OS4.5 | Ocean Remote Sensing
Ocean Remote Sensing
Convener: Aida Alvera-Azcárate | Co-conveners: Craig Donlon, Guoqi Han, Tong Lee, Adrien Martin
Orals
| Fri, 28 Apr, 14:00–15:40 (CEST), 16:15–17:55 (CEST)
 
Room E2
Posters on site
| Attendance Fri, 28 Apr, 10:45–12:30 (CEST)
 
Hall X5
Posters virtual
| Attendance Fri, 28 Apr, 10:45–12:30 (CEST)
 
vHall CR/OS
Orals |
Fri, 14:00
Fri, 10:45
Fri, 10:45
Advanced remote sensing capabilities have provided unprecedented opportunities for monitoring and studying the ocean environment as well as improving ocean and climate predictions. Synthesis of remote sensing data with in situ measurements and ocean models have further enhanced the values of oceanic remote sensing measurements. This session provides a forum for interdisciplinary discussions of the latest advances in oceanographic remote sensing and the related applications and to promote collaborations.

We welcome contributions on all aspects of the oceanic remote sensing and the related applications. Topics for this session include but are not limited to: physical oceanography, marine biology and biogeochemistry, biophysical interaction, marine gravity and space geodesy, linkages of the ocean with the atmosphere, cryosphere, and hydrology, new instruments and techniques in ocean remote sensing, new mission concepts, development and evaluation of remote sensing products of the ocean, and improvements of models and forecasts using remote sensing data. Applications of multi-sensor observations to study ocean and climate processes and applications using international (virtual) constellations of satellites are particularly welcome.

Orals: Fri, 28 Apr | Room E2

Chairpersons: Tong Lee, Adrien Martin, Aida Alvera-Azcárate
14:00–14:20
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EGU23-17156
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solicited
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Highlight
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On-site presentation
Lee-Lueng Fu, Tamlin Pavelsky, Rosemary Morrow, Jean-Francois Cretaux, and Tom Farrar

SWOT is a pathfinder mission using new technology to address transformative questions on energy and water of the Earth System in a warming climate.  The excess heat energy entering the Earth system as a result of the greenhouse effect is largely stored in water, and changes in the water cycle and water resources have profound effects on life on Earth.            

 

SWOT is a next generation radar altimeter that uses synthetic aperture radar interferometry to measure the elevation of water surface over both continents and oceans in two dimensions with a radar footprint 1000 times smaller than that of a conventional altimeter. SWOT will cover the world between 78N and 78S every 21 days, leaving only small gaps comprising <5% of Earth’s surface. 

 

More than 90% of the heat from global warming since the industrial revolution has been absorbed and stored in the ocean.  A major part of this process takes place in the ocean on scales too small to be observed from space in the past.  SWOT will improve the two-dimensional spatial resolution of sea surface height from present 200 km to 20 km to address the processes of heat uptake from the atmosphere.

 

In a warming climate earth’s water cycle is accelerating, making it difficult to track and manage water resources as well as predicting floods and droughts.  The areal extent of surface water on land can be observed by conventional spaceborne sensors, but the volume of surface water in lakes and rivers will be surveyed by SWOT from space for the first time.  The numbers of rivers and lakes to be surveyed by SWOT are orders of magnitude more than the present observations.

 

The high-resolution data of SWOT near the coasts will allow us to study sea level variations in unprecedented detail. Storm surge and other impacts like salt water intrusion and river diversion will be exacerbated by the continuing sea level rise.  SWOT data will help improve models to monitor and forecast these impacts.

 

After nearly 20 years’ development, SWOT was launched on December 16, 2022 as a joint mission of NASA and the French Space Agency, CNES, with contributions from the Canadian Space Agency and the UK Space Agency. The satellite system was fully deployed within a week of launch and is in a 3-month phase of engineering checkout.  A 3-month calibration and validation phase will start afterwards in the one-day repeat initial orbit, which will transition into a 21-day repeat orbit during the science phase of the mission in mid 2023.  The release of SWOT data to the public for evaluation is expected to take place 10 months after launch.  The status of the mission at the end of April will be reported by this presentation.

How to cite: Fu, L.-L., Pavelsky, T., Morrow, R., Cretaux, J.-F., and Farrar, T.: The SWOT (Surface Water and Ocean Topography) Mission and Its Status, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-17156, https://doi.org/10.5194/egusphere-egu23-17156, 2023.

14:20–14:30
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EGU23-12743
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Virtual presentation
David McCann, Adrien Martin, Karlus Macedo, Christine Gommenginger, Louis Marié, Ruben Carrasco Alvarez, Adriano Meta, Petronilo Martin Iglesias, and Tania Casal

OSCAR (Ocean Surface Current Airborne Radar) is a new airborne instrument which provides unique 2D synoptic views of ocean and atmosphere dynamics (currents, waves, winds) below km-scale. OSCAR is a Ku-band (13.5 GHz) SAR system with Doppler and scatterometry capabilities in three azimuth look directions. The OSCAR instrument features an along-track interferometric (ATI) baseline in two lines-of-sight squinted 45° fore and aft from the broadside direction. The fore and aft antenna pairs provide interferometric Doppler measurements in two views angularly separated by 90 degrees. This ensures two orthogonal measurements of the ocean surface motion velocity that enable the retrieval of the total ocean surface current vector. In addition, backscatter measurements from the broadside antenna in the zero-Doppler direction serve to retrieve wind direction and wind speed, which are critical to correctly measure total ocean surface currents.

In each line-of-sight, the ocean surface motion sensed by the microwave radar (after correcting for navigation and geometry) has two constituents: the total ocean surface current – consisting of all currents contributing to actual horizontal transport of water – and a measurement bias associated with the Doppler signature of the surface scatterers responsible for the backscatter, a term known as Doppler wave bias or Wind-wave induced Artefact Surface Velocity — WASV (Martin et al., 2016). The WASV is caused by the phase velocity of the surface scatterers responsible for the microwave backscatter (e.g. Bragg waves) and the effect of the orbital motion of longer ocean waves. The magnitude of the WASV can reach 0.5-1 m/s and is, at first order, a function of the wind direction. A number of geophysical model functions (GMFs) have been published in recent years to correct this effect.

In May 2022, OSCAR was flown during the SEASTARex campaign over Iroise Sea (French Brittany). The campaign consisted of three flights on three different days, including acquisitions over a well-instrumented site with ground truth measurements of total ocean surface current fields from a WERA HF radar, supported by data from an X-band marine radar, stereo-video and a down-looking ADCP. Here, we present the first results of the validation of the OSCAR retrieved current fields against data from independent ground truth sensors and models.

How to cite: McCann, D., Martin, A., Macedo, K., Gommenginger, C., Marié, L., Carrasco Alvarez, R., Meta, A., Martin Iglesias, P., and Casal, T.: OSCAR: validation of 2D total surface current vector fields during the SEASTARex airborne campaign in Iroise Sea, May 2022., EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-12743, https://doi.org/10.5194/egusphere-egu23-12743, 2023.

14:30–14:40
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EGU23-14595
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ECS
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Virtual presentation
Anis Elyouncha and Leif Eriksson

Synthetic aperture radar (SAR) offers the possibility to observe the sea surface circulation with very high spatial resolution. These observations are particularly relevant in coastal areas and shelf seas. SAR has been routinely providing valuable information on sea surface winds and waves for many decades. During the last decade, a new application of SAR measurements based on the analysis of the Doppler shift has emerged, opening the possibility to measure directly the surface currents. There are still however many unresolved questions and challenges. One of the challenging questions is the wave-current interaction and its effect on the wind and wave retrieval. 

The Agulhas Current is the strongest western boundary currents in the southern hemisphere. The region of the Agulhas Current is characterized by a complex upper ocean dynamics involving a wide range of mesoscale and submesoscale processes. It thus provides an ideal natural laboratory for oceanographers and remote sensing sensors and techniques. 

Unique acquisitions of the interferometric SAR TanDEM-X over the Agulhas Current with very high spatial resolution (100 - 200 m) are analyzed. Maps of SAR-derived surface velocity are compared to model data. The SAR velocity images accurately capture the boundary and the intensity of the Agulhas Current. Moreover, these maps show unprecedented fine structure of the Agulhas Current and its interaction with the wave field. The pattern depicted by the backscatter images is on the other hand very variable from scene to scene depending on the wind and sea state. Only in particular cases, the current structure can be discerned from the backscatter. The influence of the Agulhas Current on the wind and wave field retrieval is investigated. The inversion of the backscatter to wind speed without taking the current into account lead artificially high estimates of SAR-derived wind speeds. Note that the wind and wave field retrieval will also impact the current retrieval via the wave-induced Doppler shift. 

These SAR observations are analyzed together with collocated existing products of ocean surface wind, ocean surface current, sea surface temperature and significant wave height. Our analysis indicates that wave-current interactions in regions of strong current shear produce very different signatures of sea surface roughness. The roughness signatures depend on the wave propagation direction relative to the current. A case of particularly enhanced roughness, probably due wave breaking events, is discussed.

How to cite: Elyouncha, A. and Eriksson, L.: Observations of the Agulhas Current by along-track interferometric synthetic aperture radar, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-14595, https://doi.org/10.5194/egusphere-egu23-14595, 2023.

14:40–14:50
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EGU23-10701
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ECS
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On-site presentation
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Shaun Eisner, James Carton, Deirdre Byrne, Semyon Grodsky, and Eric Leuliette

We introduce the new satellite-derived daily global mesoscale (1/6th degree) Blended Ocean Surface Currents (BOSC) product, which will be available in quasi-realtime from
NOAA CoastWatch. Existing observation-based surface current products synthesize geostrophic currents derived from satellite altimetry with an Ekman drift component
derived from surface wind stresses. BOSC builds upon this traditional approach by including additional observation-derived effects such as Stokes Drift, mesoscale advection, and high latitude surface currents inferred from Sea Ice drift. Additionally, BOSC incorporates satellite SST imagery to derive surface motion from the advection of SST features, in a process known as “feature-tracking”. These additional observations provide further constraint of surface currents in tropical, polar, and coastal regions where satellite-derived geostrophic and Ekman currents tend to provide less accurate estimates. Additionally, we offer a comparison of BOSC to existing observation-based surface current products as well as to multiple independent in situ datasets.

How to cite: Eisner, S., Carton, J., Byrne, D., Grodsky, S., and Leuliette, E.: The Development of a New Daily Global Mesoscale Blended Ocean Surface Currents (BOSC) Product, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-10701, https://doi.org/10.5194/egusphere-egu23-10701, 2023.

