SKIM, a Candidate Satellite Mission Exploring Global Ocean Currents and Waves

Ardhuin, Fabrice; Brandt, Peter; Gaultier, Lucile; Donlon, Craig; Battaglia, Alessandro; Boy, F.; Casal, Tânia; Chapron, Bertrand; Collard, Fabrice; Cravatte, Sophie; Delouis, J.-M.; Witte, E. De; Dibarboure, Gérald; Engen, G.; Johnsen, Harald; Lique, Camille; López‐Dekker, Paco; Maes, Christophe; Martin, Adrien; Marié, Louis; Menemenlis, Dimitris; Nouguier, Frédéric; Peureux, Charles; Rampal, Pierre; Ressler, Gerhard; Rio, Marie-Hélène; Rommen, Björn; Shutler, Jamie D.; Süess, Martin; Tsamados, Michel; Ubelmann, Clément; Sebille, Erik van; Oever, Martin van den; Stammer, Detlef

doi:10.3389/fmars.2019.00209

Cited by 63 publications

(37 citation statements)

References 46 publications

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“…4, the probabilities of the PIA caused by clouds and precipitation exceeding 3 dB are depicted. The 3-dB level is usually considered detrimental to altimeter waveform measurements, whereas even more stringent thresholds must be imposed for the Doppler scatterometers [10]. On the other hand, for altimeters, the instrument signal-to-noise ratios are generally very large (e.g., the data lost due to rain over the ocean are lower than 0.1% for AltiKa [8]).…”

Section: A Global Climatology Of Attenuation Due To Clouds and Precimentioning

confidence: 99%

“…launched in 2014 as a part of the Global Precipitation Measurement (GPM) mission payload [2]; several mission proposals and notional concepts targeting cloud and precipitation and their dynamics [3]- [6], including Cubesat platforms [7], are based on measurements close to such frequency. Some of the altimeters are currently operated at Ka-band (AltiKa at 35.75 GHz [8]), whereas missions targeting ocean currents (e.g., the SKIM mission [9], [10]) do also envisage the use of the Ka-band Doppler radar; similarly, synthetic aperture radars have been proposed at Ka-band [11], [12] and SWOT is a cross-track SAR interferometer targeted at water level measurements [13].…”

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confidence: 99%

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GPM-Derived Climatology of Attenuation Due to Clouds and Precipitation at Ka-Band

Battaglia

Mróz

Watters

et al. 2020

IEEE Trans. Geosci. Remote Sensing

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Attenuation from clouds and precipitation hinders the use of Ka-band in SARs, radar altimeters and in satellite link communications. The NASA-JAXA Global Precipitation Measurement (GPM) mission, with its core satellite payload including a dual-frequency (13.6 and 35.5 GHz) radar and a multifrequency passive microwave radiometer, offers an unprecedented opportunity for better quantifying such attenuation effects. Based on four years of GPM products, this article presents a global climatology of Ka-band attenuation caused by clouds and precipitation and analyses the impact of the precipitation diurnal cycle. As expected, regions of high attenuation mirror precipitation patterns. Clouds and precipitation cause two-way attenuation at 35.5 GHz in excess of 3 dB about 1.5% of the time in the regions below 65 • , peaking at as much as 10% in the tropical rain belt and the South Pacific Convergence Zone and at circa 5% along the storm tracks of the North Atlantic and Pacific Oceans. Confirming previous findings, the diurnal cycle is particularly strong over the land and during the summer period; while over the ocean, the diurnal cycle is generally weaker some coherent features emerge in the tropical oceans and in the northern hemisphere. Results are useful for estimating data loss from (sun-synchronous) satellite adopting active instruments/links at a frequency close to 35 GHz.

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Section: A Global Climatology Of Attenuation Due To Clouds and Precimentioning

confidence: 99%

mentioning

confidence: 99%

GPM-Derived Climatology of Attenuation Due to Clouds and Precipitation at Ka-Band

Battaglia

Mróz

Watters

et al. 2020

IEEE Trans. Geosci. Remote Sensing

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“…Feedback from the user communities and the needs of specific scientific studies (e.g. Ashton et al, 2016) have guided these developments and considerably extended the functionality and range of possible applications.…”

Section: Overview Of Fluxenginementioning

confidence: 99%

“…Wrobel and Piskozub, 2016;Wrobel, 2017;Woolf et al, 2019;Shutler et al, 2019), evaluate the impact of gas transfer processes on regional and global gas exchange (e.g. Ashton et al, 2016;Pereira et al, 2018), evaluate the European shelf sea CO 2 gas fluxes and sinks (Shutler et al, 2016) and investigate biological and physical controls of air-sea exchange (Henson et al, 2018). FluxEngine has also been used to identify shortfalls of current modelling approaches through the inclusion of FluxEngine outputs within an international inter-comparison (Rödenbeck et al, 2015).…”

Section: Overview Of Fluxenginementioning

confidence: 99%

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The FluxEngine air–sea gas flux toolbox: simplified interface and extensions for in situ analyses and multiple sparingly soluble gases

et al. 2019

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Abstract. The flow (flux) of climate-critical gases, such as carbon dioxide (CO2), between the ocean and the atmosphere is a fundamental component of our climate and an important driver of the biogeochemical systems within the oceans. Therefore, the accurate calculation of these air–sea gas fluxes is critical if we are to monitor the oceans and assess the impact that these gases are having on Earth's climate and ecosystems. FluxEngine is an open-source software toolbox that allows users to easily perform calculations of air–sea gas fluxes from model, in situ, and Earth observation data. The original development and verification of the toolbox was described in a previous publication. The toolbox has now been considerably updated to allow for its use as a Python library, to enable simplified installation, to ensure verification of its installation, to enable the handling of multiple sparingly soluble gases, and to enable the greatly expanded functionality for supporting in situ dataset analyses. This new functionality for supporting in situ analyses includes user-defined grids, time periods and projections, the ability to reanalyse in situ CO2 data to a common temperature dataset, and the ability to easily calculate gas fluxes using in situ data from drifting buoys, fixed moorings, and research cruises. Here we describe these new capabilities and demonstrate their application through illustrative case studies. The first case study demonstrates the workflow for accurately calculating CO2 fluxes using in situ data from four research cruises from the Surface Ocean CO2 ATlas (SOCAT) database. The second case study calculates air–sea CO2 fluxes using in situ data from a fixed monitoring station in the Baltic Sea. The third case study focuses on nitrous oxide (N2O) and, through a user-defined gas transfer parameterisation, identifies that biological surfactants in the North Atlantic could suppress individual N2O sea–air gas fluxes by up to 13 %. The fourth and final case study illustrates how a dissipation-based gas transfer parameterisation can be implemented and used. The updated version of the toolbox (version 3) and all documentation is now freely available.

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