World Library  


Add to Book Shelf
Flag as Inappropriate
Email this Book

Assimilating Globcolour Ocean Colour Data Into a Pre-operational Physical-biogeochemical Model : Volume 8, Issue 5 (05/09/2012)

By Ford, D. A.

Click here to view

Book Id: WPLBN0004020608
Format Type: PDF Article :
File Size: Pages 21
Reproduction Date: 2015

Title: Assimilating Globcolour Ocean Colour Data Into a Pre-operational Physical-biogeochemical Model : Volume 8, Issue 5 (05/09/2012)  
Author: Ford, D. A.
Volume: Vol. 8, Issue 5
Language: English
Subject: Science, Ocean, Science
Collections: Periodicals: Journal and Magazine Collection, Copernicus GmbH
Historic
Publication Date:
2012
Publisher: Copernicus Gmbh, Göttingen, Germany
Member Page: Copernicus Publications

Citation

APA MLA Chicago

Barciela, R. M., Lea, D., Edwards, K. P., Martin, M. J., Ford, D. A., & Demaria, J. (2012). Assimilating Globcolour Ocean Colour Data Into a Pre-operational Physical-biogeochemical Model : Volume 8, Issue 5 (05/09/2012). Retrieved from http://worldlibrary.net/


Description
Description: Met Office, FitzRoy Road, Exeter, EX1 3PB, UK. As part of the GlobColour project, daily chlorophyll a observations, derived using remotely sensed ocean colour data from the MERIS, MODIS and SeaWiFS sensors, are produced. The ability of these products to be assimilated into a pre-operational global coupled physical-biogeochemical model has been tested, on both a hindcast and near-real-time basis, and the impact on the system assessed. The assimilation was found to immediately and considerably improve the bias, root mean square error and correlation of modelled surface chlorophyll concentration compared to the GlobColour observations, an improvement which was sustained throughout the year and in every ocean basin. Errors against independent in situ chlorophyll observations were also reduced, both at and beneath the ocean surface. However, the model fit to in situ observations was not consistently better than that of climatology, due to errors in the underlying model. The assimilation scheme used is multivariate, updating all biogeochemical model state variables at all depths. The other variables were not degraded by the assimilation, with annual mean surface fields of nutrients, alkalinity and carbon variables remaining of similar quality compared to climatology. There was evidence of improved representation of zooplankton concentration, and reduced errors were seen against in situ observations of nitrate and pCO2, but too few observations were available to conclude about global model skill. The near-real-time GlobColour products were found to be sufficiently reliable for operational purposes, and of benefit to both operational-style systems and reanalyses.

Summary
Assimilating GlobColour ocean colour data into a pre-operational physical-biogeochemical model

