WeatherBench 2: A Benchmark for the Next Generation of Data‐Driven Global Weather Models

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

Potential and Limitations of Machine Learning for Modeling Warm‐Rain Cloud Microphysical Processes

Autor: Axel Seifert, Stephan Rasp

Publikováno v: Journal of Advances in Modeling Earth Systems, Vol 12, Iss 12, Pp n/a-n/a (2020)

Abstract The use of machine learning based on neural networks for cloud microphysical parameterizations is investigated. As an example, we use the warm‐rain formation by collision‐coalescence, that is, the parameterization of autoconversion, accr

Externí odkaz: https://doaj.org/article/11f5056aa89746fd840cdd112ed1e2ad

Using weak constrained neural networks to improve simulations in the gray zone

Autor: Yvonne Ruckstuhl, Raphael Kriegmair, Stephan Rasp, George Craig

Machine learning represents a potential method to cope with the gray zone problem of representing motions in dynamical systems on scales comparable to the model resolution. Here we explore the possibility of using a neural network to directly learn t

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::cdefc597c62aa27856722e8dd1605542
https://doi.org/10.5194/egusphere-egu23-5523

The role of internal variability in global climate projections of extreme events

Autor: Mackenzie L. Blanusa, Carla J. López-Zurita, Stephan Rasp

Climate projection uncertainty can be partitioned into model uncertainty, scenario uncertainty and internal variability. Here, we investigate the different sources of uncertainty in the projected frequencies of daily maximum temperature and precipita

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::4a3f09dc2e553568540c315d375d79af
http://arxiv.org/abs/2208.08275

Combining Crowdsourcing and Deep Learning to Explore the Mesoscale Organization of Shallow Convection

Autor: Sandrine Bony, Bjorn Stevens, Stephan Rasp, Hauke Schulz

Publikováno v: Bulletin of the American Meteorological Society
Bulletin of the American Meteorological Society, American Meteorological Society, 2020, ⟨10.1175/BAMS-D-19-0324.1⟩

Humans excel at detecting interesting patterns in images, for example those taken from satellites. This kind of anecdotal evidence can lead to the discovery of new phenomena. However, it is often difficult to gather enough data of subjective features

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::3df678c1d5a6985474ebdbebf10d529e
https://doi.org/10.1175/bams-d-19-0324.1