flood events with flood duration <4 at least once. Using the information, this study proposes a flood severity map by combining geomorphological and physical flood parameters for a reach of Brahmaputra river. From the map different flood severity zones can be observed.

The Effects of Uncorrelated Measurement Noise on SWOT Estimates of Sea Surface Height, Velocity, and Vorticity

Abstract The Ka-band Radar Interferometer (KaRIn) on the Surface Water and Ocean Topography (SWOT) satellite will revolutionize satellite altimetry by measuring sea surface height (SSH) with unprecedented accuracy and resolution across two 50-km swaths separated by a 20-km gap. The original plan to provide an SSH product with a footprint diameter of 1 km has changed to providing two SSH data products with footprint diameters of 0.5 and 2 km. The swath-averaged standard deviations and wavenumber spectra of the uncorrelated measurement errors for these footprints are derived from the SWOT science requirements that are expressed in terms of the wavenumber spectrum of SSH after smoothing with a filter cutoff wavelength of 15 km. The availability of two-dimensional fields of SSH within the measurement swaths will provide the first spaceborne estimates of instantaneous surface velocity and vorticity through the geostrophic equations. The swath-averaged standard deviations of the noise in estimates of velocity and vorticity derived by propagation of the uncorrelated SSH measurement noise through the finite difference approximations of the derivatives are shown to be too large for the SWOT data products to be used directly in most applications, even for the coarsest footprint diameter of 2 km. It is shown from wavenumber spectra and maps constructed from simulated SWOT data that additional smoothing will be required for most applications of SWOT estimates of velocity and vorticity. Equations are presented for the swath-averaged standard deviations and wavenumber spectra of residual noise in SSH and geostrophically computed velocity and vorticity after isotropic two-dimensional smoothing for any user-defined smoother and filter cutoff wavelength of the smoothing.

Evaluating SWOT water level information using a large scale hydrology simulator: A case study over India

An important goal of the Surface Water and Ocean Topography (SWOT) mission is to provide river width, height, slope to estimate global river discharges. To appraise the feasibility of SWOT data over India, the present study utilizes the SWOT simulator from Centre National D’Etudes Spatiales (CNES) to generate a long time-series (three years) of SWOT observations over the Mahanadi River basin in India. Two independent sources of river geometry are used in this study, namely satellite-based and hydrodynamic model-based estimates. Satellite-based river geometry is derived using a web-based User Interface (UI) developed in-house to generate time series of accurate river width shapefiles from Sentinel-1 satellite imagery. To compare these results, a 1D unsteady flow analysis is carried out over the Mahanadi River to simulate channel water depth and inundation extent using the Hydrologic Engineering Center's River Analysis System (HEC-RAS) model. The simulated observations are used in this study to assess the potential error in SWOT channel water depth measurements. Results indicate a good spatial correlation between the water surface area derived from Sentinel and HEC-RAS models with a correlation of 0.74. The simulated data shows a bias of 20 cm compared to gauge observations. This study is conducted as a proof of concept in demonstrating the ability of simulated SWOT data in capturing river water surface elevation with respect to the conventional HEC-RAS model. It also implements a new tool in google earth engine (GEE) to generate input to SWOT simulator.

Exploring the potential of SWOT mission for reservoir monitoring in Mahanadi basin

The Surface Water and Ocean Topography (SWOT) mission, which is scheduled to launch in 2022, would offer India with unprecedented hydrologic applications. This study presents the preliminary results of the CNES Large-Scale SWOT Hydrology Simulator to illustrate how SWOT data can be used to monitor reservoir water levels. Except for some factors such as effects due to topography, and error due to idealized water detection, the CNES simulator accounts for practically all of the probable noise expected in future SWOT data. In this paper, we look at how SWOT can be used to monitor reservoirs in difficult situations. SWOT will monitor the reservoir in two swaths in this case, with an unmonitored zone in the middle. This paper provides a strategy for extrapolating data across two swaths to determine reservoir volume. The results demonstrate that the proxy water surface elevation (WSE) created by the CNES simulator has a great potential in scientific research relating to reservoir characteristics and surface water dynamics. It is observed that the reservoir volume estimated using proxy SWOT data has a maximum bias of −7.65 % and a minimum bias of −2.19 %.

Monitoring Lake Levels From Space: Preliminary Analysis With SWOT

Lakes are an essential component of biogeochemical processes, and variations in lake level are regarded as indicators of climate change. For more than a decade, satellite altimetry has successfully monitored variation in water levels over inland seas, lakes, rivers, and wetlands. Through altimetry, the surface water levels are measured at varying temporal scales depending on the orbit cycle of the satellite. The futuristic mission of Surface Water and Ocean Topography (SWOT) scheduled to be launched in year 2022 shall offer the spatial coverage and resolution suitable for water level estimation and volume calculation in small water bodies like lakes worldwide. With a radar interferometer in Ka-band, SWOT proposes to provide two-dimensional maps of water heights 21 days repeat orbit configuration. Cycle average SWOT datasets for land will be developed with higher temporal resolution, with temporal resolution varying geographically. This work assesses the potential of SWOT for monitoring water volumes over a case study lake by analyzing SWOT like synthetic data produced using the SWOT simulator developed by the Centre National d'Etudes Spatiales (CNES). With SWOT relying on a novel technology, the initial 90 days of this mission after launch shall focus on an extensive calibration and validation. Firsthand results of SWOT-simulated water levels and volumes are presented over a case study region in the tropical band, namely, Pookode Lake, in the ecologically fragile district of Wayanad, Kerala, India. It is the second-largest freshwater lake in Kerala that is being affected by anthropogenic activities, causing huge depletion in lake water storage in the last four decades. Our analysis indicated that the lake region is subjected to a rise in temperature of 0.018°C per year. We further assess the potential of remote sensing and SWOT data to monitor water storage of Pookode Lake, which is undergoing a rapid change. Results show that the proxy water surface elevations have immense potential in scientific studies pertaining to lake monitoring across the world. Overall, the study shows the potential of SWOT for monitoring the variability of water levels and volumes in this region.