14:50–15:00
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EGU23-8159
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On-site presentation
Charles-Antoine Guérin, Virginie Capelle, and Jean-Michel Hartmann

The sea-surface slope probability is an important physical parameter to describe the ocean surface and its interaction with the atmosphere. Its reference measurement dates back to the celebrated airborne experiment conducted by Cox and Munk (CM) in 1951 using sun glitter patterns. The obtained two-dimensional slope distribution function deviates slightly from the Gaussian distribution with pronounced up-to-crosswind and up-to-downwind asymmetries, a stronger peakedness and a slower decay at large values. It is classically parametrized by a Gram-Charlier representation with seven directional parameters describing the mean square slopes (MSSs) as well as the skewness and kurtosis coefficients for wind speeds up to 15 m/s. The MSSs are shown to follow a quasi-linear trend with wind speed, a result which has been confirmed by many subsequent airborne and spaceborne optical measurements and wave-tank experiments. The higher-order statistical coefficients have a non trivial dependence on wind speed as shown by the more recent results by Bréon and Henriot (2006); however, they are challenging to evaluate accurately and suffer from a larger uncertainty.

We re-examine the sea-surface probability by using radiances collected from space by the Infrared Atmospheric Sounder Interferometer (IASI) when looking down at ocean surface during the day. This is achieved by using about 300 channels between 3.6 and 4.0 μm and a physically-based approach which properly takes the contribution of the reflected solar radiation into account. This unique data set covers 13 years of observations over the world ocean, resulting in about 150 millions IASI appropriate spectra and as many wave-slope probabilities. Based on these experimental wave-slopes we revisit and discuss CM results and methodology and their limitations. We propose an original and robust approach for accurate retrievals of the Gram-Charlier parameters. Our findings for the MSSs are fully compatible with those of CM but our lower uncertainties enable to point out departures from the linear wind-speed dependencies and a slight overestimation of the upwind MSS described by the linear fit of CM at moderate wind speed. Our skewness and kurtosis coefficients show clear influences of the wind speed, with a steady decrease of the former and the alongwind kurtosis coefficient being maximal at moderate wind speeds, features that CM could not point out due to the limitations of their measurements. We revisit the renormalization procedure employed by CM to obtain the complete variances from truncated pdfs and show that it imposes stringent conditions on the kurtosis coefficients that allow to determine them accurately. We also provide measurements of the shifted position of the most probable slope as well as a demonstration of a qualitative change of regime in the updown wind asymmetry of the wave-slope probability when the wind speed increases.

How to cite: Guérin, C.-A., Capelle, V., and Hartmann, J.-M.: Determining the wave-slope statistics using IASI observations of the sea surface, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-8159, https://doi.org/10.5194/egusphere-egu23-8159, 2023.

15:00–15:10
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EGU23-9894
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Virtual presentation
Ben Timmermans, Christine Gommenginger, and Chris Banks

The growing satellite record of sea state observations is becoming increasingly important for climate change research, to improve ocean and weather forecasts and to inform climate change mitigation and investment strategies. In this context, coastal processes and impacts are of particular concern, driving a yet stronger research imperative. The Copernicus Sentinel-6 Michael Freilich (S6-MF) mission was launched in November 2020 by the European Space Agency to succeed Jason-3 (J3) as the long term satellite altimetry reference mission. S6-MF commissioning involved a unique 12-month Tandem Experiment during which S6-MF flew approximately 30 seconds behind J3 on the same ground tracks, resulting in an unprecedented global dataset of quasi-simultaneous collocated altimeter sea state measurements in Low-Resolution Mode (LRM) and Synthetic Aperture Radar (SAR) mode.

In this work, this unique dataset is examined to evaluate uncertainties in altimeter significant wave height (Hs) observations from the two missions in different operating modes and different sea state conditions. A particular focus is placed on the evaluation of uncertainties in the coastal zone by exploiting the large number of moored buoys located near the coast of the U.S. S6-MF and J3 data are compared with buoy measurements and reanalysis data using, amongst other methods, triple collocation (TC) analysis. Attention is paid to both the collocation methodology and possible correlation of random errors. Results indicate that, over both global and coastal oceans, J3 and S6-MF Low-Resolution Hs are almost identical, with near-zero bias, low RMS difference and very high correlation. This very high correlation precludes the use of triple collocation to the J3/S6-MF-SAR/buoy triplets. Comparing S6-MF SAR with J3 LRM and buoys confirms the positive sea-state dependent bias in SAR Hs. Triple collocation of J3, S6-MF and buoys reveals the sensitivity of measurement uncertainty to collocation criteria, particular in coastal areas. Further, we show how sea state dependence of measurement uncertainty varies between oceanic and coastal settings. In general, we find that steeper spatial gradients of sea state typically associated with coastal regions can hamper interpretation of TC analyses without undue consideration. These findings demonstrate the value of the Tandem Experiment to evaluate uncertainty and provide evidence of the stability and/or enhancements of new mission data contributing to the growing satellite climate record.

How to cite: Timmermans, B., Gommenginger, C., and Banks, C.: Coastal Sea State Uncertainty From a Triple Collocation Analysis of Observations During the Sentinel-6 Michael Freilich – Jason-3 Tandem Phase Experiment, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-9894, https://doi.org/10.5194/egusphere-egu23-9894, 2023.

15:10–15:20
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EGU23-12294
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ECS
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On-site presentation
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Oscar Vergara, Gérald Dibarboure, Anaëlle Treboutte, Yannice Faugère, and François Boy

The soon-to-be available swath altimetry observations from the SWOT mission are expected to drastically improve our capacity to observe fine-scale ocean processes (wavelengths shorter than 100 km). With its increased observing capacity and lower signal-to-noise (SNR) ratio in comparison to conventional nadir altimetry, this new technology is expected to deliver unprecedented high-resolution two-dimensional observations of the ocean surface circulation and surface water bodies. Benefiting from the technological maturity acquired during the preparation for SWOT, a new constellation concept composed of swath altimeters has been proposed to carry on the European operational observing system towards the end of the decade. In the present work, we focus on evaluating the ocean observing capabilities of a novel swath altimeter concept (WiSA – Wide Swath Altimeter). Using the observed surface wave field (SWH – Significant Wave Height) and the instrumental characteristics, we compute global estimates of the SNR. We observe an average global observability around 40 km wavelength over 50% of the global ocean, and 47 km on average over 80% of the globe. Little or no seasonality is observed in the SNR, related to the seasonal compensation of the two competing factors that contribute to the observability, namely the instrumental noise levels and the observed spectral slopes. The performance of recently developed data-driven filtering techniques is also evaluated, considerably increasing the purely instrumental observing capabilities. The results are also discussed from an operational perspective, considering the contribution of a constellation of swath altimeters over a mono-satellite mission.

How to cite: Vergara, O., Dibarboure, G., Treboutte, A., Faugère, Y., and Boy, F.: Detection capabilities of a multi-satellite wide-swath altimetry conceptual mission, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-12294, https://doi.org/10.5194/egusphere-egu23-12294, 2023.

15:20–15:30
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EGU23-3755
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On-site presentation
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Oleg Melnichenko, Peter Hacker, James Potemra, Thomas Meissner, and Frank Wentz

We introduce a new version of the multi-mission sea surface salinity (SSS) optimum interpolation analysis (OISSS) which combines observations from NASA’s AQUARIUS/SAC-D and SMAP (Soil Moisture Active-Passive) satellite missions into continuous and consistent SSS data record. The dataset covers the period from September 2011 to present. Measurements from ESA’s SMOS (Soil Moisture and Ocean Salinity) satellite are used to fill gaps in SMAP observations during June-July 2019 and August-September 2022, when the SMAP satellite was in a safe mode and did not deliver scientific data. The analysis is based on Optimum Interpolation (OI), utilizes Level-2 (swath) data, and uses satellite-specific bias-correction algorithms to correct the satellite retrievals for large-scale biases.  The dataset includes uncertainty estimates, both formal and empirical. We use this dataset as an example to discuss requirements for the multi-mission SSS data products.

To demonstrate its utility, the new dataset is used to characterize spatial patterns of SSS variability in the global ocean and on different time scales. The spatial pattern of the regional SSS trends show that the subtropical North Pacific is becoming fresher while the subtropical South Indian Ocean is becoming saltier. This is seemingly a part of a longer term oscillation as the trends are reversed compared to the preceding decade (2005-2015) estimated from Argo data. In particular, abrupt changes occurred during 2015, related, presumably, to a strong El Nino event of 2015-2016. The annual cycle is a dominant signal globally and can nicely be described by two leading empirical orthogonal functions (EOFs) explaining more than 35% of the total SSS variance. Except for the Indian Ocean, the oscillations are out of phase in the Northern and Southern Hemispheres and describe poleward propagation away from the Equator driven, presumably, by Ekman dynamics. The intra-seasonal signal is strongest in the tropics, particularly in the quasi-zonal bands associated with the Inter-tropical convergence zone (ITCZ) and South Pacific convergence zone (SPCZ), but also near outflows of major rivers, including the Amazon, Congo, Mississippi, Plata, Ganges and Brahmaputra.  Another region of interest is the northern North Atlantic, where satellite observations during the last decade have provided an unprecedented resource to study the spatial distribution and temporal evolution of SSS, allowing to observe areas typically not available by in-situ components of the ocean observing system. Here, the multi-mission SSS dataset is examined in its accuracy and appropriateness for studying SSS variability in high latitudes and marginal seas.

 

How to cite: Melnichenko, O., Hacker, P., Potemra, J., Meissner, T., and Wentz, F.: A New Multi-Mission Sea Surface Salinity Optimum Interpolation (OISSS) Analysis for Ocean Research and Applications, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-3755, https://doi.org/10.5194/egusphere-egu23-3755, 2023.

15:30–15:40
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EGU23-10816
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On-site presentation
Jisun Shin, So-Hyun Kim, and Young-Heon Jo

Ocean surface salinity dataset is useful for research on climate change and its variability. In particular, a gridded daily ocean surface salinity product with high spatial resolution can provide information of short-term variability in the East China Sea (ECS) and the Yellow Sea (YS). Here, we conducted gap-filling of daily surface salinity product based on the Geostationary Ocean  Color Imager (GOCI) for the period 2011-2020 with spatial resolution of 500 m using machine learning approach. For this, we used GOCI-based daily surface salinity preoduct as ground-truth data with envrionemntal variables such as sea surface temperature (SST), sea surface height (SSH), eastward seawater velocity (uo), northward seawater velocity (vo), and seawater salinity (SS) as input data of machine learning model. To identify importance between daily surface salinity and environmental variables affecting daily surface salinity, feature importance ranking was used. Our model shows gap-free daily surface salinity product based GOCI. In addition, the successful application of machine learning model provides the information of long-term variation of daily surface salinity at high spatial resolution in the ECS and the YS.

