Sathyendranath
Shubha
Sathyendranath
Shubha
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ArticleTowards an end-to-end analysis and prediction system for weather, climate, and marine applications in the Red Sea(American Meteorological Society, 2021-01-01) Hoteit, Ibrahim ; Abualnaja, Yasser ; Afzal, Shehzad ; Ait-El-Fquih, Boujemaa ; Akylas, Triantaphyllos ; Antony, Charls ; Dawson, Clint N. ; Asfahani, Khaled ; Brewin, Robert J. W. ; Cavaleri, Luigi ; Cerovecki, Ivana ; Cornuelle, Bruce D. ; Desamsetti, Srinivas ; Attada, Raju ; Dasari, Hari ; Sanchez-Garrido, Jose ; Genevier, Lily ; El Gharamti, Mohamad ; Gittings, John A. ; Gokul, Elamurugu ; Gopalakrishnan, Ganesh ; Guo, Daquan ; Hadri, Bilel ; Hadwiger, Markus ; Hammoud, Mohammed Abed ; Hendershott, Myrl ; Hittawe, Mohamad ; Karumuri, Ashok ; Knio, Omar ; Kohl, Armin ; Kortas, Samuel ; Krokos, George ; Kunchala, Ravi ; Issa, Leila ; Lakkis, Issam ; Langodan, Sabique ; Lermusiaux, Pierre F. J. ; Luong, Thang ; Ma, Jingyi ; Le Maitre, Olivier ; Mazloff, Matthew R. ; El Mohtar, Samah ; Papadopoulos, Vassilis P. ; Platt, Trevor ; Pratt, Lawrence J. ; Raboudi, Naila ; Racault, Marie-Fanny ; Raitsos, Dionysios E. ; Razak, Shanas ; Sanikommu, Sivareddy ; Sathyendranath, Shubha ; Sofianos, Sarantis S. ; Subramanian, Aneesh C. ; Sun, Rui ; Titi, Edriss ; Toye, Habib ; Triantafyllou, George ; Tsiaras, Kostas ; Vasou, Panagiotis ; Viswanadhapalli, Yesubabu ; Wang, Yixin ; Yao, Fengchao ; Zhan, Peng ; Zodiatis, GeorgeThe Red Sea, home to the second-longest coral reef system in the world, is a vital resource for the Kingdom of Saudi Arabia. The Red Sea provides 90% of the Kingdom’s potable water by desalinization, supporting tourism, shipping, aquaculture, and fishing industries, which together contribute about 10%–20% of the country’s GDP. All these activities, and those elsewhere in the Red Sea region, critically depend on oceanic and atmospheric conditions. At a time of mega-development projects along the Red Sea coast, and global warming, authorities are working on optimizing the harnessing of environmental resources, including renewable energy and rainwater harvesting. All these require high-resolution weather and climate information. Toward this end, we have undertaken a multipronged research and development activity in which we are developing an integrated data-driven regional coupled modeling system. The telescopically nested components include 5-km- to 600-m-resolution atmospheric models to address weather and climate challenges, 4-km- to 50-m-resolution ocean models with regional and coastal configurations to simulate and predict the general and mesoscale circulation, 4-km- to 100-m-resolution ecosystem models to simulate the biogeochemistry, and 1-km- to 50-m-resolution wave models. In addition, a complementary probabilistic transport modeling system predicts dispersion of contaminant plumes, oil spill, and marine ecosystem connectivity. Advanced ensemble data assimilation capabilities have also been implemented for accurate forecasting. Resulting achievements include significant advancement in our understanding of the regional circulation and its connection to the global climate, development, and validation of long-term Red Sea regional atmospheric–oceanic–wave reanalyses and forecasting capacities. These products are being extensively used by academia, government, and industry in various weather and marine studies and operations, environmental policies, renewable energy applications, impact assessment, flood forecasting, and more.
