Surface Water And Ocean Topography Research Articles

Internal tides off the Amazon shelf during two contrasted seasons: interactions with background circulation and SSH imprints

Abstract. The Amazon shelf break is a key region for internal tide (IT) generation. It also shows a large seasonal variation in circulation and associated stratification. This study, based on a high-resolution model (1/36∘) explicitly forced by tide, aims to better characterize how the ITs vary between two contrasted seasons. During the season from March to July (MAMJJ) the currents and mesoscale eddies are weak while the pycnocline is shallower and stronger. From August to December (ASOND) mean currents and mesoscale eddies are strong, and the pycnocline is deeper and weaker than in MAMJJ. For both seasons, semi-diurnal M2 ITs are generated on the shelf break mainly between the 100 and 1000 m isobath in the model. South of 2∘ N, the conversion from barotropic to baroclinic tide is more efficient in MAMJJ than in ASOND. Local dissipation of the coherent M2 at the generation sites is higher in MAMJJ (30 %) than in ASOND (22 %), because higher modes are favourably generated (modes 2 and 3), making the internal wave packet more dispersive. The remaining fraction (70 %–80 %) propagates away from the generation sites and mainly dissipates locally every ∼ 100 km, which corresponds to the mode 1 reflection beams. About 13 %, 30 %, and 40 % of the M2 coherent IT dissipates at the first, second, and third beams. M2 coherent baroclinic flux propagates more northward during MAMJJ while it seems to be blocked at 6∘ N during ASOND. There is no intensified dissipation of the coherent M2 that could explain the disappearance of the coherent flux. In fact, the flux at this location becomes more incoherent because of strong interaction with the currents. This has been shown in the paper using 25 h mean snapshots of the baroclinic flux that shows branching and stronger eastward deviation of the IT when interacting with mesoscale eddies and stratification during ASOND. Finally, we evaluated sea surface height (SSH) frequency and wavenumber spectra for subtidal (f<1/28 h−1), tidal (1/28 h−1 < f<1/11 h−1), and supertidal (f>1/11 h−1) frequencies. Tidal frequencies explain most of the SSH variability for wavelengths between 250 and 70 km. Below 70 km, the SSH is mainly incoherent and supertidal. The length scale at which the SSH becomes dominated by unbalanced (non-geostrophic) IT was estimated to be around 250 km. Our results highlight the complexity of correctly predicting IT SSH in order to better observe mesoscale and sub-mesoscale eddies from existing and upcoming altimetric missions, notably the Surface Water Ocean Topography (SWOT) mission.

Building User‐Readiness for Satellite Earth Observing Missions: The Case of the Surface Water and Ocean Topography (SWOT) Mission

AbstractThe goal in this commentary is to share the development of the NASA Applied Science pre‐launch protocol called the Early Adopter Program (EAP) that is designed to build user‐readiness of planned satellite Earth observing missions proactively and before the launch. Here we focus in particular on the Surface Water and Ocean Topography satellite mission EAP as an illustration of benefits of such a program of proactive and sustained user community engagement. Such a commentary will be of value to other satellite Earth observation missions that are currently in service, scheduled for launch or prioritized for development in the near future.

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.

Seasonality and Spatial Dependence of Mesoscale and Submesoscale Ocean Currents from Along-Track Satellite Altimetry

