A Bayesian Spatial Analysis of the Heterogeneity in Human Mobility Changes During the First Wave of the COVID-19 Epidemic in the United States

Autor: Miguel Carrion Alvarez, Javier Pérez Trufero, Jorge Mateu, Giulia Carella

Publikováno v: The American Statistician. 76:64-72

The spread of COVID-19 in the U.S. prompted nonpharmaceutical interventions which caused a reduction in mobility everywhere, although with large disparities between different counties. Using a Bayesian spatial modeling framework, we investigated the

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::a4fcdd6cbbea520bb1dc1f8b4ea10329
https://doi.org/10.1080/00031305.2021.1965657

Deep learning-based downscaling of seasonal forecasts over the Iberian Peninsula

Autor: Lluís Palma Garcia, Nube Gonzalez-Reviriego, Giulia Carella, Llorenç Lledó, Carlos Alberto Gómez-Gonzalez, Raül Marcos, Albert Soret Miravet

Seasonal climate predictions can forecast the climate variability up to several months ahead and support a wide range of societal activities. The coarse spatial resolution of seasonal forecasts needs to be refined to the regional/local scale for spec

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::0dc6b3f11bc0a7814ecc081ecc115d11
https://doi.org/10.5194/egusphere-egu21-12253

Boosting performance in Machine Learning of Turbulent and Geophysical Flows via scale separation

Autor: Pascal Yiou, C. G. Ngoungue Langue, Adnane Hamid, Soulivanh Thao, Mathieu Vrac, Giulia Carella, Davide Faranda, Valerie Gautard, Flavio Maria Emanuele Pons

Recent advances in statistical learning have opened the possibility to forecast the behavior of chaotic systems using recurrent neural networks. In this letter we investigate the applicability of this framework to geophysical flows, known to be inter

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::221703ec06a90731b9d0bc38cad2afcd
https://doi.org/10.5194/egusphere-egu2020-7569

A Call for New Approaches to Quantifying Biases in Observations of Sea Surface Temperature

Autor: Alexey Kaplan, John Kennedy, Boyin Huang, David E. Parker, Shoji Hirahara, Thomas M. Smith, Masayoshi Ishii, Huai-Min Zhang, Christopher P. Atkinson, Victor Venema, Finban Lindgren, Nick Rayner, Christopher J. Merchant, David I. Berry, Elizabeth C. Kent, Simone Morak-Bozzo, Souichiro Yasui, Giulia Carella, Philip Jones, Yoshikazu Fukuda

Publikováno v: Kent, E C, Berry, D I, Carella, G, Kennedy, J J, Parker, D E, Atkinson, C P, Rayner, N A, Smith, T M, Hirahara, S, Huang, B, Zhang, H-M, Kaplan, A, Fukuda, Y, Ishii, M, Jones, P D, Lindgren, F, Merchant, C J, Morak-Bozzo, S, Venema, V & Yasui, S 2017, ' A call for new approaches to quantifying biases in observations of sea-surface temperature ', Bulletin of the American Meteorological Society, vol. 98, no. 8, pp. 1601-1616 . https://doi.org/10.1175/BAMS-D-15-00251.1

Global surface-temperature is a fundamental measure of climate change. We discuss bias estimation for sea-surface temperature and recommend the improvements to data, observational metadata, and uncertainty modeling needed to make progress.Global surf

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::5849edd91f9eaca332fca2a808e49f1d
https://doi.org/10.1175/bams-d-15-00251.1

Response to reviewers

Autor: Giulia Carella

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::b7d44301149a0e057e40afa1b4c8810d
https://doi.org/10.5194/essd-2018-138-ac1