Zobrazeno 1 - 10
of 27
pro vyhledávání: '"Riccardo Silini"'
Publikováno v:
npj Climate and Atmospheric Science, Vol 4, Iss 1, Pp 1-7 (2021)
Abstract The socioeconomic impact of weather extremes draws the attention of researchers to the development of novel methodologies to make more accurate weather predictions. The Madden–Julian oscillation (MJO) is the dominant mode of variability in
Externí odkaz:
https://doaj.org/article/fececcd91ff74ab4ac7e491179b51cad
Autor:
Riccardo Silini, Cristina Masoller
Publikováno v:
Scientific Reports, Vol 11, Iss 1, Pp 1-13 (2021)
Abstract Identifying, from time series analysis, reliable indicators of causal relationships is essential for many disciplines. Main challenges are distinguishing correlation from causality and discriminating between direct and indirect interactions.
Externí odkaz:
https://doaj.org/article/e163728f51ca4d9588d462cb776638de
We evaluate causal dependencies between thirteen indices that represent large-scale climate patterns (El Nino/Southern Oscillation, the North Atlantic Oscillation, the Pacifc Decadal Oscillation, etc.) using a recently proposed approach based on a li
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::6adc32b2b87ce9f0c33360f0dd78e6a4
https://hdl.handle.net/2117/379385
https://hdl.handle.net/2117/379385
Publikováno v:
eISSN
The Madden–Julian Oscillation (MJO) is one of the main sources of sub-seasonal atmospheric predictability in the tropical region. The MJO affects precipitation over highly populated areas, especially around southern India. Therefore, predicting its
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::34b4d36b6134ee08854827d6f9fbf3a1
https://egusphere.copernicus.org/preprints/2022/egusphere-2022-524/
https://egusphere.copernicus.org/preprints/2022/egusphere-2022-524/
The Madden-Julian Oscillation (MJO) is one of the main sources of sub-seasonal atmospheric predictability in the Tropical region. The MJO affects precipitation over highly populated areas, especially around Southern India. Therefore, predicting its p
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::cb1814f6ce387f2ad2e469a272367cb7
https://doi.org/10.5194/ems2022-497
https://doi.org/10.5194/ems2022-497
Skillful forecast of the Madden Julian Oscillation (MJO) has an important scientific interest because the MJO represents one of the most important sources of sub-seasonal predictability. Proxies of the MJO can be derived from the first principal comp
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::6481349e577eb38e2b4c874aab8546a1
https://doi.org/10.5194/egusphere-egu22-742
https://doi.org/10.5194/egusphere-egu22-742
Publikováno v:
Advances in Forest Fire Research 2022 ISBN: 9789892622989
Understanding the current fire-climate relationships is of utmost importance in order to assess the potential impacts that projected climate may exert in the near future. However, the many factors involved in fire activity often prevent a proper attr
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::270132db4bbc5993d79cf3f5e685694f
https://doi.org/10.14195/978-989-26-2298-9_23
https://doi.org/10.14195/978-989-26-2298-9_23
Publikováno v:
COLIBRI
Universidad de la República
instacron:Universidad de la República
UPCommons. Portal del coneixement obert de la UPC
Universitat Politècnica de Catalunya (UPC)
npj Climate and Atmospheric Science, Vol 4, Iss 1, Pp 1-7 (2021)
Universidad de la República
instacron:Universidad de la República
UPCommons. Portal del coneixement obert de la UPC
Universitat Politècnica de Catalunya (UPC)
npj Climate and Atmospheric Science, Vol 4, Iss 1, Pp 1-7 (2021)
The socioeconomic impact of weather extremes draws the attention of researchers to the development of novel methodologies to make more accurate weather predictions. The Madden–Julian oscillation (MJO) is the dominant mode of variability in the trop
Publikováno v:
Journal of Physics: Complexity. 3:045011
Reliable anomaly/outlier detection algorithms have practical applications in many fields. For instance, anomaly detection allows to filter and clean the data used to train machine learning algorithms, improving their performance. However, outlier min