Assimilating realistically simulated wide-swath altimeter observations in a high-resolution shelf-seas forecasting system

Abstract. The impact of assimilating simulated wide-swath altimetry observations from the upcoming Surface Water and Ocean Topography (SWOT) mission is assessed using observing system simulation experiments (OSSEs). These experiments use the Met Office 1.5 km resolution North West European Shelf analysis and forecasting system. In an effort to understand the importance of future work to account for correlated errors in the data assimilation scheme, we simulate SWOT observations with and without realistic correlated errors. These are assimilated in OSSEs along with simulated observations of the standard observing network, also with realistic errors added. It was found that while the assimilation of SWOT observations without correlated errors reduced the RMSE (root mean squared error) in sea surface height (SSH) and surface current speeds by up to 20 %, the inclusion of correlated errors in the observations degraded both the SSH and surface currents, introduced an erroneous increase in the mean surface currents and degraded the subsurface temperature and salinity. While restricting the SWOT data to the inner half of the swath and applying observation averaging with a 5 km radius negated most of the negative impacts, it also severely limited the positive impacts. To realise the full benefits in the prediction of the ocean mesoscale offered by wide-swath altimetry missions, it is crucial that methods to ameliorate the effects of correlated errors in the processing of the SWOT observations and account for the correlated errors in the assimilation are implemented.

Fine-Scale Ocean Currents Derived From in situ Observations in Anticipation of the Upcoming SWOT Altimetric Mission

After the launch of the Surface Water and Ocean Topography (SWOT) satellite planned for 2022, the region around the Balearic Islands (western Mediterranean Sea) will be the target of several in situ sampling campaigns aimed at validating the first available tranche of SWOT data. In preparation for this validation, the PRE-SWOT cruise in 2018 was conceived to explore the three-dimensional (3D) circulation at scales of 20 km that SWOT aims to resolve, included in the fine-scale range (1–100 km) as defined by the altimetric community. These scales and associated variability are not captured by contemporary nadir altimeters. Temperature and salinity observations reveal a front that separates local Atlantic Water in the northeast from recent Atlantic Water in the southeast, and extends from the surface to ~150 m depth with maximum geostrophic velocities of the order of 0.20 m s−1 and a geostrophic Rossby number that ranges between −0.24 and 0.32. This front is associated with a 3D vertical velocity field characterized by an upwelling cell surrounded by two downwelling cells, one to the east and the other to the west. The upwelling cell is located near an area with high nitrate concentrations, possibly indicating a recent inflow of nutrients. Meanwhile, subduction of chlorophyll-a in the western downwelling cell is detected in glider observations. The comparison of the altimetric geostrophic velocity with the CTD-derived geostrophic velocity, the ADCP horizontal velocity, and drifter trajectories, shows that the present-day resolution of altimetric products precludes the representation of the currents that drive the drifter displacement. The Lagrangian analysis based on these velocities demonstrates that the study region has frontogenetic dynamics not detected by altimetry. Our results suggest that the horizontal component of the flow is mainly geostrophic down to scales of 20 km in the study region and during the period analyzed, and should therefore be resolvable by SWOT and other future satellite-borne altimeters with higher resolutions. In addition, fine-scale features have an impact on the physical and biochemical spatial variability, and multi-platform in situ sampling with a resolution similar to that expected from SWOT can capture this variability.

A Framework for Estimating Global‐Scale River Discharge by Assimilating Satellite Altimetry

AbstractUnderstanding spatial and temporal variations in terrestrial waters is key to assessing the global hydrological cycle. The future Surface Water and Ocean Topography (SWOT) satellite mission will observe the elevation and slope of surface waters at &lt;100 m resolution. Methods for incorporating SWOT measurements into river hydrodynamic models have been developed to generate spatially and temporally continuous discharge estimates. However, most SWOT data assimilation studies have been conducted at the local scale. We developed a novel framework for estimating river discharge at the global scale by incorporating SWOT observations into the CaMa‐Flood hydrodynamic model. The local ensemble transform Kalman filter with adaptive local patches was used to assimilate SWOT observations. We tested the framework using multimodel runoff forcing and inaccurate model parameters represented by corrupted Manning's coefficient values. Assimilation of virtual SWOT observations considerably improved river discharge estimates for continental‐scale rivers at high latitudes (&gt;50°) and also downstream river reaches at low latitudes. High assimilation efficiency in downstream river reaches was related to both local state correction and the propagation of corrected hydrodynamic states from upstream river reaches. Accurate global river discharge estimates were obtained (Kling‐Gupta efficiency [KGE] &gt; 0.90) in river reaches with &gt;270 accumulated overpasses per SWOT cycle when no model error was assumed. Introducing model errors decreased this accuracy (KGE ≈ 0.85). Therefore, improved hydrodynamic models are essential for maximizing SWOT information. These synthetic experiments showed where discharge estimates could be improved using SWOT observations. Further advances are needed for global‐scale data assimilation.