Abstract Along-track wavenumber spectral densities of sea surface height (SSH) are estimated from Jason-2 altimetry data as a function of spatial location and calendar month to understand the seasonality of meso- and submesoscale balanced dynamics across the global ocean. Regions with significant mode-1 and mode-2 baroclinic tides are rejected, restricting the analysis to the extratropics. Where balanced motion dominates, the SSH spectral density is averaged over all pass segments in a region for each calendar month and is fit to a four-parameter model consisting of a flat plateau at low wavenumbers, a transition at wavenumber k0 to a red power law spectrum k−s, and a white spectrum at high wavenumbers that models the altimeter noise. The monthly time series of the model parameters are compared to the evolution of the mixed layer. The annual mode of the spectral slope s reaches a minimum after the mixed layer deepens, and the annual mode of the bandpassed kinetic energy in the ranges [2k0, 4k0] and [k0, 2k0] peak ∼2 and ∼4 months, respectively, after the maximum of the annual mode of the mixed layer depth. This analysis is consistent with an energization of the submesoscale by a winter mixed layer instability followed by an inverse cascade of kinetic energy to the mesoscale, in agreement with prior modeling studies and in situ measurements. These results are compared to prior modeling, in situ, and satellite investigations of specific regions and are broadly consistent with them within measurement uncertainties. Significance Statement This paper uses satellite observations to understand the source of ocean dynamics at the 1–100-km scales at which vertical motion becomes important and which are thus relevant for biology and for the exchange of heat and carbon with the atmosphere. The observations are consistent with a seasonal variation of dynamics at these scales, predicted by a specific theory of upper-ocean turbulence and confirmed by modeling studies and regional observations. We update prior satellite-based studies by excluding regions with competing effects, by our treatment of the noise, and by our characterization of the seasonality. This work provides a template for analyzing data from the upcoming Surface Water and Ocean Topography (SWOT) satellite.

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.

Engaging Potential Users of the Planned Surface Water and Ocean Topography (SWOT) Mission in the Year of Launch

Engaging Potential Users of the Planned Surface Water and Ocean Topography (SWOT) Mission in the Year of Launch

Cloud-based framework for inter-comparing submesoscale-permitting realistic ocean models

Abstract. With the increase in computational power, ocean models with kilometer-scale resolution have emerged over the last decade. These models have been used for quantifying the energetic exchanges between spatial scales, informing the design of eddy parametrizations, and preparing observing networks. The increase in resolution, however, has drastically increased the size of model outputs, making it difficult to transfer and analyze the data. It remains, nonetheless, of primary importance to assess more systematically the realism of these models. Here, we showcase a cloud-based analysis framework proposed by the Pangeo project that aims to tackle such distribution and analysis challenges. We analyze the output of eight submesoscale-permitting simulations, all on the cloud, for a crossover region of the upcoming Surface Water and Ocean Topography (SWOT) altimeter mission near the Gulf Stream separation. The cloud-based analysis framework (i) minimizes the cost of duplicating and storing ghost copies of data and (ii) allows for seamless sharing of analysis results amongst collaborators. We describe the framework and provide example analyses (e.g., sea-surface height variability, submesoscale vertical buoyancy fluxes, and comparison to predictions from the mixed-layer instability parametrization). Basin- to global-scale, submesoscale-permitting models are still at their early stage of development; their cost and carbon footprints are also rather large. It would, therefore, benefit the community to document the different model configurations for future best practices. We also argue that an emphasis on data analysis strategies would be crucial for improving the models themselves.

Deriving River Discharge Using Remotely Sensed Water Surface Characteristics and Satellite Altimetry in the Mississippi River Basin

River discharges are critical for understanding hydrologic and ecological systems, yet in situ data are limited in many regions of the world. While approximating river discharge using satellite-derived water surface characteristics is possible, the key challenges are unknown channel bathymetry and roughness. Here, we present an application for merging mean river-reach characteristics and time-varying altimetry measurements to estimate river discharge for sites within the Mississippi River Basin (USA). This project leverages the Surface Water and Ocean Topography (SWOT) River Database (SWORD) for approximating mean river-reach widths and slopes and altimetry data from JASON-2/3 (2008–Present) and Sentinel-3A/B (2015–Present) obtained from the Hydroweb Theia virtual stations. River discharge is calculated using Manning’s Equation, with optimized parameters for surface roughness, bottom elevation, and channel shape determined using the Kling–Gupta Efficiency (KGE). The results of this study indicate the use of optimized characteristics return 87% of sites with KGE &gt; −0.41, which indicates that the approach provides discharges that outperform using the mean discharge. The use of precipitation to approximate missing flows not observed by satellites results in 66% of sites with KGE &gt; −0.41, while the use of TWSA results in 65% of sites with KGE &gt; −0.41. Future research will focus on extending this application for all available sites in the United States, as well as trying to understand how climate and landscape factors (e.g., precipitation, temperature, soil moisture, landcover) relate to river and watershed characteristics.

