Zobrazeno 1 - 8
of 8
pro vyhledávání: '"Gabriele Franch"'
Autor:
Marco Giazzi, Gianandrea Peressutti, Luca Cerri, Matteo Fumi, Isabella Francesca Riva, Andrea Chini, Gianluca Ferrari, Guido Cioni, Gabriele Franch, Gianni Tartari, Flavio Galbiati, Vincenzo Condemi, Alessandro Ceppi
Publikováno v:
Atmosphere, Vol 13, Iss 6, p 928 (2022)
Citizen science has shown great potential for bringing large groups of people closer to science, thanks in part to cooperation with universities and research centers. In this context, amateur weather networks played a major role in the last few decad
Externí odkaz:
https://doaj.org/article/434879cddd424711af9507451b17ce0b
Autor:
Gabriele Franch, Daniele Nerini, Marta Pendesini, Luca Coviello, Giuseppe Jurman, Cesare Furlanello
Publikováno v:
Atmosphere, Vol 11, Iss 3, p 267 (2020)
One of the most crucial applications of radar-based precipitation nowcasting systems is the short-term forecast of extreme rainfall events such as flash floods and severe thunderstorms. While deep learning nowcasting models have recently shown to pro
Externí odkaz:
https://doaj.org/article/489c9a7c0efc4c7ba90b7061a28d7e0c
Autor:
Gabriele Franch, Elena Tomasi, Virginia Poli, Chiara Cardinali, Marco Cristoforetti, Pier Paolo Alberoni
This work introduces a novel deep-learning method for generating realistic ensembles nowcast of radar-based precipitation at a five-minute time resolution for the next 60 minutes and longer.The proposed method is composed of a combination of two mode
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::1b2c32ed8f55beb49c08a214209d1859
https://doi.org/10.5194/egusphere-egu23-15153
https://doi.org/10.5194/egusphere-egu23-15153
Autor:
Marta Pendesini, Giuseppe Jurman, Valerio Maggio, Cesare Furlanello, Gabriele Franch, Luca Coviello
Publikováno v:
Scientific Data
Franch, G, Maggio, V, Coviello, L, Pendesini, M, Jurman, G & Furlanello, C 2020, ' TAASRAD19, a high-resolution weather radar reflectivity dataset for precipitation nowcasting ', Scientific Data, vol. 7, 234 (2020) . https://doi.org/10.1038/s41597-020-0574-8
Scientific Data, Vol 7, Iss 1, Pp 1-13 (2020)
Franch, G, Maggio, V, Coviello, L, Pendesini, M, Jurman, G & Furlanello, C 2020, ' TAASRAD19, a high-resolution weather radar reflectivity dataset for precipitation nowcasting ', Scientific Data, vol. 7, 234 (2020) . https://doi.org/10.1038/s41597-020-0574-8
Scientific Data, Vol 7, Iss 1, Pp 1-13 (2020)
We introduce TAASRAD19, a high-resolution radar reflectivity dataset collected by the Civil Protection weather radar of the Trentino South Tyrol Region, in the Italian Alps. The dataset includes 894,916 timesteps of precipitation from more than 9 yea
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::6e05188b3b9b568dcc8934cb5d9facb5
https://doi.org/10.1038/s41597-020-0574-8
https://doi.org/10.1038/s41597-020-0574-8
Publikováno v:
Remote Sensing; Volume 11; Issue 24; Pages: 2922
The use of analog-similar weather patterns for weather forecasting and analysis is an established method in meteorology. The most challenging aspect of using this approach in the context of operational radar applications is to be able to perform a fa
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::f1d1c7542854e30e87492ba83905c889
Publikováno v:
Journal of Applied Remote Sensing. 14:1
The goal of this research is to develop a general deep learning solution for atmospheric correction and target detection using multiple hyperspectral scenes. It is assumed that the scenes differ only in range and viewing angles, that they are acquire
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::7ae09b60186b7f78fd5a00473f886303
https://doi.org/10.1117/1.jrs.14.024518
https://doi.org/10.1117/1.jrs.14.024518