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ArticlePhytoplankton phenology indices in coral reef ecosystems : application to ocean-color observations in the Red Sea(Elsevier, 2015-02-18) Racault, Marie-Fanny ; Raitsos, Dionysios E. ; Berumen, Michael L. ; Brewin, Robert J. W. ; Platt, Trevor ; Sathyendranath, Shubha ; Hoteit, IbrahimPhytoplankton, at the base of the marine food web, represent a fundamental food source in coral reef ecosystems. The timing (phenology) and magnitude of the phytoplankton biomass are major determinants of trophic interactions. The Red Sea is one of the warmest and most saline basins in the world, characterized by an arid tropical climate regulated by the monsoon. These extreme conditions are particularly challenging for marine life. Phytoplankton phenological indices provide objective and quantitative metrics to characterize phytoplankton seasonality. The indices i.e. timings of initiation, peak, termination and duration are estimated here using 15 years (1997–2012) of remote sensing ocean-color data from the European Space Agency (ESA) Climate Change Initiative project (OC-CCI) in the entire Red Sea basin. The OC-CCI product, comprising merged and bias-corrected observations from three independent ocean-color sensors (SeaWiFS, MODIS and MERIS), and processed using the POLYMER algorithm (MERIS period), shows a significant increase in chlorophyll data coverage, especially in the southern Red Sea during the months of summer NW monsoon. In open and reef-bound coastal waters, the performance of OC-CCI chlorophyll data is shown to be comparable with the performance of other standard chlorophyll products for the global oceans. These features have permitted us to investigate phytoplankton phenology in the entire Red Sea basin, and during both winter SE monsoon and summer NW monsoon periods. The phenological indices are estimated in the four open water provinces of the basin, and further examined at six coral reef complexes of particular socio-economic importance in the Red Sea, including Siyal Islands, Sharm El Sheikh, Al Wajh bank, Thuwal reefs, Al Lith reefs and Farasan Islands. Most of the open and deeper waters of the basin show an apparent higher chlorophyll concentration and longer duration of phytoplankton growth during the winter period (relative to the summer phytoplankton growth period). In contrast, most of the reef-bound coastal waters display equal or higher peak chlorophyll concentrations and equal or longer duration of phytoplankton growth during the summer period (relative to the winter phytoplankton growth period). The ecological and biological significance of the phytoplankton seasonal characteristics are discussed in context of ecosystem state assessment, and particularly to support further understanding of the structure and functioning of coral reef ecosystems in the Red Sea.
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ArticleAn ocean-colour time series for use in climate studies: The experience of the ocean-colour climate change initiative (OC-CCI)(MDPI, 2019-10-03) Sathyendranath, Shubha ; Brewin, Robert J. W. ; Brockmann, Carsten ; Brotas, Vanda ; Calton, Ben ; Chuprin, Andrei ; Cipollini, Paolo ; Couto, André B. ; Dingle, James ; Doerffer, Roland ; Donlon, Craig ; Dowell, Mark ; Farman, Alex ; Grant, Michael ; Groom, Steven ; Horseman, Andrew ; Jackson, Thomas ; Krasemann, Hajo ; Lavender, Samantha ; Martinez-Vicente, Victor ; Mazeran, Constant ; Melin, Frederic ; Moore, Timothy S. ; Müller, Dagmar ; Regner, Peter ; Roy, Shovonlal ; Steele, Chris J. ; Steinmetz, François ; Swinton, John ; Taberner, Malcolm ; Thompson, Adam ; Valente, André ; Zühlke, Marco ; Brando, Vittorio ; Feng, Hui ; Feldman, Gene ; Franz, Bryan A. ; Frouin, Robert ; Gould, Richard ; Hooker, Stanford B. ; Kahru, Mati ; Kratzer, Susanne ; Mitchell, B. Greg ; Muller-Karger, Frank E. ; Sosik, Heidi M. ; Voss, Kenneth ; Werdell, Jeremy ; Platt, TrevorOcean colour is recognised as an Essential Climate Variable (ECV) by the Global Climate Observing System (GCOS); and spectrally-resolved water-leaving radiances (or remote-sensing reflectances) in the visible domain, and chlorophyll-a concentration are identified as required ECV products. Time series of the products at the global scale and at high spatial resolution, derived from ocean-colour data, are key to studying the dynamics of phytoplankton at seasonal and inter-annual scales; their role in marine biogeochemistry; the global carbon cycle; the modulation of how phytoplankton distribute solar-induced heat in the upper layers of the ocean; and the response of the marine ecosystem to climate variability and change. However, generating a long time series of these products from ocean-colour data is not a trivial task: algorithms that are best suited for climate studies have to be selected from a number that are available for atmospheric correction of the satellite signal and for retrieval of chlorophyll-a concentration; since satellites have a finite life span, data from multiple sensors have to be merged to create a single time series, and any uncorrected inter-sensor biases could introduce artefacts in the series, e.g., different sensors monitor radiances at different wavebands such that producing a consistent time series of reflectances is not straightforward. Another requirement is that the products have