Zobrazeno 1 - 9
of 9
pro vyhledávání: '"Ezequiel Cimadevilla"'
Autor:
Maialen Iturbide, Jesús Fernández, José M. Gutiérrez, Anna Pirani, David Huard, Alaa Al Khourdajie, Jorge Baño-Medina, Joaquin Bedia, Ana Casanueva, Ezequiel Cimadevilla, Antonio S. Cofiño, Matteo De Felice, Javier Diez-Sierra, Markel García-Díez, James Goldie, Dimitris A. Herrera, Sixto Herrera, Rodrigo Manzanas, Josipa Milovac, Aparna Radhakrishnan, Daniel San-Martín, Alessandro Spinuso, Kristen M. Thyng, Claire Trenham, Özge Yelekçi
Publikováno v:
Scientific Data, Vol 9, Iss 1, Pp 1-10 (2022)
Abstract The Sixth Assessment Report (AR6) of the Intergovernmental Panel on Climate Change (IPCC) has adopted the FAIR Guiding Principles. We present the Atlas chapter of Working Group I (WGI) as a test case. We describe the application of the FAIR
Externí odkaz:
https://doaj.org/article/835a9fda89454ebaa0a137fd843255b4
The ESGF Virtual Aggregation (EVA) is a new data workflow approach that aims to advance the sharing and reuse of scientific climate data stored in the Earth System Grid Federation (ESGF). The ESGF is a global infrastructure and network of internation
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::9a5de8d3c27ff4c32ef14f2391e389fc
https://doi.org/10.5194/egusphere-egu23-16117
https://doi.org/10.5194/egusphere-egu23-16117
Autor:
Javier Diez-Sierra, Maialen Iturbide, José M. Gutiérrez, Jesús Fernández, Josipa Milovac, Antonio S. Cofiño, Ezequiel Cimadevilla, Grigory Nikulin, Guillaume Levavasseur, Erik Kjellström, Katharina Bülow, András Horányi, Anca Brookshaw, Markel García-Díez, Antonio Pérez, Jorge Baño-Medina, Bodo Ahrens, Antoinette Alias, Moetasim Ashfaq, Melissa Bukovsky, Erasmo Buonomo, Steven Caluwaerts, Sin Chan Chou, Ole B. Christensen, James M. Ciarlò, Erika Coppola, Lola Corre, Marie-Estelle Demory, Vladimir Djurdjevic, Jason P. Evans, Rowan Fealy, Hendrik Feldmann, Daniela Jacob, Sanjay Jayanarayanan, Jack Katzfey, Klaus Keuler, Christoph Kittel, Mehmet Levent Kurnaz, René Laprise, Piero Lionello, Seth McGinnis, Paola Mercogliano, Pierre Nabat, Barış Önol, Tugba Ozturk, Hans-Jürgen Panitz, Dominique Paquin, Ildikó Pieczka, Francesca Raffaele, Armelle Reca Remedio, John Scinocca, Florence Sevault, Samuel Somot, Christian Steger, Fredolin Tangang, Claas Teichmann, Piet Termonia, Marcus Thatcher, Csaba Torma, Erik van Meijgaard, Robert Vautard, Kirsten Warrach-Sagi, Katja Winger, George Zittis
Publikováno v:
Bulletin of the American Meteorological Society, 103 (12)
Bulletin of the American Meteorological Society
Bulletin of the American Meteorological Society, 2022, 103 (12), pp.E2804-E2826. ⟨10.1175/BAMS-D-22-0111.1⟩
Diez-Sierra, J.; Iturbide, M.; Gutiérrez, J. M.; Fernández, J.; Milovac, J.; Cofiño, A. S.; Cimadevilla, E.; Nikulin, G.; Levavasseur, G.; Kjellström, E.; Bülow, K.; Horányi, A.; Brookshaw, A.; García-Díez, M.; Pérez, A.; Baño-Medina, J.; Ahrens, B.; Alias, A.; Ashfaq, M.; Bukovsky, M.; Buonomo, E.; Caluwaerts, S.; Chan Chou, S.; Christensen, O. B.; Ciarlo´, J. M.; Coppola, E.; Corre, L.; Demory, M.; Djurdjevic, V.; Evans, J. P.; Fealy, R.; Feldmann, H.; Jacob, D.; Jayanarayanan, S.; Katzfey, J.; Keuler, K.; Kittel, C.; Levent