How to cite: Shin, J., Kim, S.-H., and Jo, Y.-H.: Gap-filling processes for GOCI-based daily surface salinity product using environmental variables and machine learning approach, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-10816, https://doi.org/10.5194/egusphere-egu23-10816, 2023.

Coffee break
Chairpersons: Adrien Martin, Tong Lee, Aida Alvera-Azcárate
16:15–16:25
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EGU23-14466
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On-site presentation
Olivier Membrive, Cécile Hernandez, Hervé Roquet, Stéphane Saux-Picart, Steinar Eastwood, Ad Stoffelen, Anton Verhoef, Eva Howe, and Jean-François Piolle

The OSI SAF (Ocean and Sea Ice Satellite Application Facility) is a dedicated EUMETSAT centre for processing satellite data at the ocean-atmosphere interface. It’s a consortium constituted of Météo-France, as a leading institute, and four cooperating institutes: MET Norway, DMI (Denmark), Ifremer (France), KNMI (Netherlands). 

Utilizing specialist expertise the consortium processes and distributes near real-time products related to key parameters of the ocean-atmosphere interface as well as climate data records of these parameters. Main products are: Winds, Sea and Ice Surface Temperature (SST/IST) and Sea Ice Parameters at both poles: Concentration, Edge, Type, Emissivity and Drift.

The applications of products are numerous. The most general ones are the assimilation into models, the validation of models, oceanography, research and environmental monitoring. The presentation will showcase OSI SAF ocean remote sensing products from meteorological satellites both from the polar and geostationary orbit.  The presentation will focus on the latest developments.

In the next few years, existing Sea Ice, Wind and SST Climate Data records will be improved and extended. It will also be the occasion to discuss the expectations of international colleagues and to describe the plans to exploit the capabilities offered by the future MTG and Metop-SG satellites. 

How to cite: Membrive, O., Hernandez, C., Roquet, H., Saux-Picart, S., Eastwood, S., Stoffelen, A., Verhoef, A., Howe, E., and Piolle, J.-F.: Ocean remote sensing from meteorological satellites at OSI SAF, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-14466, https://doi.org/10.5194/egusphere-egu23-14466, 2023.

16:25–16:35
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EGU23-3701
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On-site presentation
Thomas Meissner, Katherine Wentz, Lucrezia Ricciardulli, and Frank Wentz

A series of new passive microwave satellite sensors will offer strongly enhanced capability to measure ocean surface vector winds (OSVW), sea surface temperature (SST) and salinity (SSS) during the next decade and beyond. Our presentation gives an overview of the features of these instruments and how we plan to use them in future Earth observations.

The Weather System Follow on Microwave Imager WSF-M MWI, operated by the US DoD scheduled to be launched in January 2024, is a follow-up of the US Navy’s WindSat. The sensor calibration system will continue the four-point calibration method implemented with GMI employing a combination of internal and external calibration targets. Like GMI, WSF-MWI is expected to reach absolute calibration accuracy.  As WindSat did, it will provide fully polarimetric measurements at X, Ku and Ka-band and thus be able to continue WindSat’s OSVW data record.

COVWR, developed by NASA JPL and US DoD, was launched in December 2022. Its novel cost-effective design consists of a fixed feedhorn bench and has only the antenna dish spinning. It is fully polarimetric at 3 frequencies within Ku, K, and Ka bands and can observe most Earth locations simultaneously using fore and aft looks. The 2-look capability strongly aids the measurement of wind direction as it does not have to rely on any external input from Numerical Weather Prediction Models, as for example scatterometers do.

JAXA’s AMSR3, to be launched in late 2023, continues the series that started with AMSR-E in 2002 and followed with AMSR-2 in 2012. The presence of the C-band channels is vital for globally measuring SST with passive microwave sensors. The global availability of microwave SST is essential for the scientific community, as it provides observations in the presence of clouds and aerosols, where infrared sensors fail. The AMSR3 sensor will observe at two C-band and at two X-band frequencies, which will result in increased capabilities to measure ocean wind speeds through rain, including in strong tropical and extratropical storms where most other passive microwave sensors cannot provide usable retrievals. It is possible to find combinations between the C- and X-band channels that minimize the impact of rain but are still sensitive to wind speed, which enables disentangling passive wind and rain signals.       

The ultimate passive microwave sensor for ocean observations will be ESA’s CIMR, which is anticipated to launch in 2028. CIMR has an antenna with 8-meter diameter, measures at 5 frequencies between L- and Ka-band, is fully polarimetric at each frequency and has fore and aft looks. These combined features will not only provide the capability for measuring global SST, OSVW and wind speeds in rain as mentioned above. The large antenna will allow these observations to occur at a spatial resolution of 15 km. This constitutes a significant enhancement over currently operating sensors, which reach resolutions of about 50-km for SST and 30-km for OSVW. Finally, the presence of L-band enables CIMR to also measure SSS and thus provide continued SSS satellite data after SMOS, Aquarius and SMAP.

How to cite: Meissner, T., Wentz, K., Ricciardulli, L., and Wentz, F.: Passive microwave satellite sensors of the next decade for observing ocean vector winds, temperature, and salinity: WSF-MWI, COWVR, AMSR3, CIMR, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-3701, https://doi.org/10.5194/egusphere-egu23-3701, 2023.

16:35–16:45
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EGU23-14473
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ECS
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On-site presentation
Sihun Jung and Jungho Im

Estimating diurnal variations of Sea Surface Temperature (SST) is important for studying air-sea heat exchange. Existing operational diurnal SSTs are derived from numerical models incorporating satellite data, and assimilated with in-situ measurements, which are very accurate. However, numerical model-based methods incur significant computational costs for identifying the diurnal cycle of SST from various heat flux sources (i.e., sensible, latent heat). In this study, we first proposed a Generative Adversarial Network (GAN) method to reconstruct high-resolution diurnal SST using satellite observations as an actual diurnal signal from the ocean surface layer. A generator in the GAN model was trained using the diurnal variability-related variables, including the hourly SSTs and shortwave radiation measurements from Himawari-8 geostationary satellite observations, to estimate diurnal SSTs. The discriminator in the GAN model was learned to reduce the difference in spatiotemporal variability of diurnal SSTs between a satellite data-assimilated numerical model product (Global Ocean OSTIA Diurnal Skin Sea Surface Temperature; Copernicus marine service) and estimated SST from the generator. The results showed that the reconstructed SST had a better spatial distribution of ocean phenomena such as front and eddy than compared with the numerical model-derived SST. It implied that the GAN model could simulate a high spatial variability of SSTs using satellite-based data with a spatial resolution of 2km. The proposed GAN model produced high validation accuracy, resulting in the coefficient of determination of 0.99, bias of -0.2℃, and root mean square errors of 0.58℃ when compared with in situ SST Quality Monitor drifting buoy data. Since we use geostationary satellite data, the proposed model can capture real diurnal variability of SST more frequently than existing numerical model data using analysis data. In addition, the proposed deep learning model is much more computationally efficient than the numerical models.

How to cite: Jung, S. and Im, J.: Generative Adversarial Network for Reconstructing Diurnal Sea Surface Temperature using Satellite Data over North-west Pacific, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-14473, https://doi.org/10.5194/egusphere-egu23-14473, 2023.

16:45–16:55
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EGU23-3670
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Virtual presentation
Lucrezia Ricciardulli, Gregory Foltz, Andrew Manaster, and Thomas Meissner

In-situ measurements of extreme winds within hurricanes are challenging and scarce at the global level. They are mostly provided by risky reconnaissance flights, most often in the tropical North Atlantic. In 2021, a novel NOAA project deployed 5 Saildrones (SDs) to monitor the tropical Atlantic storm-track areas. One of these missions, SD-1045, crossed Hurricane Sam (Cat. 4) on September 30, 2021, providing an unprecedented view of ocean surface conditions within a major hurricane, reporting surface winds as high as about 40 m/s.  New SD missions for the 2022 Atlantic hurricane season were also able to intercept the tracks of Hurricane Fiona and Ian.

Here we present a comprehensive analysis and interpretation of the Saildrone ocean surface wind measurements in these hurricanes, using the following datasets for comparison: NDBC buoys in the path of the storms, microwave (MW) radiometer wind retrievals and tropical cyclone (TC) winds from SMAP and AMSR2, wind retrievals from the ASCAT scatterometers, from the high-resolution Synthetic Aperture Radars, and the HWRF model winds. The methodology for adjusting the SD wind measurements to a 10m reference height and to the different spatial scales of satellite observations will be described in detail. In this presentation, we will address the consistency of the SD observations with the satellite data at all wind speed regimes, with special focus at extreme winds.

This study can serve as foundation for planning and monitoring the quality of wind measurements from SD missions in the tropics and extra-tropics using satellite data.  Additionally, if properly interpreted, future SD missions can provide a unique and much needed reference source of calibration/validation for satellite observations at wind speeds above 20 m/s, for which buoy data are less accurate.

How to cite: Ricciardulli, L., Foltz, G., Manaster, A., and Meissner, T.: Assessment of Saildrone Wind Measurements in Tropical Cyclones using Microwave Satellite Sensors, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-3670, https://doi.org/10.5194/egusphere-egu23-3670, 2023.

16:55–17:05
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EGU23-16907
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On-site presentation
Myung-Sook Park, Antonio Mannino, Ryan A. Vandermeulen, and Seonju Lee

This study will present how climate change impacts ocean ecosystems using NASA’s historical ocean color data (SeaWiFS-MODIS-VIIRS). The general trend analyses of ocean color data for less than three decades are hard to distinguish between natural and anthropogenic changes in multiple climate-forcing impacts. Alternatively, we bring a new approach for extracting the ocean’s primary physical modes for modulating climate variability to the ocean ecosystem, called Ocean Physical Modes projection to Ocean Color data (OPM-OC) analysis. This will show how the multiple climate-forcing components separately contribute to the satellite observable biological properties, such as Chlorophyll-a concentration, Colored Dissolved Organic Matter (CDOM), and Inherent optical properties. Also, applying the OPM-OC to the Apparent Visible Wavelength (AVW) index enables to detection of a more extensive shift of ocean color remote sensing reflectance spectrum in the tropical ocean gyre circulation by global warming.

How to cite: Park, M.-S., Mannino, A., Vandermeulen, R. A., and Lee, S.: Climate impact on ocean ecosystem based on 25 years of ocean color satellite data, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-16907, https://doi.org/10.5194/egusphere-egu23-16907, 2023.

