Forecasting PM10 Levels Using Machine Learning Models in the Arctic: A Comparative Study

Autor: Paolo Fazzini, Marco Montuori, Antonello Pasini, Alice Cuzzucoli, Ilaria Crotti, Emilio Fortunato Campana, Francesco Petracchini, Srdjan Dobricic

Publikováno v: Remote Sensing, Vol 15, Iss 13, p 3348 (2023)

In this study, we present a statistical forecasting framework and assess its efficacy using a range of established machine learning algorithms for predicting Particulate Matter (PM) concentrations in the Arctic, specifically in Pallas (FI), Reykjavik

Externí odkaz: https://doaj.org/article/cf52967cab9546b394a598ca73730267

Nonlinear impacts of future anthropogenic aerosol emissions on Arctic warming

Autor: Simone Russo, Elisabetta Vignati, Zbigniew Klimont, Srdjan Dobricic, R. Van Dingenen, Luca Pozzoli, J. Wilson

Past reductions of anthropogenic aerosol concentrations in Europe and North America could have amplified Arctic warming. In the future the impact of air pollution policies may differ, because the major anthropogenic sources of atmospheric aerosols ar

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::a22ab84c3cd5e172fb5c41115dbeabc6
http://pure.iiasa.ac.at/id/eprint/15963/

Large-Scale Atmospheric Warming in Winter and the Arctic Sea Ice Retreat

Autor: Elisabetta Vignati, Srdjan Dobricic, Simone Russo

Publikováno v: Journal of Climate. 29:2869-2888

The ongoing shrinkage of the Arctic sea ice cover is likely linked to the global temperature rise, the pronounced warming in the Arctic, and possibly weather anomalies in the midlatitudes. By evaluating independent components of global atmospheric en

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::ca06ed6584dc73eb779a8384d3ecfbcb
https://doi.org/10.1175/jcli-d-15-0417.1

A hybrid variational-ensemble data assimilation scheme with systematic error correction for limited-area ocean models

Autor: Reiner Onken, Srdjan Dobricic, Aniello Russo, Paolo Oddo, Emanuel Coelho, Craig V.W. Lewis, Andrea Storto

Publikováno v: Ocean Science, Vol 12, Iss 5, Pp 1137-1153 (2016)
Ocean science
12 (2016). doi:10.5194/os-12-1137-2016
info:cnr-pdr/source/autori:Oddo, Paolo; Storto, Andrea; Dobricic, Srdjan; Russo, Aniello; Lewis, Craig; Onken, Reiner; Coelho, Emanuel/titolo:A hybrid variational-ensemble data assimilation scheme with systematic error correction for limited-area ocean models/doi:10.5194%2Fos-12-1137-2016/rivista:Ocean science (Print)/anno:2016/pagina_da:/pagina_a:/intervallo_pagine:/volume:12
Oddo, P.; Storto, A.; Dobricic, S.; Russo, A.; Lewis, C.; Onken, R.; Coelho, E.: A hybrid variational-ensemble data assimilation scheme with systematic error correction for limited-area ocean models. In: Ocean Science. Vol. 12 (2016) 5, 1137-1153. (DOI: 10.5194/os-12-1137-2016)

A hybrid variational-ensemble data assimilation scheme to estimate the vertical and horizontal parts of the background error covariance matrix for an ocean variational data assimilation system is presented and tested in a limited-area ocean model imp

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::3d1402ceef682adf26ae420fe09fa240
https://www.ocean-sci.net/12/1137/2016/

Ocean ensemble forecasting. Part II: Mediterranean Forecast System response

Autor: A. Bonazzi, Christopher K. Wikle, Srdjan Dobricic, L. Mark Berliner, Nadia Pinardi, Ralph F. Milliff

Publikováno v: Quarterly Journal of the Royal Meteorological Society. 137:879-893

This article analyzes the ocean forecast response to surface vector wind (SVW) distributions generated by a Bayesian hierarchical model (BHM) developed in Part I of this series. A new method for ocean ensemble forecasting (OEF), the so-called BHM-SVW

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::f94595ae88c99e108e51baea8fa298a7
https://doi.org/10.1002/qj.816