to be validated against in situ observations. Furthermore, the uncertainties in the products have to be quantified, ideally on a pixel-by-pixel basis, to facilitate applications and interpretations that are consistent with the quality of the data. This paper outlines an approach that was adopted for generating an ocean-colour time series for climate studies, using data from the MERIS (MEdium spectral Resolution Imaging Spectrometer) sensor of the European Space Agency; the SeaWiFS (Sea-viewing Wide-Field-of-view Sensor) and MODIS-Aqua (Moderate-resolution Imaging Spectroradiometer-Aqua) sensors from the National Aeronautics and Space Administration (USA); and VIIRS (Visible and Infrared Imaging Radiometer Suite) from the National Oceanic and Atmospheric Administration (USA). The time series now covers the period from late 1997 to end of 2018. To ensure that the products meet, as well as possible, the requirements of the user community, marine-ecosystem modellers, and remote-sensing scientists were consulted at the outset on their immediate and longer-term requirements as well as on their expectations of ocean-colour data for use in climate research. Taking the user requirements into account, a series of objective criteria were established, against which available algorithms for processing ocean-colour data were evaluated and ranked. The algorithms that performed best with respect to the climate user requirements were selected to process data from the satellite sensors. Remote-sensing reflectance data from MODIS-Aqua, MERIS, and VIIRS were band-shifted to match the wavebands of SeaWiFS. Overlapping data were used to correct for mean biases between sensors at every pixel. The remote-sensing reflectance data derived from the sensors were merged, and the selected in-water algorithm was applied to the merged data to generate maps of chlorophyll concentration, inherent optical properties at SeaWiFS wavelengths, and the diffuse attenuation coefficient at 490 nm. The merged products were validated against in situ observations. The uncertainties established on the basis of comparisons with in situ data were combined with an optical classification of the remote-sensing reflectance data using a fuzzy-logic approach, and were used to generate uncertainties (root mean square difference and bias) for each product at each pixel.
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ArticleA compilation of global bio-optical in situ data for ocean-colour satellite applications - version two(Copernicus Publications, 2019-07-15) Valente, André ; Sathyendranath, Shubha ; Brotas, Vanda ; Groom, Steven ; Grant, Michael ; Taberner, Malcolm ; Antoine, David ; Arnone, Robert ; Balch, William M. ; Barker, Kathryn ; Barlow, Ray ; Belanger, Simon ; Berthon, Jean-François ; Besiktepe, Sukru ; Borsheim, Yngve ; Bracher, Astrid ; Brando, Vittorio ; Canuti, Elisabetta ; Chavez, Francisco P. ; Cianca, Andrés ; Claustre, Hervé ; Clementson, Lesley ; Crout, Richard ; Frouin, Robert ; García-Soto, Carlos ; Gibb, Stuart W. ; Gould, Richard ; Hooker, Stanford B. ; Kahru, Mati ; Kampel, Milton ; Klein, Holger ; Kratzer, Susanne ; Kudela, Raphael M. ; Ledesma, Jesus ; Loisel, Hubert ; Matrai, Patricia A. ; McKee, David ; Mitchell, Brian G. ; Moisan, Tiffany ; Muller-Karger, Frank E. ; O'Dowd, Leonie ; Ondrusek, Michael ; Platt, Trevor ; Poulton, Alex J. ; Repecaud, Michel ; Schroeder, Thomas ; Smyth, Timothy ; Smythe-Wright, Denise ; Sosik, Heidi M. ; Twardowski, Michael ; Vellucci, Vincenzo ; Voss, Kenneth ; Werdell, Jeremy ; Wernand, Marcel ; Wright, Simon ; Zibordi, GiuseppeA global compilation of in situ data is useful to evaluate the quality of ocean-colour satellite data records. Here we describe the data compiled for the validation of the ocean-colour products from the ESA Ocean Colour Climate Change Initiative (OC-CCI). The data were acquired from several sources (including, inter alia, MOBY, BOUSSOLE, AERONET-OC, SeaBASS, NOMAD, MERMAID, AMT, ICES, HOT and GeP&CO) and span the period from 1997 to 2018. Observations of the following variables were compiled: spectral remote-sensing reflectances, concentrations of chlorophyll a, spectral inherent optical properties, spectral diffuse attenuation coefficients and total suspended matter. The data were from multi-project archives acquired via open internet services or from individual projects, acquired directly from data providers. Methodologies were implemented for homogenization, quality control and merging of all data. No changes were made to the original data, other than averaging of observations that were close in time and space, elimination of some points after quality control and conversion to a standard format. The final result is a merged table designed for validation of satellite-derived ocean-colour products and available in text format. Metadata of each in situ measurement (original source, cruise or experiment, principal investigator) was propagated throughout the work and made available in the final table. By making the metadata available, provenance is better documented, and it is also possible to analyse each set of data separately. This paper also describes the changes that were made to the compilation in relation to the previous version (Valente et al., 2016). The compiled data are available at https://doi.org/10.1594/PANGAEA.898188 (Valente et al., 2019).
