The CMEMS GlobColour chlorophyll &amp;lt;i&amp;gt;a&amp;lt;/i&amp;gt; product based on satellite observation: multi-sensor merging and flagging strategies

Garnesson, Philippe; Mangin, Antoine; d’Andon, O. Fanton; Demaria, Julien; Bretagnon, Marine

doi:10.5194/os-15-819-2019

Cited by 98 publications

(73 citation statements)

References 13 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…which is based on a spatial and temporal interpolation of the level 3 product at a spatial resolution of 0.0417 Garnesson et al, 2019b, a). The chl-a analyses involve multiple chl-a algorithms, i.e., CI algorithm for oligotrophic waters (Hu et al, 2012) and OC5 algorithm for mesotrophic and coastal waters (Gohin et al, 2002;Gohin, 2011).…”

Section: Response Variablesmentioning

confidence: 99%

“…The CI and OC5 continuity is ensured using the same approach as that utilized by NASA. When the chl-a concentration is in the range from 0.15 to 0.2 mg m −3 , a linear interpolation is used (Garnesson et al, 2019b). The algorithm validation has shown a good relationship between in situ measurements and satellite observations of chl-a concentration, while daily level 4 products in general show a low bias (0.04) and a coefficient of determination of 0.71 at global scale (Garnesson et al, 2019a).…”

Section: Response Variablesmentioning

confidence: 99%

See 1 more Smart Citation

Oceanic response to the consecutive Hurricanes Dorian and Humberto (2019) in the Sargasso Sea

Avila-Alonso¹,

Baetens²,

Cárdenas³

et al. 2020

Preprint

View full text Add to dashboard Cite

Abstract. Understanding the oceanic response to tropical cyclones (TCs) is of importance for studies on climate change. Although the oceanic effects induced by individual TCs have been extensively investigated, studies on the oceanic response to the passage of consecutive TCs are rare. In this work, we assess the upper oceanic response to the passage of the Hurricanes Dorian and Humberto over the western Sargasso Sea in 2019 using satellite remote sensing and modelled data. We found that the combined effects of these slow-moving TCs led to an increased oceanic response during the third and fourth post-storm weeks of Dorian (accounting for both Dorian and Humberto effects) because of the induced mixing and upwelling at this time. Overall, anomalies of sea surface temperature, ocean heat content and mean temperature from the sea surface to a depth of 100 m were a 50, 63 and 57% smaller (more negative) in the third/fourth post-storm weeks than in the first/second poststorm weeks (accounting only for Dorian effects) of Dorian, respectively, while surface chlorophyll-a (chl-a) concentration anomalies, the mean ch-a concentration in the euphotic zone and the chl-a concentration in the deep chlorophyll maximum were 16, 4 and 16% higher in the third/fourth post-storm weeks than in the first/second post-storm weeks, respectively. The sea surface cooling and increased biological response induced by these TCs were significantly higher (Mann-Whitney test p < 0.05) as compared to climatological records. Our climatological analysis reveals that the strongest TC-induced oceanographic variability in the western Sargasso Sea can be associated with the occurrence of consecutive TCs and long-lasting TC forcing.

show abstract

Section: Response Variablesmentioning

confidence: 99%

Section: Response Variablesmentioning

confidence: 99%

Oceanic response to the consecutive Hurricanes Dorian and Humberto (2019) in the Sargasso Sea

Avila-Alonso¹,

Baetens²,

Cárdenas³

et al. 2020

Preprint

View full text Add to dashboard Cite

show abstract

“…The ρ are used in this study to replace Chl and b bp satellite estimations used in SOCA2016, in order to avoid additional input variability due to ocean color algorithms errors. For the long-term vision, PAR and ρ data come from GlobColour satellite multi-mission data (Garnesson et al, 2019) that were downloaded from the Copernicus Marine Environment Monitoring Service (CMEMS, http://marine.copernicus.eu/). The matchup was done using the value of the closest pixel available with a 5-day window (before and after the observation) and within a 5x5 pixel grid.…”

