Zobrazeno 1 - 10
of 13
pro vyhledávání: '"Roope Tervo"'
Autor:
Jacob Senior-Williams, Frank Hogervorst, Erwin Platen, Arie Kuijt, Jacobus Onderwaater, Roope Tervo, Viju O. John, Arata Okuyama
Publikováno v:
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol 17, Pp 5234-5244 (2024)
The work performed in this study evaluated the application of generalized pretrained object detection models for the identification and classification of tropical storm (TS) systems through transfer learning. While the majority of literature focuses
Externí odkaz:
https://doaj.org/article/13d5fae0619a4475ad08efb369957086
Autor:
Roberto Cuccu, Vasileios Baousis, Umberto Modigliani, Charalampos Kominos, Xavier Abellan, Roope Tervo
The European Centre for Medium-Range Weather Forecasts (ECMWF) together with the European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT) have worked together to offer to their Member States a new paradigm to access and cons
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::946b5f7df22e8084d49093a248eb7a23
https://doi.org/10.5194/egusphere-egu23-12539
https://doi.org/10.5194/egusphere-egu23-12539
Autor:
Armagan Karatosun, Michael Grant, Vasileios Baousis, Duncan McGregor, Richard Care, John Nolan, Roope Tervo
Although utilizing the cloud infrastructure for big data processing algorithms is increasingly common, the challenges of utilizing cloud infrastructures efficiently and effectively are often underestimated. This is especially true in multi-cloud scen
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::81ea727b4e0b776b1ef4acb249ac3631
https://doi.org/10.5194/egusphere-egu23-3639
https://doi.org/10.5194/egusphere-egu23-3639
Autor:
Rob Roebeling, Viju John, Joerg Schulz, Jaap Onderwaater, Oliver Sus, Ken R. Knapp, Andrew Heidinger, Tasuku Tabata, Arata Okuyama, Frank Ruethrich, Paul Poli, Mike Grant, Roope Tervo, Timo Hanschmann
The utilisation of observations of past, present, and future geostationary satellites for climate monitoring is a challenge. Since the late 1970s, space agencies operated up to 50 geostationary satellite missions with a variety of instrumentation. Me
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::844558d093af23dbd1f2c93e026ad137
https://doi.org/10.5194/egusphere-egu23-15951
https://doi.org/10.5194/egusphere-egu23-15951
Autor:
Roope Tervo, Michael Grant
Artificial Intelligence (AI) and Machine Learning (ML)-applications have become a huge hype. What does it mean to serve data for AI and ML? EUMETSAT climate reprocessing data records try to meet following guidelines as far as possible.In ML applicati
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::702b99d797e0ecf5ba2e89ab90d5fa09
https://doi.org/10.5194/ems2022-17
https://doi.org/10.5194/ems2022-17
Autor:
Roope Tervo, Joerg Schulz, Joachim Saalmueller, Xavier Abellan, Umberto Modigliani, Vasileios Baousis
The European Weather Cloud (EWC) is set to be the cloud-based collaboration platform for meteorological application development and operations in Europe and enables the digital transformation of the European Meteorological Infrastructure.It consists
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::ec6a5ae0abd352d05fff847d33dbeb7c
https://doi.org/10.5194/ems2022-16
https://doi.org/10.5194/ems2022-16
Autor:
Mikko Visa, Roope Tervo
Finnish Meteorological Institute has a long history of open data. Partly as a result of the INSPIRE directive almost all important data was opened back in 2013. Because of this we have quite a long history of usage of the data and as well experience
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::edd95d22e41db18109822020eb3a867e
https://doi.org/10.5194/ems2021-448
https://doi.org/10.5194/ems2021-448
Publikováno v:
IEEE Transactions on Geoscience and Remote Sensing. 57:8618-8626
Prediction of power outages caused by convective storms which are highly localised in space and time is of crucial importance to power grid operators. We propose a new machine learning approach to predict the damage caused by storms. This approach hi
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::266994aedef1744a251ffa402f403a7d
https://doi.org/10.1109/tgrs.2019.2921809
https://doi.org/10.1109/tgrs.2019.2921809