The Surface Water and Ocean Topography Mission (SWOT): the Ka-band Radar Interferometer (KaRIn) for water level measurements at all scales

Rodríguez, Ernesto; Esteban-Fernández, Daniel

doi:10.1117/12.868525

Cited by 19 publications

(9 citation statements)

References 9 publications

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“…Figure 7A shows examples of SSH fields in the Nature Run and the spatial coverage of simulated satellite altimetry data for conventional nadir and SWOT altimeter missions. Only the Ka-Band Radar Interferometer (Karin) noise in accordance with the error budget of SWOT mission concept (e.g., Durand et al, 2010;Rodriguez and Esteban-Fernandez, 2010). With 7 × 7 km spatial resolution, the noise ranges from 0.2 cm RMS in the inner part of the swath to about 0.35 cm on the outer edges of the swath.…”

Section: Evaluation Of Impact Of Wide-swath Altimetry Missionsmentioning

confidence: 94%

Observing System Evaluation Based on Ocean Data Assimilation and Prediction Systems: On-Going Challenges and a Future Vision for Designing and Supporting Ocean Observational Networks

et al. 2019

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This paper summarizes recent efforts on Observing System Evaluation (OS-Eval) by the Ocean Data Assimilation and Prediction (ODAP) communities such as GODAE OceanView and CLIVAR-GSOP. It provides some examples of existing OS-Eval methodologies, and attempts to discuss the potential and limitation of the existing approaches. Observing System Experiment (OSE) studies illustrate the impacts of the severe decrease in the number of TAO buoys during 2012-2014 and TRITON buoys since 2013 on ODAP system performance. Multi-system evaluation of the impacts of assimilating satellite sea surface salinity data based on OSEs has been performed to demonstrate the need to continue and enhance satellite salinity missions. Impacts of underwater gliders have been assessed using Observing System Simulation Experiments (OSSEs) to provide guidance on the effective coordination of the western North Atlantic observing system elements. OSSEs are also being performed under H2020 AtlantOS Fujii et al.Observing System Evaluation project with the goal to enhance and optimize the Atlantic in-situ networks. Potential of future satellite missions of wide-swath altimetry and surface ocean currents monitoring is explored through OSSEs and evaluation of Degrees of Freedom for Signal (DFS). Forecast Sensitivity Observation Impacts (FSOI) are routinely evaluated for monitoring the ocean observation impacts in the US Navy's ODAP system. Perspectives on the extension of OS-Eval to coastal regions, the deep ocean, polar regions, coupled data assimilation, and biogeochemical applications are also presented. Based on the examples above, we identify the limitations of OS-Eval, indicating that the most significant limitation is reduction of robustness and reliability of the results due to their system-dependency. The difficulty of performing evaluation in near real time is also critical. A strategy to mitigate the limitation and to strengthen the impact of evaluations is discussed. In particular, we emphasize the importance of collaboration within the ODAP community for multi-system evaluation and of communication with ocean observational communities on the design of OS-Eval, required resources, and effective distribution of the results. Finally, we recommend further developing OS-Eval activities at international level with the support of the international ODAP (e.g., OceanPredict and CLIVAR-GSOP) and observational communities.

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Section: Evaluation Of Impact Of Wide-swath Altimetry Missionsmentioning

confidence: 94%

Observing System Evaluation Based on Ocean Data Assimilation and Prediction Systems: On-Going Challenges and a Future Vision for Designing and Supporting Ocean Observational Networks

et al. 2019

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“…The future SWOT instrument will provide time series (∼4 − 20 days frequency depending on the location) of WS elevation Z and water extend therefore the river width W , Rodriguez and others (2012); Rodriguez and Esteban-Fernandez (2010). These measurements may be available at different scales: at RefDataSc at the "nodes" location and at SwReachSc.…”

Section: Swot-like Datamentioning

confidence: 99%

“…The future Surface Water and Ocean Topography (SWOT) mission (NASA-CNES et al) planned to launch in 2022 will provide unprecedented Water Surface (WS) measurements of rivers wider than 50 − 100 m. SWOT instrument will measure the WS elevation Z (with a decimetric accuracy over 1 km 2 ) and the WS width W (with a varying uncertainty of a few m, depending of the river plan form). This instrument will cover a great majority of the globe with relatively frequent revisits: from 1 to 4 revisits per 21 days repeat cycle, see Rodriguez and Esteban-Fernandez (2010); Rodriguez and others (2012). Given such WS measurements and in view to set up river flow models, the following inverse problem arises: to estimate the discharge Q(x, t) but also the unobserved 1 https://doi.org/10.5194/npg-2020-32 Preprint.…”

Section: Introductionmentioning

confidence: 99%

Hybrid Neural Network – Variational Data Assimilation algorithm to infer river discharges from SWOT-like data

Monnier

2020

Preprint

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Abstract. A new algorithm to estimate river discharges from altimetry measurements only is designed. A first estimation is obtained by an artificial neural network trained from the altimetry large scale water surface measurements plus drainage area information. The combination of this purely data-based estimation and a dedicated algebraic flow model provides a first physically-consistent estimation. The latter is next employed as the first guess of an advanced variational data assimilation formulation. The final estimation is highly accurate for rivers presenting features within the learning partition; for rivers far outside the learning partition, the space-time variations of discharge remain accurately approximated however the global estimation presents a potential bias. Indeed, it is shown that if the estimation is based on the hydrodynamics models only, the inverse problem may be well-defined but up to a bias only (the bias scales the global estimation). This bias is removed thanks to the ANN but for rivers in the learning partition only. For rivers outside the learning partition, any mean value (eg. annual, seasonal) enables to remove the bias. Finally, the present hybrid and hierarchical inversion strategy seems to provide much more accurate estimations compared to the state-of-the-art for the considered 29 heterogeneous river portions.

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“…A most promising future development is the Surface Water and Ocean Topography Mission, which is scheduled for launch in 2020. It will include a radar altimeter, an interferometer (Ka-Band Radar Interferometer [KaRIN]), and a microwave radiometer (Rodríguez and Estéban-Fernández 2010).…”

Section: Sensors For Surface Water Remote Sensingmentioning

confidence: 99%

Earth Observation for Water Resources Management: Current Use and Future Opportunities for the Water Sector

Pakulski¹,

Wijnen²,

Niel³

et al. 2015

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Some rights reserved 1 2 3 4 18 17 16 15 This work is a product of the staff of The World Bank with external contributions. The findings, interpretations, and conclusions expressed in this work do not necessarily reflect the views of The World Bank, its Board of Executive Directors, or the governments they represent. The World Bank does not guarantee the accuracy of the data included in this work. The boundaries, colors, denominations, and other information shown on any map in this work do not imply any judgment on the part of The World Bank concerning the legal status of any territory or the endorsement or acceptance of such boundaries.Nothing herein shall constitute or be considered to be a limitation upon or waiver of the privileges and immunities of The World Bank, all of which are specifically reserved.

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The Surface Water and Ocean Topography Mission (SWOT): the Ka-band Radar Interferometer (KaRIn) for water level measurements at all scales

Cited by 19 publications

References 9 publications

Observing System Evaluation Based on Ocean Data Assimilation and Prediction Systems: On-Going Challenges and a Future Vision for Designing and Supporting Ocean Observational Networks

Observing System Evaluation Based on Ocean Data Assimilation and Prediction Systems: On-Going Challenges and a Future Vision for Designing and Supporting Ocean Observational Networks

Hybrid Neural Network – Variational Data Assimilation algorithm to infer river discharges from SWOT-like data

Earth Observation for Water Resources Management: Current Use and Future Opportunities for the Water Sector

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