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.

Additive Manufacturing of Compliant Mechanisms for Deployable Aerospace Structures

In the past 10 years, complex deployable structures have become common on CubeSats and large-scale spacecraft. As new missions are pursued, there is an increased need for more mass and volume efficient deployments. Over the same period, metal additive manufacturing (AM) has enabled new forms of spaceflight hardware. However, AM of compliant mechanisms has not been fully leveraged for deployable aerospace structures. The Surface Water Ocean Topography and NASA-ISRO Synthetic Aperture Radar mission missions launching in 2022 both utilize large deployable masts. Each mast deployment is driven by numerous spring and damper mechanisms. Because of volume constraints, the spring mechanisms designed utilize high aspect ratio rectangular cross section torsion springs that represent the state of the art of manufacturing. This extreme spring design resulted in manufacturing difficulties and hardware failures during ground mechanism testing. Upon re-examining the mechanism design, AM enables torque performance, mass, and complexity improvements. AM allows for torsion spring cross sections not otherwise possible with traditional spring manufacturing methods. Prototype springs of various cross sections were printed in maraging steel and tested. Results confirmed design analysis, and doubling of the spring constant was achieved when compared to the traditional springs. The use of AM also allows springs to be built monolithically with surrounding structure. Design, manufacturing, and test findings will be discussed along with future implications for deployable aerospace structures.

Sparse Approximation of the Precision Matrices for the Wide-Swath Altimeters

The upcoming technology of wide-swath altimetry from space will deliver a large volume of data on the ocean surface at unprecedentedly high spatial resolution. These data are contaminated by errors caused by the uncertainties in the geometry and orientation of the on-board interferometer and environmental conditions, such as sea surface roughness and atmospheric state. Being highly correlated along and across the swath, these errors present a certain challenge for accurate processing in operational data assimilation centers. In particular, the error covariance matrix R of the Surface Water and Ocean Topography (SWOT) mission may contain trillions of elements for a transoceanic swath segment at kilometer resolution, and this makes its handling a computationally prohibitive task. Analysis presented here shows, however, that the SWOT precision matrix R−1 and its symmetric square root can be efficiently approximated by a sparse block-diagonal matrix within an accuracy of a few per cent. A series of observational system experiments with simulated data shows that such approximation comes at the expense of a relatively minor reduction in the assimilation accuracy, and, therefore, could be useful in operational systems targeted at the retrieval of submesoscale variability of the ocean surface.

Reimagining the Surface Water and Ocean Topography Mission as the “Landsat” of Surface Water [Perspective

The Surface Water Ocean Topography (SWOT) mission, jointly developed by NASA and French Space Agency (CNES) with contributions from the Canadian and U.K. space agencies, and planned for launch in 2022, is designed to provide a spatially distributed and high-frequency measurement of water elevation data for the hydrology and oceanography communities for the first time <xref ref-type="bibr" rid="ref1" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">[1]</xref> , <xref ref-type="bibr" rid="ref2" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">[2]</xref> . By virtue of its novel observational capability and stated scientific goals, SWOT satellite data are expected to have a profound impact on our understanding of global surface water. Although there have been many satellite missions that can either map water extent or water elevation, SWOT is the first mission that will measure extent and elevation concurrently regardless of cloud cover conditions and with a higher degree of precision.

Characterization of Internal Tide Incoherence: Eulerian versus Lagrangian Perspectives

Abstract The Lagrangian and Eulerian surface current signatures of a low-mode internal tide propagating through a turbulent balanced flow are compared in idealized numerical simulations. Lagrangian and Eulerian total (i.e., coherent plus incoherent) tidal amplitudes are found to be similar. Compared to Eulerian diagnostics, the Lagrangian tidal signal is more incoherent with comparable or smaller incoherence time scales and larger incoherent amplitudes. The larger level of incoherence in Lagrangian data is proposed to result from the deformation of an Eulerian internal tide signal induced by drifter displacements. Based on the latter hypothesis, a theoretical model successfully predicts Lagrangian autocovariances by relating Lagrangian and Eulerian autocovariances and the properties of the internal tides and jet. These results have implications for the separation of balanced flow and internal tides signals in the sea level data collected by the future Surface Water and Ocean Topography (SWOT) satellite mission.