17:05–17:15
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EGU23-17532
|
On-site presentation
Stephanie Schollaert Uz, Troy J. Ames, J. Blake Clark, and Dirk Aurin

Nearshore processes that impact water quality for boating, swimming and aquaculture happen at scales where current satellite data lack spatial or spectral resolution. Upcoming government and commercial satellite missions aim to fill this gap. To prepare to maximize the use of these Earth observations and address this challenge, we have been working closely with stakeholders around the Chesapeake Bay to explore satellite-derived indicators that could assist practitioners, i.e. clarity, harmful algal blooms, bacterial indices. Several key assets have recently been deployed to improve the potential for this effort: namely, a new NASA Aerosol Robotic Network for Ocean Color (AERONET-OC) site in the Chesapeake Bay to support ocean color atmospheric correction and validation, hyperspectral satellite data from DESIS and PRISMA, and in situ sampling for satellite calibration and validation. In coordination with these activities, we developed an artificial intelligence (AI) framework for feature discrimination using a single satellite sensor, starting with Sentinel 3a&b OLCI. We are currently extending our initial methodology to integrate multiple satellite data sets of differing spatial, spectral, and temporal resolution, namely MODIS-Aqua with its long record, and DESIS and PRISMA for their hyperspectral and higher spatial resolution. With this Deep learning for Environmental and Ecological Prediction-eValuation and Insight with Ensembles of Water quality (DEEP-VIEW) framework we hope to improve predictions of estuarine impacts of runoff and pollution from land, changes in water clarity, and other metrics that are needed by resource managers and other stakeholders to safeguard health and safety around the Chesapeake Bay.

How to cite: Schollaert Uz, S., Ames, T. J., Clark, J. B., and Aurin, D.: Integration of satellite and in situ observations into machine learning for coastal water quality, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-17532, https://doi.org/10.5194/egusphere-egu23-17532, 2023.

17:15–17:25
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EGU23-8599
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ECS
|
On-site presentation
Alejandro Roman, Antonio Tovar-Sanchez, Adam Gauci, Alan Deidun, Isabel Caballero, Emanuele Colica, Sebastiano D'Amico, Sergio Heredia, and Gabriel Navarro

The concentrations of parameters such as Chlorophyll-a (Chl-a) and Total Suspended Solids (TSS) in seawaters have already been used as indicators of the water quality, the biogeochemical status of surface waters, and nutrient availability. Unmanned Aerial Vehicles (UAVs) have gained global popularity as a remote-sensing tool as they address the optical challenges of water-quality studies in coastal regions. In this work, we evaluate the applicability of a 5-band multispectral sensor mounted on a UAV to derive scientifically valuable water parameters (Chl-a and TSS). The performance of the OC-2 and OC-3 algorithms for Chl-a estimation, as well as the TSS estimation method by Nechad et al. (2010), are tested in two different sites along the Mediterranean coastline. This study provides water quality details on the centimetre-scale and improves the existing approximations that are available for the region through Sentinel-3 OLCI imagery at a much lower spatial resolution of 300 m. The Chl-a and TSS values derived for the studied regions were within the expected ranges and varied between 0 to 3 mg/m3 and 10 to 20 mg/m3, respectively. In addition, a novel Python workflow for the manual generation of an orthomosaic in aquatic areas based on the sensor’s metadata, without the need to resort to commercial photogrammetric software, is proposed. Linear regressions were also applied to compare the Remote Sensing reflectance (Rrs) retrieval methods tested, suggesting strong R2 correlations between 0.83 and 0.91 for the “deglinting” method.

How to cite: Roman, A., Tovar-Sanchez, A., Gauci, A., Deidun, A., Caballero, I., Colica, E., D'Amico, S., Heredia, S., and Navarro, G.: High-resolution UAV multispectral imagery for water-quality monitoring in coastal regions, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-8599, https://doi.org/10.5194/egusphere-egu23-8599, 2023.

17:25–17:35
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EGU23-8662
|
On-site presentation
Ana B. Ruescas, Jorge Garcia-Jimenez, Dagmar Mueller, Carsten Brockmann, Julia Amoros, and Kerstin Stelzer

Monitoring and mapping of major phytoplankton groups (PG) or functional types (PFT) has been targeted as a relevant topic for the understanding and study of marine ecosystem, especially under the present climate change scenario. Developing of algorithms that determine the structure of the phytoplankton communities is a reality since the last 20 years, but not much advanced has been done in the field of image spectroscopy due to the lack of spaceborne sensors with a systematic high temporal and spatial scales. Some operational sensors are changing the game right now, like PRISMA, ENMAP and, in the future, SGB, CHIME and other developments in the hyperspectral dimension. Most of the approaches for determining PFT or PG are based on phytoplankton abundance, cell size or bio-optical properties that use chlorophyll-a or spectral features (absorption, backscatter, and/or reflectance) on water in the VIS-NIR range as inputs. These and other approaches based on machine learning and deep learning are being tested on ENMAP imagery over the Baltic Sea. We will use reflectance data provided by DLR. Comparison of atmospheric correction approaches seems to be a necessary step, and radiance data will be process with current available algorithms. Since ENMAP has 240 bands, and high spatial resolution (30 m), we will tackle the dimensionality reduction problem adapting well-known machine learning approaches to the sensor characteristics (https://isp.uv.es/soft_feature.html).

How to cite: Ruescas, A. B., Garcia-Jimenez, J., Mueller, D., Brockmann, C., Amoros, J., and Stelzer, K.: Study of ENMAP imagery for the application of methods for Phytoplankton Functional Types determination in coastal waters, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-8662, https://doi.org/10.5194/egusphere-egu23-8662, 2023.

17:35–17:45
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EGU23-15931
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ECS
|
On-site presentation
Marine Bretagnon, Jean-Noël Druon, Antoine Mangin, Marco Clerici, and Christophe Lavaysse

Phytoplankton is at the base of the marine food web but only 10 to 20 % of the primary production is available to higher trophic levels. Besides chlorophyll-a concentration, chlorophyll-a horizontal gradients were shown to mostly sustain the development of mesozooplankton and the marine food web. The detection of chlorophyll-a gradients from satellite ocean colour data therefore appears to be central to the management of sustainable fisheries.

An algorithm allowing the detection of relevant frontal productivity to fish production has been published in 2021 by the Joint research Centre (JRC). This algorithm was operationally computed and made publicly available at global scale in the JRC Data Catalogue and distributed to national institutes in Africa throughout a software named eStation within the GMES & Africa Program. This algorithm has been developed based on MODIS-Aqua data as this sensor longevity of more than 20 years is an asset for studying the temporal evolution of fish ecological habitat and the impact of fisheries by comparing effective with potential fishing yields. However, MODIS-Aqua observation will likely end soon. It becomes therefore primordial to adapt the analysis from MODIS-Aqua to the recent Sentinel 3 OLCI (Ocean and Land Colour Imager) sensor to ensure the continuity of the fish production monitoring including in real-time. OLCI-S3A, which started in April 2016, is the first sensor of the constellation to observe the chlorophyll-a concentration with the objectives to improve the daily coverage of the ocean surface and increase the accuracy of the gradient retrieval.

Here, we present the methodology used to recalibrate the relevant chlorophyll-a gradients to fish production from MODIS-Aqua to OLCI-S3A data at 4 and 1 km spatial resolution with the objective to enriching the Copernicus Marine Environment Monitoring Service catalogue and pursuing the operational marine activities of the eStation on Potential Fishing Zones (PFZ) within the GMES & Africa Program.

How to cite: Bretagnon, M., Druon, J.-N., Mangin, A., Clerici, M., and Lavaysse, C.: Recalibration of the relevant productivity frontal features to fish production from the MODIS-Aqua to Sentinel 3 OLCI ocean colour sensors, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-15931, https://doi.org/10.5194/egusphere-egu23-15931, 2023.

17:45–17:55
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EGU23-13911
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On-site presentation
Cedric jamet, Daniel Jorge Schaeffer, and Hubert Loisel

The fine-scale study of the diffuse attenuation coefficient, Kd(l), of the spectral solar downward irradiance  is only feasible by ocean color remote sensing. Several empirical and semi-analytical methods exist. However, most of these models are generally applicable for clear open ocean waters. They show limitations when applied to coastal waters. A new empirical method based on neural networks has been developed using a relationship between the remote-sensing reflectances between 443 and 670 nm and Kd(λ). The architecture of the neural network has been defined using synthetical and in situ dataset. The model has been developed for SeaWiFS, MODIS-AQUA, MERIS, VIIRS, OLCI and PACE space-borne sensors. Validation using in-situ measurements from a wide range of type of waters (from oligotrophic to very turbid waters) shows similar retrievals accuracies for low values of Kd(490)  (i.e. <0.20 m-1) and better estimates for greater values of and Kd(490). The new model is compared to empirical and semi-empirical methods and is suitable for open water but also for turbid waters.

How to cite: jamet, C., Jorge Schaeffer, D., and Loisel, H.: Estimation of the spectral diffuse attenuation coefficient Kd(λ) from UV to NIR using ocean color images: Application from SeaWiFS to PACE, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-13911, https://doi.org/10.5194/egusphere-egu23-13911, 2023.

Posters on site: Fri, 28 Apr, 10:45–12:30 | Hall X5

Chairpersons: Adrien Martin, Tong Lee, Aida Alvera-Azcárate
X5.373
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EGU23-575
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ECS
|
Ana Filipa Duarte, Álvaro Peliz, Renato Mendes, Luís Matias, and Leonardo Azevedo

Seismic oceanography as remote sensing of the ocean structure by multichannel reflection seismic method can provide high-resolution images enabling the study of fine-scale ocean processes along large distances.

The seismic acoustic response depends on differences in ocean temperature and salinity, and the resulting seismic images track the interfaces between those thermohaline layers both laterally and in depth. The structural interpretation of observed seismic reflections provides valuable oceanographic insights to understand mixing processes and phenomena occurring at different water column depths.

Three parallel 2D multichannel seismic reflection profiles acquired by the Portuguese Task Force for the Extension of the Continental Shelf in the Madeira Abyssal Plain (MAP), profiles covering 300km and ~100km apart from each other, dating from 2006, were processed to enhance the amplitudes of the water column (Azevedo, L. et al., 2021) and analyzed jointly with conductivity-temperature-depth probes (CTDs) from 2002 and 2005 acquired by Poseidon research vessel.

The structure of the water column in this area is characterized by the intrusion of Mediterranean Outflow Waters (MOW), warmer and salty water mass expressing between the 500 and 1500 m depth, and overlaying Subarctic Intermediate Water where temperature and salinity decrease in depth. Due to the differences in temperature and salinity gradients, the MAP region is auspicious for developing double diffusion, specifically thermohaline staircases (van der Boog, C. et al., 2021). Double diffusion is shown to influence the efficiency of vertical mixing of the different water masses; it affects the vertical transport of nutrients, temperature, and salt and contributes to ocean circulation, which is intrinsically connected to the control of the earth’s climate. Nevertheless, it is still lacking information.