Section: Bgc-argo and Satellite Matchup Databasementioning

confidence: 99%

Estimation of Oceanic Particulate Organic Carbon With Machine Learning

Sauzède

Johnson

Claustre

et al. 2020

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

View full text Add to dashboard Cite

Abstract. Understanding and quantifying ocean carbon sinks of the planet is of paramount relevance in the current scenario of global change. Particulate organic carbon (POC) is a key biogeochemical parameter that helps us characterize export processes of the ocean. Ocean color observations enable the estimation of bio-optical proxies of POC (i.e. particulate backscattering coefficient, bbp) in the surface layer of the ocean quasi-synoptically. In parallel, the Argo program distributes vertical profiles of the physical properties with a global coverage and a high spatio-temporal resolution. Merging satellite ocean color and Argo data using a neural networkbased method has already shown strong potential to infer the vertical distribution of bio-optical properties at global scale with high space-time resolution. This method is trained and validated using a database of concurrent vertical profiles of temperature, salinity, and bio-optical properties, i.e. bbp, collected by Biogeochemical-Argo (BGC-Argo) floats, matched up with satellite ocean color products. The present study aims at improving this method by 1) using a larger dataset from BGC-Argo network since 2016 for training, 2) using additional inputs such as altimetry data, which provide significant information on mesoscale processes impacting the vertical distribution of bbp, 3) improving the vertical resolution of estimation, and 4) examining the potential of alternative machine learning-based techniques. As a first attempt with the new data, we used some feature-specific preprocessing routines followed by a Multi-Output Random Forest algorithm on two regions with different ocean dynamics: North Atlantic and Subtropical Gyres. The statistics and the bbp profiles obtained from the validation floats show promising results and suggest this direction is worth investigating even further at global scale.

show abstract

“…Two dailies Chlorophyll-a products are distributed: one limited to the daily observations (called L3), and the other based on a space-time interpolation: the "Cloud Free" (called L4). Products of daily surface chl-a datasets are based on the merging of the sensors SeaWiFS, MODIS, MERIS, VIIRS-SNPP&JPSS1, and OLCI-S3A&S3B with the spatial resolution is 4 km (Copernicus Marine Service, 2019; Garnesson et al, 2019).…”

Section: Datamentioning

confidence: 99%

Seasonal Upwelling in the Northern Arafura Sea from Multi-datasets in 2017

Atmadipoera¹,

Almatin²,

Zuraida³

et al. 2020

JST

View full text Add to dashboard Cite

Seasonal upwelling phenomenon in the Arafura Sea plays an important role on supplying upwelled nutrient-rich water to sustain biogeochemistry processes and thus contributes to high marine primary productivity and fisheries resources in this region. The objective of this research was to investigate physical process and dynamics of upwelling by analyzing stratification of seawater properties, evolution of surface ocean-atmosphere parameters, and current structure and transport volume in the northern Arafura Sea. The multi-datasets in 2017 were used in this study, acquired from field CTD measurement, satellite-derived sea surface parameters, and the ocean general circulation model outputs, which were processed and analyzed using the available standard procedure. It was found that upwelling event was associated with a sharp subsurface thin layer that upsloping isotherms (23.5 - 25.5°C), isohalines (33.50 - 34.25 psu), and isopycnals (21.8 - 23.2 kg/m³) from the shelf-break region to the inner shelf region at a distance of approximately 167 km. This barrier layer separated the first surface mixed layer from the second mixed layer beneath the subsurface layer. The model suggests that the current in these two layers is in the opposite direction, to the west in the first layer as a response to the Ekman drift and to the east in the second layer as a current extension from deep Aru basin. Therefore, upwelling dynamics here is not only generated by the southeasterly monsoon winds from May (onset) to November (termination) that transport warm and fresh surface water away from the shelf, but also modulated by the presence of strong inflow currents beneath subsurface that supply colder saltier nutrient-rich water into the shelf. During the upwelling period, mean transport volume in the upper 25 m depth between Aru and Papua at 134.25°E was -0.28 (±0.34) Sv (westward), but the transport volume between 25m and 110m depth was +1.06 (±0.29) Sv (eastward), suggesting this inflow may regulate the upwelling and supply Arafura shelf water.

show abstract

The CMEMS GlobColour chlorophyll <i>a</i> product based on satellite observation: multi-sensor merging and flagging strategies

Cited by 98 publications

References 13 publications

Oceanic response to the consecutive Hurricanes Dorian and Humberto (2019) in the Sargasso Sea

Oceanic response to the consecutive Hurricanes Dorian and Humberto (2019) in the Sargasso Sea

Estimation of Oceanic Particulate Organic Carbon With Machine Learning

Seasonal Upwelling in the Northern Arafura Sea from Multi-datasets in 2017

Contact Info

Product

Resources

About