Kurnaz, M.; Laprise, R.; Lionello, P.; McGinnis, S.; Mercogliano, P.; Nabat, P.; Önol, B.; Ozturk, T.; Panitz, H.; Paquin, D.; Pieczka, I.; Raffaele, F.; Reca Remedio, A.; Scinocca, J.; Sevault, F.; Somot, S.; Steger, C.; Tangang, F.; Teichmann, C.; Termonia, P.; Thatcher, M.; Torma, C.; van Meijgaard, E.; Vautard, R.; Warrach-Sagi, K.; Winger, K.; Zittis, G.: The Worldwide C3S CORDEX Grand Ensemble: A Major Contribution to Assess Regional Climate Change in the IPCC AR6 Atlas. In: Bulletin of the American Meteorological Society. Vol. 103 (2022) 12, E2804-E2826. (DOI: /10.1175/BAMS-D-22-0111.1)
Bulletin of the American Meteorological Society, 2022, 103(12), E2804-E2826
BULLETIN OF THE AMERICAN METEOROLOGICAL SOCIETY
Bulletin of the American Meteorological Society, 103 (12), E2804–E2826
Bulletin of the American Meteorological Society
Bulletin of the American Meteorological Society, 2022, 103 (12), pp.E2804-E2826. ⟨10.1175/BAMS-D-22-0111.1⟩
Diez-Sierra, J.; Iturbide, M.; Gutiérrez, J. M.; Fernández, J.; Milovac, J.; Cofiño, A. S.; Cimadevilla, E.; Nikulin, G.; Levavasseur, G.; Kjellström, E.; Bülow, K.; Horányi, A.; Brookshaw, A.; García-Díez, M.; Pérez, A.; Baño-Medina, J.; Ahrens, B.; Alias, A.; Ashfaq, M.; Bukovsky, M.; Buonomo, E.; Caluwaerts, S.; Chan Chou, S.; Christensen, O. B.; Ciarlo´, J. M.; Coppola, E.; Corre, L.; Demory, M.; Djurdjevic, V.; Evans, J. P.; Fealy, R.; Feldmann, H.; Jacob, D.; Jayanarayanan, S.; Katzfey, J.; Keuler, K.; Kittel, C.; Levent Kurnaz, M.; Laprise, R.; Lionello, P.; McGinnis, S.; Mercogliano, P.; Nabat, P.; Önol, B.; Ozturk, T.; Panitz, H.; Paquin, D.; Pieczka, I.; Raffaele, F.; Reca Remedio, A.; Scinocca, J.; Sevault, F.; Somot, S.; Steger, C.; Tangang, F.; Teichmann, C.; Termonia, P.; Thatcher, M.; Torma, C.; van Meijgaard, E.; Vautard, R.; Warrach-Sagi, K.; Winger, K.; Zittis, G.: The Worldwide C3S CORDEX Grand Ensemble: A Major Contribution to Assess Regional Climate Change in the IPCC AR6 Atlas. In: Bulletin of the American Meteorological Society. Vol. 103 (2022) 12, E2804-E2826. (DOI: /10.1175/BAMS-D-22-0111.1)
Bulletin of the American Meteorological Society, 2022, 103(12), E2804-E2826
BULLETIN OF THE AMERICAN METEOROLOGICAL SOCIETY
Bulletin of the American Meteorological Society, 103 (12), E2804–E2826
The collaboration between the Coordinated Regional Climate Downscaling Experiment (CORDEX) and the Earth System Grid Federation (ESGF) provides open access to an unprecedented ensemble of regional climate model (RCM) simulations, across the 14 CORDEX
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::15ce00fbdc08bdebba3838b0edec1fdb
https://hdl.handle.net/20.500.11850/589140
https://hdl.handle.net/20.500.11850/589140
Climate datasets are usually provided in separate files that facilitate dataset management in climate data distribution systems. In ESGF1 (Earth System Grid Federation) a time series of a variable is split into smaller pieces of data in order to redu
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::9bcba10bcdaff0bc12566fefbf60ebed
https://doi.org/10.5194/egusphere-egu22-7151
https://doi.org/10.5194/egusphere-egu22-7151
Autor:
Jorge Baño-Medina, Rodrigo Manzanas, Ezequiel Cimadevilla, Jesús Fernández, Antonio S. Cofiño, Jose Manuel Gutiérrez
Deep Learning (DL) has recently emerged as a powerful approach to downscale climate variables from low-resolution GCM fields, showing promising capabilities to reproduce the local scale in present conditions [1]. There have also been some prospects a
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::31c3c47607dcd855c532afce563d8e4a
https://doi.org/10.5194/egusphere-egu22-11855
https://doi.org/10.5194/egusphere-egu22-11855
Autor:
Javier Diez-Sierra, Maialen Iturbide, José Manuel Gutiérrez, Jesús Fernandez, Josipa Milovac, Antonio S. Cofiño, Ezequiel Cimadevilla
CORDEX users are confronted with multiple sources of climate change information in regions where multiple domains overlap. Assessing the consistency of these sources (particularly the consistency of the climate signals) and understanding potential co
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::b92a33af728722a1adad1f1f081f883d
https://doi.org/10.5194/egusphere-egu22-6059
https://doi.org/10.5194/egusphere-egu22-6059
Autor:
Jorge Baño-Medina, Rodrigo Manzanas, Ezequiel Cimadevilla, Jesús Fernández, Jose González-Abad, Antonio S. Cofiño, José Manuel Gutiérrez
Publikováno v:
Geoscientific Model Development, 2022, 15(17), 6747-6758
Deep learning (DL) has recently emerged as an innovative tool to downscale climate variables from large-scale atmospheric fields under the perfect-prognosis (PP) approach. Different convolutional neural networks (CNNs) have been applied under present
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::7d42078e6da590b03bc8b45d72d1d4d4
https://gmd.copernicus.org/preprints/gmd-2022-57/
https://gmd.copernicus.org/preprints/gmd-2022-57/
Data analysis in climate science has been traditionally performed in two different environments, local workstations and HPC infrastructures. Local workstations provide a non scalable environment in which data analysis is restricted to small datasets
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::307ecff608680b02dfde0bbab503b6c1
https://doi.org/10.5194/egusphere-egu2020-19280
https://doi.org/10.5194/egusphere-egu2020-19280
Autor:
Jesús Fernández, Ezequiel Cimadevilla, Jorge Baño-Medina, Antonio S. Cofiño, M. D. Frías, Rodrigo Manzanas, Daniel San-Martín, José M. Gutiérrez, Joaquín Bedia, Maialen Iturbide, Sixto Herrera
Publikováno v:
Environmental Modelling & Software Volume 111, January 2019, Pages 42-54
UCrea Repositorio Abierto de la Universidad de Cantabria
Universidad de Cantabria (UC)
Digital.CSIC. Repositorio Institucional del CSIC
instname
UCrea Repositorio Abierto de la Universidad de Cantabria
Universidad de Cantabria (UC)
Digital.CSIC. Repositorio Institucional del CSIC
instname
Climate-driven sectoral applications commonly require different types of climate data (e.g. observations, reanalysis, climate change projections) from different providers. Data access, harmonization and post-processing (e.g. bias correction) are time
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::5f64f19d73e047ccd72722a460764a61
http://hdl.handle.net/10902/14999
http://hdl.handle.net/10902/14999