We detected the thermohaline staircases expression in temperature and salinity profiles plotted as a function of depth, noticing that the interfaces of mixing followed by layers of well-mixed temperature and salinity are well defined as a step structure and were validated as double diffusion by calculating the Turner angle and Density Ratio at those depths.

Simultaneously, the seismic profiles are characterized by continuous sub-horizontal reflections between the ~1200 to 2000 meters of depth. By correlating the CTD profiles with the seismic images, it is noticeable that the staircases on the vertical profiles correspond to the reflections on the seismic at the expected depths and are covering almost the entirety of seismic profiles.

Since those reflections are present in the three parallel seismic profiles, we use them to predict the lateral continuity of the step-like structures and build models of the incidence of double-diffusive thermohaline staircases in the region, contributing to the knowledge of those processes' extension and expression in the Madeira Abyssal plain.

References:

van der Boog, C. G., Dijkstra, H. A., Pietrzak, J. D., & Katsman, C. A. (2021). Double-diffusive mixing makes a small contribution to the global ocean circulation. Communications Earth & Environment, 2(1), 1-9.

Azevedo, L., Matias, L., Turco, F., Tromm, R., & Peliz, Á. (2021). Geostatistical seismic inversion for temperature and salinity in the Madeira Abyssal Plain. Frontiers in Marine Science, 8, 685007.

How to cite: Duarte, A. F., Peliz, Á., Mendes, R., Matias, L., and Azevedo, L.: Characterizing the ocean with acoustic waves, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-575, https://doi.org/10.5194/egusphere-egu23-575, 2023.

X5.374
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EGU23-910
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ECS
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Sthitapragya Ray and Debadatta Swain

Coastal Upwelling, the upward flux of nutrient-rich waters into the euphotic layer, is associated with remarkable phytoplankton blooms, which form the base of the marine food web. In addition this entrainment of cold deeper waters to the surface, leads to sea surface temperature (SST) cooling that can also be determined from satellite observations of coastal SST gradients (often resulting in thermal fronts). Thermal fronts, (especially in high chlorophyll regions of the ocean,) are typically associated with significant biological activity.Thus, the detection of potential fishing zones (PFZs) typically involves the identification of fronts from satellite or model SST and Chl-a data. The western Bay of Bengal region presents some unique challenges with regard to the characterization and detection of PFZs based on these satellite data alone. Namely, the presence of clouds during southwest monsoon, (the season associated with the largest fish catches) limits the availability of infrared and visible data necessary for the estimation of high resolution SST and Chl-a. This difficulty is usually circumvented by using modelled SST and Chl-a data, which unfortunately illustrate significant disagreements with the corresponding observational datasets, especially for fronts with low persistence. Coastal upwelling along the east coast of India is seasonal and driven by southwesterly winds in the pre-monsoon (March – May) and earlier half of monsoon (June – July.)  We have previously characterized the seasonal variability of this system based on the near-shore SST gradient (represented in terms of an SST based upwelling index UISST.) In addition to this the second complex empirical orthogonal function of SSHA was also observed to consist of negative coastal anomalies that are strongly correlated with the local alongshore windstress (AWS) (which is considered the wind based proxy upwelling index), the driver of coastal upwelling (Ray et al, 2022.) This study includes a multiscale analysis of the association between the generation of SST fronts or PFZs and the proxies of coastal upwelling (such as UISST, AWS, SSHA reconstructed from the second EOF mode.) e.g. figure 1 illustrates the occurrence of high frontal probability indices (FPIs) along a part of the coast previously identified to be a local wind-driven coastal upwelling system (Ray et al, 2022,) while figure 2 illustrates a close agreement (correlation coefficient = 85%) between the seasonally filtered SST-based upwelling index and the FPI around one coastal point. An improved understanding of the role of coastal upwelling in the generation of PFZs is potentially of great societal importance as it can enable the development of methods of detecting/forecasting the probability of formation of PFZs based on surface wind and SSHA observations which are not affected by the presence of clouds.

Figure 1

Figure 2

 

Reference:

Ray, S., Swain, D., Ali, M. M., & Bourassa, M. A. (2022). Coastal Upwelling in the Western Bay of Bengal: Role of Local and Remote Windstress. Remote Sensing14(19), 4703.

How to cite: Ray, S. and Swain, D.: Role of Coastal Upwelling in the Generation of Potential Fishing Zones in the South-western Bay of Bengal, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-910, https://doi.org/10.5194/egusphere-egu23-910, 2023.

X5.375
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EGU23-1705
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ECS
Luís Figueiredo, Renato Mendes, Caio Fonteles, and Nuno Loureiro

Remote sensing plays a vital role in understanding and managing the oceans. This technology is used to observe and monitor the ocean's physical, chemical, and biological properties, allowing scientists to detect large-scale changes in the marine environment, such as currents, sea surface temperature, and marine life populations. This data can then be utilized to track changes in the marine environment, assess the ocean’s health, and identify areas that require conservation efforts. 

In oceanography, a front is a boundary between two distinct water masses with different properties, such as temperature, salinity, and density. These fronts are critical scientific phenomena and have a cascade of events of significant importance to the fishing, marine biology, shipping, and logistics industries. For example, upwelling fronts are typically sites of strong vertical movements that bring cold, nutrient-rich water to the euphotic zone. This phenomenon is a primary factor controlling phytoplankton growth, which is the foundation of the marine food chain. It can also influence the concentration of floating marine litter, plastic, and other human-made objects. 

Our work comprised the search, revision, and implementation of three algorithms to detect oceanic fronts through the model and satellite sea surface temperature (SST) data. The chosen algorithms, Canny, Belkin O’Reilly, and Cayula-Cornillon, use SST data to provide historical frontal probability maps and near-real-time daily fronts identification. These algorithms were aggregated, simplified, and adapted for use in the Python programming language. 

Establishing free and open repositories helps to spur research, innovation, and development. That’s why we have created the following public repository (https://github.com/CoLAB-ATLANTIC/JUNO), which includes a set of notebooks outlining the step-by-step process for obtaining frontal probability or daily fronts maps using each of the three algorithms. The method consists of downloading the data (MUR or CMEMS), applying the algorithms, and saving the results in a NetCDF file. 

This repository will help scientists, researchers, and business people understand the ocean’s dynamics and make front detection more accessible. Through this repository, our work is making strides to advance the oceanography field and make ocean research more efficient and available to everyone.

How to cite: Figueiredo, L., Mendes, R., Fonteles, C., and Loureiro, N.: Public Repository for Ocean Front Detection: a Contribution to Marine Science, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-1705, https://doi.org/10.5194/egusphere-egu23-1705, 2023.

X5.376
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EGU23-2167
Chung-Ru Ho and Yu-Hao Tseng

Although the issue of the global warming hiatus during 1998-2013 is still under debate, the surface velocity of the Kuroshio has been shown to decrease east of Taiwan but increase east of Luzon during the global warm hiatus period. In this study, we revealed a significant increase in cut-off events on the Kuroshio main path east of Taiwan during the hiatus period. This is probably related to the interaction between the weakening Kuroshio off the east of Taiwan and the westward propagation of the mesoscale cyclone eddy from the western Pacific Ocean. The cut-off event of the main path of the Kuroshio is determined using the momentum ratio of the Kuroshio to the eddy. If the ratio is less than one, the eddy dominates and the Kuroshio is cut off. Sea surface velocities derived from satellite altimeter data for these events in the Kuroshio region east of Taiwan and the Kuroshio intrusion region west of the Luzon Strait were then analyzed by empirical orthogonal functions. The results showed that the Kuroshio intrusion to the west of the Luzon Strait increased after an average of about one month after the main Kuroshio off east of Taiwan was cut off. This appears to be reverse feedback from the Kuroshio downstream to upstream.

How to cite: Ho, C.-R. and Tseng, Y.-H.: Relationship between Kuroshio variation east of Taiwan and the Kuroshio intrusion west of the Luzon Strait during 1998-2013, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-2167, https://doi.org/10.5194/egusphere-egu23-2167, 2023.

X5.377
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EGU23-2173
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ECS
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Yu-Hao Tseng and Chung-Ru Ho

When the Kuroshio passes through the Luzon Strait from its upstream east of Luzon Island to its downstream east of Taiwan, there are three types of possible routes. First of them is the western component, also known as the western branch, which can be further divided into looping path and leaking path (the Kuroshio intruding into the South China Sea). The second type is the mainstream connecting the eastern Luzon Island and the eastern Taiwan Island, which has the pattern nearly as same as the long-term mean of the Kuroshio path in this region. Lastly, the third type of route is the eastern branch that is to be focused by this study. Its definition is that the east components of the current around the Kuroshio's route are greater than the north components and then taking the material away from the Kuroshio main stream in the eastward direction. Therefore, to find the possible routes of the Kuroshio and make the numbers of trajectories of each simulation to be fairly same with the other days, we use OpenDrift (an open-source Python-based framework for Lagrangian particle modeling) as a tool to simulate the trajectories of the Kuroshio started at a given position 18.375°N and 122.875°E from 1993 to 2020. The input data that used as simulation is the geostrophic current derived from altimeter data provided by CMEMS from January 1993 to December 2021. The spatial and temporal resolutions of the input data are 0.25° and one-day, respectively. The results revealed that the accumulated numbers of trajectories as the type of the eastern branch of the Kuroshio would be more frequently during March to June. Meanwhile, the averaged wind stress curl (WSC) of the 10-m wind field data from NCEP/NCAR Reanalysis 1 with a 4-times daily temporal resolution and a 1.875° horizontal resolution was calculated. The difference between the monthly WSC and the annual mean WSC over the entire Pacific Ocean from 1993 to 2020 showed that there is a significant eastward transport along the 20°N-21°N latitude in May. This implies that the eastern branch of the Kuroshio may be caused by WSC.

How to cite: Tseng, Y.-H. and Ho, C.-R.: The eastward trajectories in the Kuroshio upstream region, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-2173, https://doi.org/10.5194/egusphere-egu23-2173, 2023.