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ArticleA compilation of global bio-optical in situ data for ocean-colour satellite applications(Copernicus Publications on behalf of the European Geosciences Union, 2016-06-03) Valente, André ; Sathyendranath, Shubha ; Brotas, Vanda ; Groom, Steven ; Grant, Michael ; Taberner, Malcolm ; Antoine, David ; Arnone, Robert ; Balch, William M. ; Barker, Kathryn ; Barlow, Ray ; Belanger, Simon ; Berthon, Jean-François ; Besiktepe, Sukru ; Brando, Vittorio ; Canuti, Elisabetta ; Chavez, Francisco P. ; Claustre, Hervé ; Crout, Richard ; Frouin, Robert ; García-Soto, Carlos ; Gibb, Stuart W. ; Gould, Richard ; Hooker, Stanford B. ; Kahru, Mati ; Klein, Holger ; Kratzer, Susanne ; Loisel, Hubert ; McKee, David ; Mitchell, Brian G. ; Moisan, Tiffany ; Muller-Karger, Frank E. ; O'Dowd, Leonie ; Ondrusek, Michael ; Poulton, Alex J. ; Repecaud, Michel ; Smyth, Timothy ; Sosik, Heidi M. ; Twardowski, Michael ; Voss, Kenneth ; Werdell, Jeremy ; Wernand, Marcel ; Zibordi, GiuseppeA compiled set of in situ data is important to evaluate the quality of ocean-colour satellite-data records. Here we describe the data compiled for the validation of the ocean-colour products from the ESA Ocean Colour Climate Change Initiative (OC-CCI). The data were acquired from several sources (MOBY, BOUSSOLE, AERONET-OC, SeaBASS, NOMAD, MERMAID, AMT, ICES, HOT, GeP&CO), span between 1997 and 2012, and have a global distribution. Observations of the following variables were compiled: spectral remote-sensing reflectances, concentrations of chlorophyll a, spectral inherent optical properties and spectral diffuse attenuation coefficients. The data were from multi-project archives acquired via the open internet services or from individual projects, acquired directly from data providers. Methodologies were implemented for homogenisation, quality control and merging of all data. No changes were made to the original data, other than averaging of observations that were close in time and space, elimination of some points after quality control and conversion to a standard format. The final result is a merged table designed for validation of satellite-derived ocean-colour products and available in text format. Metadata of each in situ measurement (original source, cruise or experiment, principal investigator) were preserved throughout the work and made available in the final table. Using all the data in a validation exercise increases the number of matchups and enhances the representativeness of different marine regimes. By making available the metadata, it is also possible to analyse each set of data separately. The compiled data are available at doi:10.1594/PANGAEA.854832 (Valente et al., 2015).
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ArticleDominant timescales of variability in global satellite chlorophyll and SST revealed with a MOving Standard deviation Saturation (MOSS) approach(Elsevier, 2022-12-23) Jönsson, Bror F. ; Salisbury, Joseph ; Atwood, Elizabeth C. ; Sathyendranath, Shubha ; Mahadevan, AmalaA novel method to assess dominant timescales of variability in sparse satellite data.•Applied on Chl and SST, the method shows unexpected global patterns of variability.•Shortest timescales can be found in subtropical gyres and upwelling regions.•SST generally have much longer timescales of variability than Chl.Satellite-derived sea surface temperature (SST) and chlorophyll (Chl) datasets have been invaluable for estimating the oceanic primary production, air-sea heat exchange, and the spatial and seasonal patterns in their variability. However, data gaps, resulting from clouds and other factors, reduce coverage unevenly (to just about 20%) and make it difficult to analyze the temporal variability of Chl and SST on sub-seasonal time scales. Here, we present a MOving Standard deviation Saturation (MOSS) method to enable the analysis of sparse time series (with as little as 10% of the data). We apply the method to identify the dominating (sub-annual) timescales of variability, τd, for SST and Chl in every region. We find that τd values for Chl and SST are not consistent or correlated with each other over large areas, and in general, SST varies on longer timescales than Chl, i.e. τd(SST) >τd(Chl). There is a threefold variability in τd for SST and Chl even within regions that are traditionally considered to be biogeographically homogeneous. The largest τd for Chl is generally found on the equatorial side of the trade wind belts, whereas the smallest τd are found in the tropical Pacific and near coasts, especially where upwelling is common. If the temporal variability in Chl and SST were driven largely by ocean dynamics or advection by the flow, regional patterns of τd for SST and Chl should co-vary. This is seen in coastal upwelling zones, but more broadly, the lack of coherence between τd(Chl) and τd(SST) suggests that biological processes, such as phytoplankton growth and loss, decouple the timescales of Chl variability from those of SST and generate shorter term variability in Chl.