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| Mcphaden, Mj; Foltz, Jr; Lee, T.; Murty, V. S. N.; Ravichandran, Muthalagu; Vecchi, Ga; Vialard, Jerome; Wiggert, J.d.; Yu, L.. |
| Cyclone Nargis (Figure 1a) made landfall in Myanmar (formerly Burma) on 2 May 2008 with sustained winds of approximately 210 kilometers per hour, equivalent to a category 3–4 hurricane. In addition, Nargis brought approximately 600 millimeters of rain and a storm surge of 3–4 meters to the low-lying and densely populated Irrawaddy River delta. In its wake, the storm left an estimated 130,000 dead or missing and more than $10 billion in economic losses. It was the worst natural disaster to strike the Indian Ocean region since the 26 December 2004 tsunami and the worst recorded natural disaster ever to affect Myanmar. |
| Tipo: Text |
Palavras-chave: IndOOS; Bay of Bengal; Cyclone Nargis. |
| Ano: 2009 |
URL: http://archimer.ifremer.fr/doc/00185/29643/28009.pdf |
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| Wickert, Jens; Andersen, O.; Bandeiras, J.; Bertino, L.; Cardellach, E.; Camps, A.; Catarino, N.; Chapron, Bertrand; Foti, G.; Gommenginger, C.; Hatton, J.; Hoeg, P.; Jaeggi, A.; Kern, M.; Lee, T.; Martin-neira, M.; Park, H.; Pierdicca, N.; Rosello, J.; Semmling, M.; Shum, C. K.; Zuffada, C.; Soulat, F.; Sousa, A.; Xi, J.. |
| GEROS-ISS (GEROS hereafter) stands for GNSS REflectometry, Radio Occultation and Scatterometry onboard the International Space Station. It is a scientific experiment, proposed to the European Space Agency (ESA) in 2011 for installation aboard the ISS. The main focus of GEROS is the dedicated use of signals from the currently available Global Navigation Satellite Systems (GNSS) for remote sensing of the System Earth with focus to Climate Change characterisation. The GEROS mission idea and the current status are briefly reviewed. |
| Tipo: Text |
Palavras-chave: GNSS; Reflectometry; ISS; Sea Surface Height; Oceanography. |
| Ano: 2016 |
URL: http://archimer.ifremer.fr/doc/00377/48799/49606.pdf |
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| Balmaseda, M. A.; Hernandez, F.; Storto, A.; Palmer, M. D.; Alves, O.; Shi, L.; Smith, G. C.; Toyoda, T.; Valdivieso, M.; Barnier, B.; Behringer, D.; Boyer, T.; Chang, Y-s.; Chepurin, G. A.; Ferry, N.; Forget, Gael; Fujii, Y.; Good, S.; Guinehut, S.; Haines, K.; Ishikawa, Y.; Keeley, S.; Koehls, A.; Lee, T.; Martin, M. J.; Masina, S.; Masuda, S.; Meyssignac, B.; Mogensen, K.; Parent, L.; Peterson, K. A.; Tang, Y. M.; Yin, Y.; Vernieres, G.; Wang, X.; Waters, J.; Wedd, R.; Wang, O.; Xue, Y.; Chevallier, M.; Lemieux, J-f.; Dupont, F.; Kuragano, T.; Kamachi, M.; Awaji, T.; Caltabiano, A.; Wilmer-becker, K.; Gaillard, Fabienne. |
| Uncertainty in ocean analysis methods and deficiencies in the observing system are major obstacles for the reliable reconstruction of the past ocean climate. The variety of existing ocean reanalyses is exploited in a multi-reanalysis ensemble to improve the ocean state estimation and to gauge uncertainty levels. The ensemble-based analysis of signal-to-noise ratio allows the identification of ocean characteristics for which the estimation is robust (such as tropical mixed-layer-depth, upper ocean heat content), and where large uncertainty exists (deep ocean, Southern Ocean, sea ice thickness, salinity), providing guidance for future enhancement of the observing and data assimilation systems. |
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| Ano: 2015 |
URL: http://archimer.ifremer.fr/doc/00280/39090/37655.pdf |