X5.378
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EGU23-4875
Tong Lee, Sarah Gille, Fabrice Ardhuin, Justin Boland, Mark Bourassa, Paul Chang, Sophie Cravatte, Tom Farrar, Melanie Fewings, Gregg Jacobs, Zorana Jelenak, Florent Lyard, Jackie May, Elisabeth Remy, Lionel Renault, Ernesto Rodriguez, Clément Ubelmann, Bia Villas Bôas, and Alexander Wineteer

Ocean surface currents are critical not only to ocean dynamics, but also to marine ecosystems, maritime navigation and safety, search and rescue, monitoring and mitigation of marine pollution including oil spills, plastic, and debris. Wind-current coupling impacts both the ocean and the atmosphere, thereby influencing weather and climate. Recent modeling studies underscore the importance of submeoscale-to-mesoscale surface currents in ocean dynamics, marine ecosystems, and air-sea interactions. However, the present observing system is inadequate in observing these currents, posing major challenges in understanding their impacts. Moreover, many operational oceanography applications require measurements of these small-scale currents over the global ocean.  To reduce these knowledge and capability gaps, here we present a satellite mission concept “Ocean Dynamics and Surface Exchange with the Atmosphere” (ODYSEA) that is being proposed as a NASA Earth System Explorers satellite through a strong partnership with CNES. The mission will provide the first-ever measurements of total (geostrophic+ageostrophic) surface currents in the global ocean along with simultaneous measurements of ocean-surface vector winds. ODYSEA is designed to have a 1700-km wide swath, providing approximately daily coverage of the global ocean with 5-km postings. These measurements will provide an unprecedented opportunity to unravel the physical processes underlying small-scale ocean dynamics and air-sea interactions. ODYSEA’s near real-time data will support key operational needs such as weather and ocean forecasting, search and rescue, and seafaring.

How to cite: Lee, T., Gille, S., Ardhuin, F., Boland, J., Bourassa, M., Chang, P., Cravatte, S., Farrar, T., Fewings, M., Jacobs, G., Jelenak, Z., Lyard, F., May, J., Remy, E., Renault, L., Rodriguez, E., Ubelmann, C., Villas Bôas, B., and Wineteer, A.: A satellite mission concept to unravel small-scale ocean dynamics and air-sea interactions: ODYSEA (Ocean Dynamics and Surface Exchange with the Atmosphere), EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-4875, https://doi.org/10.5194/egusphere-egu23-4875, 2023.

X5.379
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EGU23-6246
Daniel Carlson, Burkard Baschek, Henning Burmester, Martin Hieronymi, and Rüdiger Röttgers

Small-scale (e.g. submesoscale and boundary layer scale) ocean features, like fronts and eddies, play a critical role in the transport and mixing of tracers. Despite recent advances, resolving such small-scale features in satellite imagery remains challenging. Accurately representing sub-grid-scale features in ocean models also remains an active area of research that should be guided by observations. Here, we present high resolution (~1 m) aerial observations of sea surface temperature that were acquired in December 2019 offshore of the island of Fogo (Cape Verde). The SST observations were obtained by a longwave infrared camera system that was operated from a Stemme powered glider. Direct georectification of open-ocean SST imagery was performed using the position and orientation data obtained from a global navigation satellite system receiver and an inertial navigation system. Georectified SST images were transferred in real-time to the R/V Meteor  to enable in situ sampling across a rapidly evolving front. The SST observations show strong convergence at the front and elevated Rossby Numbers (~O10-100), which are indicative of non-linear flows.

How to cite: Carlson, D., Baschek, B., Burmester, H., Hieronymi, M., and Röttgers, R.: High resolution aerial sea surface temperature observations of small-scale frontal features in the open ocean, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-6246, https://doi.org/10.5194/egusphere-egu23-6246, 2023.

X5.380
|
EGU23-9940
Adrien Martin, Karlus Macedo, Marcos Portabella, Louis Marié, José Marquez, David McCann, Ruben Carrasco, Rui Duarte, Adriano Meta, Christine Gommenginger, Petronilo Martin-Iglesias, and Tania Casal

OSCAR (Ocean Surface Current Airborne Radar) is a new airborne instrument which provides unique 2D synoptic views of ocean and atmosphere dynamics (currents, waves, winds) below km-scale. OSCAR is the airborne demonstrator of SeaSTAR, an innovative satellite mission concept currently under study in Phase 0 of ESA Earth Explorer 11. SeaSTAR aims to observe ocean submesoscale dynamics and small-scale atmosphere-ocean processes in all coastal, shelf and polar seas by providing simultaneous measurements of current and wind vectors at 1 km resolution with accuracy better than 0.1 m/s and 2 m/s respectively. A key objective of SeaSTAR is to characterize, for the first time, the magnitude, spatial structure, regional distribution and temporal variability of upper ocean dynamics on daily, seasonal and multi-annual time scales, with particular focus on coastal seas, shelf seas and Marginal Ice Zone boundaries.

OSCAR was flown over the Iroise Sea (West of Brittany, France) in May 2022 during the SEASTARex campaign. The OSCAR operations and products are representative of the spaceborne concept, with geophysical parameters and accuracies that directly relate to those of the SeaSTAR satellite mission. In itself, OSCAR provides a new observing capability that will improve our understanding of microwave Doppler sensing of the ocean thanks to its unique Doppler and scatterometry capabilities in three azimuth directions. OSCAR’s high-resolution images (8 metres pixels resolution) over a 5km swath provide 2D synoptic views of ocean and atmosphere dynamics below km-scales that are highly relevant to support and complement scientific investigations of fine-scale ocean-atmosphere processes based on in-situ, satellite and model data.

In this paper, we give an overview of the OSCAR system, of the SEASTARex campaign over the Iroise Sea in May 2022 and present the main preliminary results about the performance and imaging capability of the instrument.

How to cite: Martin, A., Macedo, K., Portabella, M., Marié, L., Marquez, J., McCann, D., Carrasco, R., Duarte, R., Meta, A., Gommenginger, C., Martin-Iglesias, P., and Casal, T.: OSCAR: a new airborne instrument to image ocean-atmosphere dynamics at the sub-mesoscale: instrument capabilities and the SEASTARex airborne campaign, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-9940, https://doi.org/10.5194/egusphere-egu23-9940, 2023.

X5.381
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EGU23-10038
Alexey Mironov, Yves Quilfen, Jean-Francois Piolle, and Bertan Chapron

The Chinese-French Ocean Satellite (CFOSAT) is an innovative space mission dedicated to the global observation and monitoring of the ocean's sea state and sea surface vector winds. CFOSAT operates two Ku-band rotating radars: the near-nadir Ku-band wave scatterometer (SWIM) and the dual-polarization, moderate-incidence-angle, Ku-band wind scatterometer (SCAT). This dual-incidence-angle instrumental configuration provides regular collocated measurements of radar backscatter to retrieve sea surface state parameters, including significant wave height, directional wave spectrum, and wind vector. Observations taken at different incidence angles have different sensitivities to sea surface parameters, such as short and long waves, surface currents, and surface temperature. Furthermore, synchronized backscatter from two different sensors can be mutually analyzed to improve the quality of sea surface wind retrievals. The joint use of two or multiple collocated data sources for geophysical retrieval requires a very high-quality of all input data, calibrated in the common reference framework. In addition to the well-studied signal distortion effects of fixed-oriented antenna design, the backscatter obtained with rotating antenna radars could potentially be influenced by additional azimuth-dependent factors, such as internal temperature variation and along-track noise amplification. Furthermore, new experimental antenna and hardware configurations can be difficult to adequately calibrate and validate quickly, which negatively impacts the speed of scientific and applied use of the acquired data. 
In this work, we propose a fast calibration approach which allows for rapid (~1 day) sigma0 calibrations. This approach is based on Numerical Weather Prediction (NWP) most probable wind histogram matching. It is applied to each instrument, satellite pass (ascending or descending), antenna azimuth, incidence angle, and polarization. All signals are then adjusted to the same level, followed by deriving a new instrument-specific Geophysical Model Function (GMF) which maps backscattered sigma0 as a function of wind speed and direction, incidence angle, naturally taking into account all factors related to the instrument (e.g. internal noise, antenna swath distortion, etc.). 
The validation of the proposed approach in application to SCAT data was done using the standard KNMI CWDP processor, where the wind vector retrieval was done for original and corrected data. A significant improvement of the retrieved wind vector quality was achieved for the left and right parts of the radar swath. The validation algorithm was applied to historical CFOSAT SWIM and SCAT data sets in terms of IFREMER Wind and Wave Operation Center (IWWOC) and incorporated into CFOSAT SCAT L2S and SWMSCAT L2S products.   
The proposed two-step strategy allows us to empirically recalibrate historical datasets of radar backscatter in cases where traditional sigma0 calibration/validation approaches are under development or the instrument faces unexpected signal level fluctuations. We anticipate that the proposed algorithm could be easily extended to most existing and future space radar configurations in order to accelerate the practical usage of satellite measurements when necessary.

How to cite: Mironov, A., Quilfen, Y., Piolle, J.-F., and Chapron, B.: The approach for the fast calibration of rotating antenna radar backscattered signal. The example of CFOSAT mission., EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-10038, https://doi.org/10.5194/egusphere-egu23-10038, 2023.

X5.382
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EGU23-11144
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ECS
Deuk Jae Hwang, Robert Frouin, Jing Tan, Jae-Hyun Ahn, Jeong-Eon Moon, and Jong-Kuk Choi

Photosynthetically available radiation (PAR) has a key role in generating primary production at the ocean surface. Various satellite sensors were used to estimate PAR at the ocean, but GOCI, the world’s first geostationary ocean color sensor, didn’t service PAR as an official product yet. In this study, PAR for the ocean around Korean Peninsula was estimated by using GOCI and validation was carried out. GOCI daily PAR was estimated from plane-parallel theory based model and corrected with in-situ measurements PAR data which had been collected at two ocean research stations. Corrected GOCI daily PAR has a high R2 value (0.99) with in-situ measurements. GOCI daily PAR also shows a high accuracy in terms of root-mean-square error (RMSE) and mean bias error (MBE); 4.98 % and -0.52 %, respectively. As a result of the comparison with other sensors derived PAR data, GOCI daily PAR shows the highest performance at the ocean around Korean Peninsula. MODIS and AHI derived daily PAR have lower accuracy than GOCI. MODIS daily PAR has RMSE of 10.40 % and -4.15 % with in-situ measurements, and AHI daily PAR has 6.51 % and -4.65 %, respectively. GOCI daily PAR help to understand the marine environment around the Korean Peninsula. For the further study, PAR from GOCI-II will be discussed.

How to cite: Hwang, D. J., Frouin, R., Tan, J., Ahn, J.-H., Moon, J.-E., and Choi, J.-K.: A study on the Estimate of Daily PAR at the ocean around Korean Peninsula using GOCI, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-11144, https://doi.org/10.5194/egusphere-egu23-11144, 2023.

