D
David L. T. Anderson
Researcher at European Centre for Medium-Range Weather Forecasts
Publications - 88
Citations - 5620
David L. T. Anderson is an academic researcher from European Centre for Medium-Range Weather Forecasts. The author has contributed to research in topics: Data assimilation & Sea surface temperature. The author has an hindex of 40, co-authored 88 publications receiving 5356 citations. Previous affiliations of David L. T. Anderson include University of Oxford.
Papers
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Journal ArticleDOI
A review of the predictability and prediction of ENSO
Mojib Latif,David L. T. Anderson,Tim P. Barnett,Mark A. Cane,Richard Kleeman,Ants Leetmaa,James J. O'Brien,Anthony Rosati,Edwin K. Schneider +8 more
TL;DR: A hierarchy of El Nino-Southern Oscillation (ENSO) prediction schemes has been developed during the Tropical Ocean-Global Atmosphere (TOGA) program which includes statistical schemes and physical models as mentioned in this paper.
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The prospects for seasonal forecasting—A review paper
Tim Palmer,David L. T. Anderson +1 more
TL;DR: The role of the largest interannual fluctuation, the El Nino Southern Oscillation, which has its origins in the tropical Pacific, but extends to influence half the globe, is the focus of much of the review as discussed by the authors.
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The ECMWF Ocean Analysis System: ORA-S3
TL;DR: A new operational ocean analysis/reanalysis system (ORA-S3) has been implemented at ECMWF as discussed by the authors, which is continuously maintained up to 11 days behind real time and is used to initialize seasonal forecasts as well as to provide a historical representation of the ocean for climate studies.
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Global seasonal rainfall forecasts using a coupled ocean–atmosphere model
TL;DR: In this article, a fully coupled global ocean-atmosphere general circulation model is used to make seasonal forecasts of the climate system with a lead time of up to 6 months.
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Three- and Four-Dimensional Variational Assimilation with a General Circulation Model of the Tropical Pacific Ocean. Part I: Formulation, Internal Diagnostics, and Consistency Checks
TL;DR: In this article, an iterative incremental approach is used to minimize a cost function that measures the statistically weighted squared differences between the observational information and their model equivalent, where the control variable of the minimization problem is an increment to the background estimate of the model initial conditions at the beginning of each assimilation window.