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| Beal, L. M.; Vialard, J.; Roxy, M.k.; Li, J.; Andres, M.; Annamalai, H.; Feng, M.; Han, W.; Hood, R.; Lee, T.; Lengaigne, Matthieu; Lumpkin, R.; Masumoto, Y.; Mcphaden, M.j.; Ravichandran, M.; Shinoda, T.; Sloyan, B.m.; Strutton, P.g.; Subramanian, A.c.; Tozuka, T.; Ummenhofer, C.c.; Unnikrishnan, A.s.; Wiggert, J.; Yu, L.; Cheng, L.; Desbruyères, Damien; Parvathi, V. |
| The Indian Ocean Observing System (IndOOS), established in 2006, is a multi-national network of sustained oceanic measurements that underpin understanding and forecasting of weather and climate for the Indian Ocean region and beyond. Almost one-third of humanity indeed lives around the Indian Ocean, many in countries dependent on fisheries and rain-fed agriculture that are vulnerable to climate variability and extremes. The Indian Ocean alone has absorbed a quarter of the global oceanic heat uptake over the last two decades and the fate of this heat and its impact on future change is unknown. Climate models project accelerating sea level rise, more frequent extremes in monsoon rainfall, and decreasing oceanic productivity. In view of these new scientific... |
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| Ano: 2020 |
URL: https://archimer.ifremer.fr/doc/00644/75658/76530.pdf |
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| Reul, Nicolas; Grodsky, S.a.; Arias, M.; Boutin, J.; Catany, R.; Chapron, Bertrand; D'Amico, F; Dinnat, E.; Donlon, C.; Fore, A.; Fournier, Severine; Guimbard, Sebastien; Hasson, A.; Kolodziejczyk, Nicolas; Lagerloef, G.; Lee, T.; Le Vine, D.m.; Lindstrom, E.; Maes, Christophe; Mecklenburg, S.; Meissner, T.; Olmedo, E.; Sabia, R.; Tenerelli, Joseph; Thouvenin-masson, C.; Turiel, A.; Vergely, J.l.; Vinogradova, N.; Wentz, F.; Yueh, S.. |
| Operated since the end of 2009, the European Space Agency (ESA) Soil Moisture and Ocean Salinity (SMOS) satellite mission is the first orbiting radiometer that collects regular and global observations from space of two Essential Climate Variables of the Global Climate Observing System: Sea Surface Salinity (SSS) and Soil Moisture. The National Aeronautics and Space Administration (NASA) Aquarius mission, with the primary objective to provide global SSS measurements from space operated from mid-2011 to mid-2015. NASA's Soil Moisture Active-Passive (SMAP) mission, primarily dedicated to soil moisture measurements, but also monitoring SSS, has been operating since early 2015. The primary sensors onboard these three missions are passive microwave radiometers... |
| Tipo: Text |
Palavras-chave: Sea surface salinity; Ocean microwave remote sensing; Radiometer; L-band; SMOS; Aquarius/SAC-D; SMAP. |
| Ano: 2020 |
URL: https://archimer.ifremer.fr/doc/00615/72750/71894.pdf |
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| Foltz, G. R.; Brandt, P.; Richter, I.; Rodríguez-fonseca, B.; Hernandez, F.; Dengler, M.; Rodrigues, R. R.; Schmidt, J. O.; Yu, L.; Lefevre, N.; Da Cunha, L. Cotrim; Mcphaden, M. J.; Araujo, M.; Karstensen, J.; Hahn, J.; Martín-rey, M.; Patricola, C. M.; Poli, P.; Zuidema, P.; Hummels, R.; Perez, Rc; Hatje, V.; Lübbecke, J. F.; Polo, I.; Lumpkin, R.; Bourlès, Bernard; Asuquo, F. E.; Lehodey, P.; Conchon, A.; Chang, P.; Dandin, P.; Schmid, C.; Sutton, A.; Giordani, H.; Xue, Y.; Illig, S.; Losada, T.; Grodsky, S. A.; Gasparin, F.; Lee, T.; Mohino, E.; Nobre, P.; Wanninkhof, R.; Keenlyside, N.; Garcon, V.; Sánchez-gómez, E.; Nnamchi, H. C.; Drévillon, M.; Storto, A.; Remy, E.; Lazar, A.; Speich, S.; Goes, M.; Dorrington, T.; Johns, W. E.; Moum, J. N.; Robinson, C.; Perruche, Coralie; De Souza, R. B.; Gaye, A. T.; López-parages, J.; Monerie, P.-a.; Castellanos, P.; Benson, N. U.; Hounkonnou, M. N.; Duhá, J. Trotte; Laxenaire, R.; Reul, Nicolas. |