X5.383
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EGU23-12776
Hee-Jeong Han, Suk Yoon, Ki-Beom Ahn, Hyun Yang, and Young-Je Park

We are conducting a research project to develop algorithms to derive information on maritime issues based on images from Geostationary Ocean Color Imager (GOCI), GOCI-II and many others satellite sensors. We are aiming for several practical application areas which are to detect floating macroalgae, marine fog, harmful algal blooms, fine aerosol particles, low sea surface salinity water, to detect and forecast abnormal sea surface temperature, and to derive ocean water quality parameters and primary production. We are also focusing on candidates to discover new practical techniques using machine learning. These practical techniques are integrated into a maritime issue service system which consists of a data collection and processing system and a web-based data display and analysis system. A scheduler was configured for the automation of the data collection and processing system, and a detailed design was carried out. A system prototype based on open-source GIS service was developed. We will verify the performance of the techniques by comparing the results with high-resolution satellite data or reliable in-situ data.

How to cite: Han, H.-J., Yoon, S., Ahn, K.-B., Yang, H., and Park, Y.-J.: Introduction to practical application and service systems of multi-satellite data including Geostationary Ocean Color Imager to maritime issues, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-12776, https://doi.org/10.5194/egusphere-egu23-12776, 2023.

X5.384
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EGU23-13033
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ECS
Marine De Carlo, Fabrice Ardhuin, and Annabelle Ollivier

Recent altimeter retracking (e.g. Tourain et al. 2021) and filtering methods (Quilfen et al. 2019) have considerably reduced the noise level in estimates of the significant wave height (Hs), allowing to study smaller scale processes. Previous studies on the along-track variations of wave height have shown that wave-current interactions may explain most of the variability at scales 20 to 100 km (Ardhuin et al. 2017, Quilfen and Chapron 2019). Here we take advantage of the very low noise level of SWIM nadir beam to explore scales under 10 km, looking at the accuracy of Hs measurements in storms.

From theory, we expect that part of the short-scale variability of the estimated Hs is related to wave groups which lead to random variations in wave height at scales of a few kilometers, depending on the sea state. Theory on signal envelopes links the spatial distribution of wave heights to the convolution of the wave spectrum (Rice 1944) thus allowing to estimate the variability linked to wave groups.

Here, we use the fact that ocean waves spectra are routinely measured by CFOSAT’s SWIM instrument to evaluate the theoretical contribution of wave groups to the wave height variability within the 80 km² SWIM boxes, using the CFOSAT L2 and L2S products. In this study, we show that, in average, around half the Hs variance at the scale of SWIM boxes can be associated to wave groups. 

How to cite: De Carlo, M., Ardhuin, F., and Ollivier, A.: Wave groups signature in small scale wave height variability from cfosat, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-13033, https://doi.org/10.5194/egusphere-egu23-13033, 2023.

X5.385
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EGU23-13663
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ECS
Lucas Charron and Alexey Mironov

As the number of satellite missions observing the ocean increases, reliable and cost-efficient tools are needed to accurately calibrate and validate the expanding network of space sensors. Traditionally, sea surface buoys have been used to measure various oceanic and atmospheric parameters; however, when it comes to subsatellite data related to processes occurring in the upper ocean layer, the use of traditional buoys is not always straightforward. The properties of different sensing bands, acquisition rates, sensor time and spatial resolution must be taken into account to properly match satellite remote signals with in-situ buoy measurements. This necessitates the creation of specialized sensors dedicated to measuring sea surface parameters, such as directional wave spectrum, sea surface current, and temperature, that have the most significant impact on remote sensing signal formation.

The Miniaturized Electronics Lagrangian Oceanographic Drifter (MELODI) program has developed a specialized electronic platform that enables the rapid construction of miniaturized, cost-effective sea surface drifters for subsatellite calibration/validation tasks. The hardware can be configured with different set of onboard sensors, data preprocessing/compression modules, and satellite-enabled communication systems to allow for the real-time collection and transmission of data. Preference is given to the extensive use of environmental-friendly and biodegradable materials, as well as to the implementation of an industrial fabrication process to reduce the time and costs of mass buoy production.

Our study addresses the specificity of measurements obtained from small-scale platforms, such as directional wave spectrum distortions due to intrinsic noise, reduction of wind impact on the buoy drift, oceanographic data preprocessing and compression for IOT small satellite messaging, etc. We present the first results of an in-situ validation campaign (7 day long) for a buoy created specifically for cal/val of the Surface Water and Ocean Topography (SWOT) mission. This miniaturized drifter (~15 cm in diameter) has onboard accelerometers, gyroscopes, magnetometer, GPS sensors and is dedicated to the systematical measurement of significant wave height, directional wave spectrum and sea surface current. The satellite-based communications allow real-time reporting every 15 min, with an expected autonomy of 2 months up to 1 year depending on the configuration and reporting frequency.

We expect that the present results,  electronic platform, and proposed algorithmic and technical solutions will allow enabling the development and implementation of a more robust network of observational drifting buoys for calibration and validation of ocean monitoring satellite missions. 

The work was supported by the project “Development of marine services using space data and IOT technologies by Kinéis” funded by IFREMER and Kinéis.

How to cite: Charron, L. and Mironov, A.: Miniaturized wave measurement drifter for undersatellite calibration and validation network, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-13663, https://doi.org/10.5194/egusphere-egu23-13663, 2023.

X5.386
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EGU23-13690
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ECS
|
Lin Zhang, Hwa Chien, and Wen-Hao Yeh

Global navigation satellite system reflectometry (GNSS-R) is designed to get the quasi-specular reflection of the GNSS signal over the Earth's surface. The reflected signal of GNSS-R recorded on a Delay-Doppler Map (DDM) may then be used to retrieve wind speed, ocean surface roughness, and latent heat flux over the open ocean and to retrieve soil moisture over the land. The L-band signal from GNSS is transmitted via forward (quasi-specular) scattering geometry, obeying the geometric optics (GO) limit of the Kirchhoff approximation (KA). One of the factors on DDM is bistatic radar cross section (BRCS) which represents sea surface roughness. The ocean surface slope and roughness spectrum sensed by GNSS-R response to the energy transferred to the ocean makes it possible for the wind speed to retrieve from the GNSS-R DDM. L-band (~1.5 GHz) microwave is less sensitive to the rain than higher frequency band signals such as Ku- band and C- band. There is a potential to use the L-band signal to retrieve wind speed over not only the fully-developed sea but over limited-fetch sea under more extreme weather systems, such as under tropical cyclones.

In this study, we will present a wave-considering retrieving wind speed algorithm for the new GNSS-R satellite, TRITON. TRITON (Wind-Hunter Satellite) is designed and manufactured by Taiwan Space Agency (TASA) and will be launched in the first season of 2023.

The wind speed retrieving algorithm contains self-built Level 1b (L1b) and Level 2 (L2) algorithms. The L1b algorithm starts from Level 1a output, power signal DDM in watts. We will introduce the procedure to calibrate the DDM to normalized bistatic radar cross section (NBRCS) in meters and compute the DDM observables (DDMA and LES) for the next level. The performance assessment of the self-developed Level 1 algorithm. Good agreements have been found compared to the CYGNSS results. The correlation coefficient among ~0.5 million DDMA_cygnss and DDMA_Triton points regression is 0.95. The root-mean-squared error is 4.99, with data ranging from 0 to 200, and the scattering index is 0.19.

In the L2 algorithm, wind speed will be retrieved in two steps. In the first step, NBRCS will be used to compute the mean square slope (mss) with the help of the Fresnel reflection coefficient. In the second step, the relationship between wave age, mss, and wind speed developed based on the state-of-art microwave remote sensing study will be applied to retrieve wind speed. Uncertainty of the two-step algorithm will be assessed and compared with the results from the existing one-step geophysical model function algorithm to check the improvement. Results under the fully-developed sea and young-sea limited-fetch condition will be presented.

Keywords: TRITON, wind speed retrieval, calibration, two-step algorithm, wave age, ocean surface roughness

How to cite: Zhang, L., Chien, H., and Yeh, W.-H.: Wave-effect considering two-step wind speed retrieving algorithm for new GNSS-R satellite, TRITON, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-13690, https://doi.org/10.5194/egusphere-egu23-13690, 2023.

X5.387
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EGU23-15569
Minsang Kim, Myung-Sook Park, Jae-Hyun Ahn, Sun-Ju Lee, and Gm-Sil Kang

In ocean color remote sensing, the importance of developing and validating atmospheric correction and ocean bio-optical algorithms has been emphasized. However, if uncertainty remains in the fundamental procedure of converting the sensor signal to the top of the atmosphere (TOA) radiance, the errors will affects the overall reliability of the ocean satellite products.

The purpose of this study is to monitor the gain parameters of two on-board GOCI-II calibration using Solar Diffuser (SD) and Diffuser Aging Monitoring Device (DAMD) and to improve the accuracy of ocean color sensors for radiometric calibration (RC) quality at the TOA level. Our results show that the SD gains parameter tends to decrease with seasonal periodicity in all bands, confirming sensor degradation and solar azimuth angle over time. In addition to the current RC model using only SD gain in the relationship between the sensor-observed digital counts and TOA radiance, we develop an azimuth angle correction model and a sensor degradation correction model. Verification will be performed by calculating the TOA radiation applied with an improved RC model around the Korean Peninsula. It will contribute to providing more stable GOCI-II ocean color products for short-term and long-term analysis.

How to cite: Kim, M., Park, M.-S., Ahn, J.-H., Lee, S.-J., and Kang, G.-S.: Improving GOCI-II In-Orbit Radiometric Calibration for the Stability of Ocean Color Data, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-15569, https://doi.org/10.5194/egusphere-egu23-15569, 2023.

X5.388
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EGU23-16196
Davide Dionisi, Simone Bucci, Claudia Cesarini, Simone Colella, Davide D'Alimonte, Lorenzo Di Ciolo, Paolo Di Girolamo, Marco Di Paolantonio, Noemi Franco, Giacomo Gostinicchi, Tamito Kajiyama, Gian Luigi Liberti, Emanuele Organelli, and Rosalia Santoleri

During the last decade, new applications exploiting data from satellite borne lidar measurements demonstrated that these sensors can give valuable information about ocean optical properties [1,2,3]. Within this framework, COLOR (CDOM-proxy retrieval from aeOLus ObseRvations) consisted in a 18-month feasibility study approved by ESA within the Aeolus+ Innovation program. COLOR had the objective to evaluate and document the feasibility of deriving an in-water prototype product from the analysis of the signal acquired by the ESA Earth Explorer Wind Mission ADM-Aeolus (Atmospheric Dynamics Mission). In particular, COLOR project focused on the AEOLUS potential retrieval of the diffuse attenuation coefficient for downwelling irradiance (Kd [m-1]) from the ocean sub-surface backscattered component of the 355 nm received lidar signal.

