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Ekaterina Kourzeneva
Researcher at Finnish Meteorological Institute
Publications - 20
Citations - 1026
Ekaterina Kourzeneva is an academic researcher from Finnish Meteorological Institute. The author has contributed to research in topics: Numerical weather prediction & Climate model. The author has an hindex of 13, co-authored 20 publications receiving 838 citations. Previous affiliations of Ekaterina Kourzeneva include Russian State Hydrometeorological University.
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The SURFEXv7.2 land and ocean surface platform for coupled or offline simulation of earth surface variables and fluxes
Valéry Masson,P. Le Moigne,Eric Martin,Stéphanie Faroux,Antoinette Alias,Ramdane Alkama,Sophie Belamari,Alina Barbu,Aaron Boone,F. Bouyssel,Pierre Brousseau,Eric Brun,Jean-Christophe Calvet,Dominique Carrer,Bertrand Decharme,Christine Delire,S. Donier,K. Essaouini,A.-L. Gibelin,Hervé Giordani,Florence Habets,M. Jidane,G. Kerdraon,Ekaterina Kourzeneva,Ekaterina Kourzeneva,Matthieu Lafaysse,Sébastien Lafont,C. Lebeaupin Brossier,Aude Lemonsu,Jean-François Mahfouf,P. Marguinaud,M. Mokhtari,S. Morin,G. Pigeon,Rui Salgado,Yann Seity,F. Taillefer,G. Tanguy,Pierre Tulet,Béatrice Vincendon,Vincent Vionnet,Aurore Voldoire +41 more
TL;DR: SURFEX as mentioned in this paper is an externalized land and ocean surface platform that describes the surface fluxes and the evolution of four types of surfaces: nature, town, inland water and ocean.
Journal Article
External data for lake parameterization in Numerical Weather Prediction and climate modeling
TL;DR: The first steps to make the set of lake parameters for the needs of atmospheric modeling are described in this paper and the mean lake depth was chosen to be the key lake parameter for which direct measurements were collected and processed.
Journal ArticleDOI
Global gridded dataset of lake coverage and lake depth for use in numerical weather prediction and climate modelling
TL;DR: In this paper, a global dataset of lake coverage and lake depth was developed for use in numerical weather prediction and climate modelling, which provides the global gridded information on lake depth with the resolution of 30 arc sec (approximately 1 km).
Journal ArticleDOI
Estimation of the mean depth of boreal lakes for use in numerical weather prediction and climate modelling
TL;DR: In this paper, a new version of the Global Lake Database (GLDB) with estimations of the typical mean lake depth for each of the selected regions, statistics from GLDB were gained and analyzed.
A study on effects of lake temperature and ice cover in HIRLAM
TL;DR: In this paper, the lake surface temperatures for a numerical weather prediction (NWP) model can be obtained in several ways: by utilizing climatic information, by assimilating LST observations and by applying lake parametrizations for prediction of LST.