Excerpt
Allen, J. I., Eknes, M., and Evensen, G.: An Ensemble Kalman Filter with a complex marine ecosystem model: hindcasting phytoplankton in the Cretan Sea, Ann. Geophys., 21, 399–411, doi:10.5194/angeo-21-399-2003, 2003.; Anderson, L. A., Robinson, A. R., and Lozano, C. J.: Physical and biological modeling in the Gulf Stream region – Part 1: Data assimilation methodology, Deep-Sea Res. Pt. I, 47, 1787–1827, 2000.; Anderson, T. R.: A spectrally averaged model of light penetration and photosynthesis, Limnol. Oceanogr., 38, 1403–1419, 1993.; Bailey, S. W. and Werdell, P. J.: A multi-sensor approach for the on-orbit validation of ocean color satellite data products, Remote Sens. Environ., 102, 12–23, 2006.; Barnes, C., Irigoien, X., De Oliveira, J. A. A., Maxwell, D., and Jennings, S.: Predicting marine phytoplankton community size structure from empirical relationships with remotely sensed variables, J. Plankton Res., 33, 13–24, 2011.; Berger, H., Langland, R., Velden, C. S., Reynolds, C. A., and Pauley, P. M.: Impact of enhanced satellite-derived atmospheric motion vector observations on numerical tropical cyclone track forecasts in the Western North Pacific during TPARC/TCS-08, J. Appl. Meteorol. Clim., 50, 2309–2318, 2011.; Berx, B., Dickey-Collas, M., Skogen, M. D., De Roeck, Y.-H., Klein, H., Barciela, R., Forster, R. M., Dombrowsky, E., Huret, M., Payne, M., Sagarminaga, Y., and Schrum, C: Does operational oceanography address the needs of fisheries and applied environmental scientists?, Oceanography, 24, 166–171, 2011.; Beşiktepe, Ş. T., Lermusiaux, P. F. J., and Robinson, A. R.: Coupled physical and biogeochemical data-driven simulations of Massachusetts Bay in late summer: real-time and postcruise data assimilation, J. Marine Syst., 40, 171–212, 2003.; Bloom, S. C., Takacs, L. L., da Silva, A. M., and Ledvina, D.: Data assimilation using incremental analysis updates, Mon. Weather Rev., 124, 1256–1271, 1996.; Blower, J. D., Blanc, F., Clancy, M., Cornillon, P., Donlon, C., Hacker, P., Haines, K., Hankin, S. C., Loubrieu, T., Pouliquen, S., Price, M., Pugh, T. F., and Srinivasan, A.: Serving GODAE data and products to the ocean community, Oceanography, 22, 70–79, 2009.; Bocquet, M., Pires, C. A., and Wu, L.: Beyond Gaussian statistical modeling in geophysical data assimilation, Mon. Weather Rev., 138, 2997–3023, 2010.; Brankart, J.-M., Testut, C.-E., Béal, D., Doron, M., Fontana, C., Meinvielle, M., Brasseur, P., and Verron, J.: Towards an improved description of ocean uncertainties: effect of local anamorphic transformations on spatial correlations, Ocean Sci., 8, 121–142, doi:10.5194/os-8-121-2012, 2012.; Campbell, J. W.: The lognormal distribution as a model for bio-optical variability in the sea, J. Geophys. Res., 100, 13237–13254, 1995.; Brasseur, P., Gruber, N., Barciela, R., Brander, K., Doron, M., El Moussaoui, A., Hobday, A. J., Huret, M., Kremeur, A. S., Lehodey, P., Matear, R., Moulin, C., Murtugudde, R., Senina, I., and Svendsen, E.: Integrating biogeochemistry and ecology into ocean data assimilation systems, Oceanography, 22, 206–215, 2009.; Carmillet, V., Brankart, J.-M., Brasseur, P., Drange, H., Evensen, G., and Verron, J.: A singular evolutive extended Kalman filter to assimilate ocean color data in a coupled physical-biochemical model of the North Atlantic ocean, Ocean Model., 3, 167–192, 2001.; Ciavatta, S., Torres, R., Saux-Picart, S., and Allen, J. I.: Can ocean color assimilation improve biogeochemical hindcasts in shelf seas?, J. Geophys. Res., 116, C12043, doi:10.1029/2011JC007219, 2011.; Cox, P. M., Betts, R. A., Jones, C. D., Spall, S. A., and Totterdell, I. J.: Acceleration of global warming due to carbon-cycle feedbacks in a coupled climate model, Nature, 408, 184–187, 2000.; Cummings, J.

 

Click To View

Additional Books


  • Dineof Reconstruction of Clouded Images ... (by )
  • Transport of Antarctic Bottom Water Thro... (by )
  • Mapping Turbidity Layers Using Seismic O... (by )
  • A New Parameterisation of Salinity Advec... (by )
  • Numerical Simulation and Decomposition o... (by )
  • High Resolution Satellite Turbidity and ... (by )
  • Transient Residence and Exposure Times :... (by )
  • Pre-operational Short-term Forecasts for... (by )
  • Effects of the 2003 European Heatwave on... (by )
  • Technical Note: a Low Cost Unmanned Aeri... (by )
  • The Land-ice Contribution to 21St Centur... (by )
  • Upper Ocean Stratification and Sea Ice G... (by )
Scroll Left
Scroll Right

 



Copyright © World Library Foundation. All rights reserved. eBooks from World Library are sponsored by the World Library Foundation,
a 501c(4) Member's Support Non-Profit Organization, and is NOT affiliated with any governmental agency or department.