The core activity of the project was the characterization of the signal from the AEOLUS ground bin through two parallel and strongly interacting activities: a) Radiative transfer numerical modelling; b) AEOLUS data analysis. The main result of the project will be presented together with the discussion of the perspectives of the satellite lidar missions dedicated to ocean color.

 

[1]  M. J. Behrenfeld et al. (2019). Global satellite-observed daily vertical migrations of ocean animals», Nature, vol. 576, n. 7786, Art. n. 7786, dic. 2019, doi: 10.1038/s41586-019-1796-9.

[2] Jamet, C., Ibrahim, A., Ahmad, Z., Angelini, F., Babin, M., Behrenfeld, M. J., et al. (2019). Going beyond standard ocean color observations: lidar and polarimetry. Front. Mar. Sci. 6:251. doi: 10.3389/fmars.2019.00251

[3]  D. Dionisi, V. E. Brando, G. Volpe, S. Colella, e R. Santoleri (2020). Seasonal distributions of ocean particulate optical properties from spaceborne lidar measurements in Mediterranean and Black sea», Remote Sens. Environ., vol. 247, pag. 111889, set. 2020, doi: 10.1016/j.rse.2020.111889.

How to cite: Dionisi, D., Bucci, S., Cesarini, C., Colella, S., D'Alimonte, D., Di Ciolo, L., Di Girolamo, P., Di Paolantonio, M., Franco, N., Gostinicchi, G., Kajiyama, T., Liberti, G. L., Organelli, E., and Santoleri, R.: Ocean color through satellite lidars: the COLOR project, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-16196, https://doi.org/10.5194/egusphere-egu23-16196, 2023.

X5.389
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EGU23-17412
Françoise Mertz, Cyril Germineaud, Laurent Soudarin, Vinca Rosmorduc, Catherine Schgounn, Florence Birol, Fernando Niño, and Thierry Guinle

AVISO (Archiving, Validation and Interpretation of Oceanographic Satellite data) is a service set up by the French spatial agency, CNES (Centre National d'Etudes Spatiales) to process, archive and distribute data and derived products from satellite missions. Its web portal AVISO+ (www.aviso.altimetry.fr) is the entry point to freely access more than 40 products from CNES and CTOH (Center for Topographic studies of the Ocean and Hydrosphere) not only for ocean-oriented applications but also for hydrology, coastal, biology and sea ice applications. In addition, the website proposes information (handbooks, use cases, outreach material, etc.) to discover the products and their use. New operational (or demonstration) products are regularly added to the AVISO+ catalogue. In 2022, the catalogue has been for instance enriched with several along-track and gridded SSALTO/DUACS experimental products, simulated SWOT Sea Surface Heights, costal products of Sea Level Anomaly (X-TRACK L2P), the Floating Sargassum detection index and climate indicators for the ocean heat content and earth energy imbalance. In 2023, new versions of the models produced by CNES and CLS for the Mean Sea Surface (MSS CNES-CLS 2022) and for the Mean Dynamic Topography (MDT CNES-CLS 2022) will be available for AVISO+ users, as well as the upcoming SWOT oceanographic products. A visualization tool will also be available online to discover the AVISO+ products, including the KaRIn measurements of the SWOT mission over the oceans. An overview of these new AVISO+ products will be presented.

How to cite: Mertz, F., Germineaud, C., Soudarin, L., Rosmorduc, V., Schgounn, C., Birol, F., Niño, F., and Guinle, T.: AVISO+: what’s new on the reference portal in satellite altimetry?, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-17412, https://doi.org/10.5194/egusphere-egu23-17412, 2023.

X5.390
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EGU23-17531
Morgaine McKibben, Stephanie Schollaert Uz, and Sherry Palacios

The multi- to hyperspectral evolution of satellite ocean color sensors is advancing space-based coastal ocean science and applications. Among anticipated new capabilities is satellite-based identification of phytoplankton groups. In this work the bio-optical Phytoplankton Detection with Optics (PHYDOTax) approach for deriving taxonomic class-level phytoplankton community composition (PCC, e.g. diatoms, dinoflagellates) from hyperspectral information (<= 10 nm spectral resolution) is evaluated in the Chesapeake Bay on the East Coast of the United States. PHYDOTax is among relatively few regionally customizable, optical PCC differentiation approaches available for optically complex water, but these features have not been tested beyond the California coastal regime where it was initially developed. Study goals include: 1) regional parameterization to an enclosed estuary, including novel addition of colored dissolved organic matter (CDOM) and non algal particles (NAP) to the algorithm, and 2) performance assessment using field-based remote sensing reflectance and pigment data from two cruise campaigns. Algorithm testing was conducted at spectral resolution settings relevant to hyperspectral sensors (e.g. 1nm, 5nm, 10nm) and with and without incorporation of CDOM and NAP. Statistical performance was typically robust for cryptophyte and cyanophyte phytoplankton groups with variable to poor results for dinoflagellate and diatom groups. Small, but significant, differences were observed in algorithm output at varied spectral resolutions, but no significant differences were observed in runs with or without CDOM and NAP. Based on these datasets, PHYDOTax is able to differentiate some phytoplankton groups in an estuary. The approach warrants further investigation with in estuaries and other optically complex regimes.

How to cite: McKibben, M., Schollaert Uz, S., and Palacios, S.: Testing a hyperspectral, bio-optical approach for identification of phytoplankton groups in the Chesapeake Bay, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-17531, https://doi.org/10.5194/egusphere-egu23-17531, 2023.

Posters virtual: Fri, 28 Apr, 10:45–12:30 | vHall CR/OS

Chairpersons: Aida Alvera-Azcárate, Tong Lee, Adrien Martin
vCO.17
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EGU23-17413
Francisco Silva, João Miguel Dias, and Renato Mendes

Internal waves are large-scale dynamics within the water column, where they have a significant impact on several processes: vertical displacement of water properties, sediments, and primary production. This study aims to record their spatio-temporal distribution in the Northeast Atlantic region using optical remote sensing techniques. It's also assessed if the Sentinel-3 platform employed in the study as an ocean color sensor benchmark is reliable with analogous results provided by SARs. Results show that the majority of internal waves were found in the Iberian Coast and the Azores Archipelago, in areas with peculiar bathymetry. At the same time, a seasonal pattern seemed to concentrate these occurrences between April and September where the sunglint impacts on data were more noticeable. This suggests that an effect that was previously considered undesirable in this type of data can actually be useful for observing internal waves on the ocean surface.

How to cite: Silva, F., Dias, J. M., and Mendes, R.: Distribution of Internal Waves in the Northeast Atlantic: An Ocean ColorRemote Sensing Analysis, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-17413, https://doi.org/10.5194/egusphere-egu23-17413, 2023.

vCO.18
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EGU23-14532
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ECS
Tianqi Xiao, Milad Asgarimehr, Caroline Arnold, Daixin Zhao, Lichao Mou, and Jens Wickert

GNSS Reflectometry (GNSS-R) has emerged as a novel remote sensing technique for monitoring geophysical parameters. GNSS signals reflected from the Earth’s surface are tracked and measured by low-mass receivers onboard small satellites, providing abundant information about the target with higher sampling frequency and special coverages. The main observable of GNSS-R is Delay-Doppler Maps (DDMs), which map signal power at a range of delay and Doppler frequency shifts. The conventional retrieval algorithms rely on the parametric regression approaches inverting observables derived from the DDMs to the ocean wind speed products. Thus, GNSS-R has become a new technique for ocean wind retrieval and hurricane monitoring. 
With the large datasets of cost-effective GNSS-R measurements available, the AI4GNSSR project (Artificial Intelligence for GNSS Reflectometry: Novel Remote Sensing of Ocean and Atmosphere) was proposed to implement Artificial Intelligence for characterizing geophysical parameters and investigating new applications and approaches for the GNSS-R technique. In this study, A global ocean wind speed dataset is created by processing the observables of NASA’s Cyclone GNSS (CyGNSS) mission. The primary implementations of AI algorithms have shown great potential in improving the quality of the existing wind speed products. The deep learning model based on convolutional layers and fully connected layers processes the input CyGNSS measurements and directly extracts features from bistatic radar cross section (BRCS) DDMs. This model achieves an overall RMSE of 1.31 m/s compared with the ERA5 reanalysis data on an unseen dataset and leads to an improvement of 28% in comparison to the operational retrieval algorithm.
Moreover, we found that data fusion with ancillary precipitation data is able to correct the rain effects, especially for high wind speed. For wind speeds larger than 16 m/s, our data fusion model outperforms the operational retrieval algorithm by 40%. For further validation of the model performance under extreme weather conditions, a case study of Hurricane Laura in August 2020 will be presented and discussed after a brief introduction to our models.

How to cite: Xiao, T., Asgarimehr, M., Arnold, C., Zhao, D., Mou, L., and Wickert, J.: Deep learning in spaceborne GNSS-R for ocean remote sensing: First insights from the AI4GNSSR project, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-14532, https://doi.org/10.5194/egusphere-egu23-14532, 2023.

vCO.19
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EGU23-3260
David Cotton

The objectives of the HYDROCOASTAL project, funded by the European Space Agency under the EO Science for Society programme, are to enhance our understanding of interactions between the inland water and coastal zone, between the coastal zone and the open ocean, and the small scale processes that govern these interactions. The project also aims to improve our capability to characterize the variation at different time scales of inland water storage, exchanges with the ocean and the impact on regional sea-level changes

 

To achieve these aims, the HYDROCOASTAL project team has developed and implemented new SAR altimeter processing algorithms for the coastal zone and inland waters, and with these processed Sentinel 3A, 3B and Cryosat-2 data to generate  to generate an initial 2-year Test Data Set for selected regions. The performance of these new algorithms has been evaluated, by statistical analyses and comparison against in situ data. From this analysis, the best performing algorithms have been identified and a processing scheme implemented to generate a global scale coastal zone and inland water altimeter data set.

 

A series of case studies are now assessing these products in terms of their scientific impacts.  All the produced data sets will be available on request to external researchers, and full descriptions of the processing algorithms are available via the project web-site

 

The presentation will provide an overview of the HYDROCOASTAL project, describe the different SAR altimeter processing algorithms that have been implemented and evaluated in the first phase of the project, and present results from the evaluation of the initial test data set. It will also present early results from  impact studies.

How to cite: Cotton, D.: Improving SAR Altimeter processing over the coastal zone and inland waters- the ESA HYDROCOASTAL project, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-3260, https://doi.org/10.5194/egusphere-egu23-3260, 2023.