| The tropical Atlantic is home to multiple coupled climate variations covering a wide range of timescales and impacting societally relevant phenomena such as continental rainfall, Atlantic hurricane activity, oceanic biological productivity, and atmospheric circulation in the equatorial Pacific. The tropical Atlantic also connects the southern and northern branches of the Atlantic meridional overturning circulation and receives freshwater input from some of the world’s largest rivers. To address these diverse, unique, and interconnected research challenges, a rich network of ocean observations has developed, building on the backbone of the Prediction and Research Moored Array in the Tropical Atlantic (PIRATA). This network has evolved naturally over time... |
| Tipo: Text |
Palavras-chave: Tropical Atlantic Ocean; Observing system; Weather; Climate; Hurricanes; Biogeochemistry; Ecosystems; Coupled model bias. |
| Ano: 2019 |
URL: https://archimer.ifremer.fr/doc/00494/60612/64096.pdf |
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| Reul, Nicolas; Chapron, Bertrand; Lee, T.; Donlon, Craig; Boutin, Jacqueline; Alory, G.. |
| Measurements from the Soil Moisture Ocean Salinity (SMOS) satellite acquired during 2012 in the western North Atlantic are used to reveal the evolution of the sea surface salinity (SSS) structure of the meandering Gulf Stream with an unprecedented space and time resolution. Combined with in situ surface and profile measurements, satellite-derived surface currents, sea surface height (SSH), surface temperature (SST), and chlorophyll (Chl) data, SMOS SSS observations are shown to coherently delineate meanders pinching off from the current to form well-identified salty- (warm-) and fresh- (cold-) core Gulf Stream rings. A covariance analysis at two locations along the separated Gulf stream path (south of Nova Scotia and in the Gulf Stream Extension) reveals a... |
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| Ano: 2014 |
URL: http://archimer.ifremer.fr/doc/00188/29974/28427.pdf |
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| Boutin, J.; Chao, Y.; Asher, W. E.; Delcroix, T.; Drucker, R.; Drushka, K.; Kolodziejczyk, Nicolas; Lee, T.; Reul, Nicolas; Reverdin, G.; Schanze, J.; Soloviev, A.; Yu, L.; Anderson, J.; Brucker, L.; Dinnat, E.; Santos-garcia, A.; Jones, W. L.; Maes, C.; Meissner, T.; Tang, W.; Vinogradova, N.; Ward, B.. |
| Remote sensing of salinity using satellite-mounted microwave radiometers provides new perspectives for studying ocean dynamics and the global hydrological cycle. Calibration and validation of these measurements is challenging because satellite and in situ methods measure salinity differently. Microwave radiometers measure the salinity in the top few centimeters of the ocean, whereas most in situ observations are reported below a depth of a few meters. Additionally, satellites measure salinity as a spatial average over an area of about 100x100 km2. In contrast, in situ sensors provide pointwise measurements at the location of the sensor. Thus, the presence of vertical gradients in, and horizontal variability of, sea surface salinity complicates comparing... |
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| Ano: 2016 |
URL: https://archimer.ifremer.fr/doc/00300/41095/40268.pdf |
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