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.

A new robust discharge estimation method applied in the context of SWOT satellite data processing

Knowing river discharge is vital for monitoring the fresh water cycle at the global scale. The Surface Water and Ocean Topography (SWOT) satellite mission will map river water surface elevations and inundated areas for rivers wider than one hundred meters worldwide. These observations can be used for estimating the river discharge. It is the global coverage which makes these observations particularly valuable, however in many cases there will be no data on the bathymetry and river bed properties available. That is why the problem of discharge estimation using solely the SWOT-type observations has received a noticeable attention recently.The attempts to solve this problem have expectedly confirmed that it is highly ill-posed and, therefore, additional data is useful. In particular, the use of the mean discharge estimates retrieved from the global scale hydrological databases (e.g. the Global Water Balance Model) have been accepted. However, taking into account the accuracy of such estimates and the issue of their relevance to the current time period, the problem is still posing a serious challenge. For example, in the results obtained by different methods reported up to date, the estimated hydrograph may suffer from a significant bias, which often makes a major contribution to the total estimation error.In this paper a new estimation method is suggested, which is specially designed to reduce the solution bias. The concept of this method is similar to the one of the Variational Expectation–Maximization method, however its perception and implementation are original and problem-oriented. In our method, the mean values of the unknown variables are obtained using the Bayesian estimator, whereas the ‘shape’ functions are updated using the variational data assimilation or generated directly using the inverted simplified hydraulic model. The two steps constitute an estimation cycle, which can be repeated after information exchange.The method has been validated using two available testing sets including 51 cases in total. It has demonstrated a much better robustness and reliability than the variational data assimilation method, and quite a promising performance in terms of accuracy. Since the proof of the concept was in the focus of this study, the issue of computational feasibility was not a priority. Nevertheless, the method in its current form can be applied at local/regional or basin scales. For a possible global scale application, a generalized discharge estimator is suggested, where the major computational burden falls on the ‘learning’ stage, which is separated from the discharge prediction algorithm.

On the Development of SWOT In Situ Calibration/Validation for Short-Wavelength Ocean Topography

Abstract The future Surface Water and Ocean Topography (SWOT) mission aims to map sea surface height (SSH) in wide swaths with an unprecedented spatial resolution and subcentimeter accuracy. The instrument performance needs to be verified using independent measurements in a process known as calibration and validation (Cal/Val). The SWOT Cal/Val needs in situ measurements that can make synoptic observations of SSH field over an O(100) km distance with an accuracy matching the SWOT requirements specified in terms of the along-track wavenumber spectrum of SSH error. No existing in situ observing system has been demonstrated to meet this challenge. A field campaign was conducted during September 2019–January 2020 to assess the potential of various instruments and platforms to meet the SWOT Cal/Val requirement. These instruments include two GPS buoys, two bottom pressure recorders (BPR), three moorings with fixed conductivity–temperature–depth (CTD) and CTD profilers, and a glider. The observations demonstrated that 1) the SSH (hydrostatic) equation can be closed with 1–3 cm RMS residual using BPR, CTD mooring and GPS SSH, and 2) using the upper-ocean steric height derived from CTD moorings enable subcentimeter accuracy in the California Current region during the 2019/20 winter. Given that the three moorings are separated at 10–20–30 km distance, the observations provide valuable information about the small-scale SSH variability associated with the ocean circulation at frequencies ranging from hourly to monthly in the region. The combined analysis sheds light on the design of the SWOT mission postlaunch Cal/Val field campaign.

