Zobrazeno 1 - 3
of 3
pro vyhledávání: '"Rob Carver"'
Autor:
Stephan Rasp, Stephan Hoyer, Alexander Merose, Ian Langmore, Peter Battaglia, Tyler Russell, Alvaro Sanchez‐Gonzalez, Vivian Yang, Rob Carver, Shreya Agrawal, Matthew Chantry, Zied Ben Bouallegue, Peter Dueben, Carla Bromberg, Jared Sisk, Luke Barrington, Aaron Bell, Fei Sha
Publikováno v:
Journal of Advances in Modeling Earth Systems, Vol 16, Iss 6, Pp n/a-n/a (2024)
Abstract WeatherBench 2 is an update to the global, medium‐range (1–14 days) weather forecasting benchmark proposed by (Rasp et al., 2020, https://doi.org/10.1029/2020ms002203), designed with the aim to accelerate progress in data‐driven weathe
Externí odkaz:
https://doaj.org/article/26fd487748a44dd98181f82522f88510
Autor:
Lasse Espeholt, Shreya Agrawal, Casper Sønderby, Manoj Kumar, Jonathan Heek, Carla Bromberg, Cenk Gazen, Rob Carver, Marcin Andrychowicz, Jason Hickey, Aaron Bell, Nal Kalchbrenner
Publikováno v:
Nature Communications, Vol 13, Iss 1, Pp 1-10 (2022)
Can AI learn from atmospheric data and improve weather forecasting? The neural network MetNet-2 achieves this by forecasting the fast changing variable of precipitation up to 12 h ahead more accurately and efficiently than traditional models based on
Externí odkaz:
https://doaj.org/article/3ce233e8b2ea4d9f8ce0eb1005f589c3
Autor:
Lasse Espeholt, Shreya Agrawal, Casper Sønderby, Manoj Kumar, Jonathan Heek, Carla Bromberg, Cenk Gazen, Rob Carver, Marcin Andrychowicz, Jason Hickey, Aaron Bell, Nal Kalchbrenner
Publikováno v:
Nature communications. 13(1)
Existing weather forecasting models are based on physics and use supercomputers to evolve the atmosphere into the future. Better physics-based forecasts require improved atmospheric models, which can be difficult to discover and develop, or increasin