Towards improved analysis of short mesoscale sea level signals from satellite altimetry

Abstract. Satellite altimeters routinely supply sea surface height (SSH) measurements, which are key observations for monitoring ocean dynamics. However, below a wavelength of about 70 km, along-track altimeter measurements are often characterized by a dramatic drop in signal-to-noise ratio (SNR), making it very challenging to fully exploit the available altimeter observations to precisely analyze small mesoscale variations in SSH. Although various approaches have been proposed and applied to identify and filter noise from measurements, no distinct methodology has emerged for systematic application in operational products. To best address this unresolved issue, the Copernicus Marine Environment Monitoring Service (CMEMS) actually provides simple band-pass filtered data to mitigate noise contamination of along-track SSH signals. More innovative and suitable noise filtering methods are thus left to users seeking to unveil small-scale altimeter signals. As demonstrated here, a fully data-driven approach is developed and applied successfully to provide robust estimates of noise-free sea level anomaly (SLA) signals (Quilfen, 2021). The method combines empirical mode decomposition (EMD), used to help analyze non-stationary and non-linear processes, and an adaptive noise filtering technique inspired by discrete wavelet transform (DWT) decompositions. It is found to best resolve the distribution of SLA variability in the 30–120 km mesoscale wavelength band. A practical uncertainty variable is attached to the denoised SLA estimates that accounts for errors related to the local SNR but also for uncertainties in the denoising process, which assumes that the SLA variability results in part from a stochastic process. For the available period, measurements from the Jason-3, Sentinel-3, and SARAL/AltiKa missions are processed and analyzed, and their energy spectral and seasonal distributions are characterized in the small mesoscale domain. In anticipation of the upcoming SWOT (Surface Water and Ocean Topography) mission data, the SASSA (Satellite Altimeter Short-scale Signals Analysis, https://doi.org/10.12770/1126742b-a5da-4fe2-b687-e64d585e138c, Quilfen and Piolle, 2021) data set of denoised SLA measurements for three reference altimeter missions has already been shown to yield valuable opportunities to evaluate global small mesoscale kinetic energy distributions.

Analytical Models for Multipath and Switch Leakage for the SWOT Interferometer.

The Ka-Band Radar Interferometer (KaRIn) instrument on the Surface Water and Ocean Topography (SWOT) mission is a single-pass synthetic aperture radar (SAR) interferometer tasked with, among others, measuring ocean topography to within a few centimeters over kilometer scale resolutions. A SAR interferometer relies on very precise phase difference measurements between two spatially distant antennas to estimate topography. Multipath phase caused by unintended scattering off the spacecraft structure is a known error source for radar interferometers and takes up a significant portion of the KaRIn error budget. This paper outlines some analytical multipath models that were used for instrument design, performance analysis and mitigation of the multipath signal.

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 %.

Reconstructing Fine‐Scale Ocean Variability via Data Assimilation of the SWOT Pre‐Launch In Situ Observing System

AbstractAfter the surface water and ocean topography (SWOT) satellite launches in 2022, an in‐situ field campaign will be conducted to estimate the ocean state for calibration and validation (CalVal) purposes. It is demonstrably difficult to capture the sea surface height (SSH) features that are the focus of SWOT, with short time periods (&lt;20 days) and fine‐scale spatial structures (15–150 km). Therefore, a critical component of the SWOT CalVal will be a data assimilation (DA) system coupled to a primitive equation numerical model that can estimate: (a) the 2D SSH over the SWOT swaths, and (b) the 3D dynamical (velocity) field. To explore the ability of DA to meet the challenges of SWOT fine‐scale observations, a multiscale DA system based on an extended 3D variational method has been developed. Here, we present a strategic evaluation of this DA system, with a focus to assimilate in‐situ data from an observing system to best represent the fine‐scale ocean variability at the CalVal site. The DA estimate is compared to independent observations taken during the 2019 pre‐launch field campaign 300‐km off Monterey Bay, California. The key result is this DA system can reconstruct the upper 500‐m steric height with O(1‐cm) error at hourly resolution, and subcentimeter error for periods longer than 2 days.