Zobrazeno 1 - 10
of 10
pro vyhledávání: '"Jonathan A. Weyn"'
Publikováno v:
Journal of Advances in Modeling Earth Systems, Vol 13, Iss 7, Pp n/a-n/a (2021)
Abstract We present an ensemble prediction system using a Deep Learning Weather Prediction (DLWP) model that recursively predicts six key atmospheric variables with six‐hour time resolution. This computationally efficient model uses convolutional n
Externí odkaz:
https://doaj.org/article/51f98f8aa6dd418e82a085d7ccde7bc3
Publikováno v:
Journal of Advances in Modeling Earth Systems, Vol 11, Iss 8, Pp 2680-2693 (2019)
Abstract We develop elementary weather prediction models using deep convolutional neural networks (CNNs) trained on past weather data to forecast one or two fundamental meteorological fields on a Northern Hemisphere grid with no explicit knowledge ab
Externí odkaz:
https://doaj.org/article/6490fd46ee6d471ea14a48f09a7d2aa2
Autor:
Stephan Rasp, Peter D. Dueben, Sebastian Scher, Jonathan A. Weyn, Soukayna Mouatadid, Nils Thuerey
Publikováno v:
Journal of Advances in Modeling Earth Systems, Vol 12, Iss 11, Pp n/a-n/a (2020)
Abstract Data‐driven approaches, most prominently deep learning, have become powerful tools for prediction in many domains. A natural question to ask is whether data‐driven methods could also be used to predict global weather patterns days in adv
Externí odkaz:
https://doaj.org/article/db7463c2769d4534a00fa07937b96c7a
Publikováno v:
Journal of Advances in Modeling Earth Systems, Vol 12, Iss 9, Pp n/a-n/a (2020)
Abstract We present a significantly improved data‐driven global weather forecasting framework using a deep convolutional neural network (CNN) to forecast several basic atmospheric variables on a global grid. New developments in this framework inclu
Externí odkaz:
https://doaj.org/article/fa2a0ca04aa7476185a92c28151468cc
Publikováno v:
Journal of Advances in Modeling Earth Systems, Vol 11, Iss 8, Pp 2680-2693 (2019)
We develop elementary weather prediction models using deep convolutional neural networks (CNNs) trained on past weather data to forecast one or two fundamental meteorological fields on a Northern Hemisphere grid with no explicit knowledge about physi
Autor:
Jonathan A. Weyn, Dale R. Durran
Publikováno v:
Quarterly Journal of the Royal Meteorological Society. 145:57-74
Autor:
Jonathan A. Weyn, Dale R. Durran
Publikováno v:
Journal of the Atmospheric Sciences. 75:3331-3345
Idealized ensemble simulations of mesoscale convective systems (MCSs) with horizontal grid spacings of 1, 1.4, and 2 km are used to analyze the influence of numerical resolution on the rate of growth of ensemble spread in convection-resolving numeric
Publikováno v:
Monthly Weather Review. 145:3901-3910
Spectra are often computed from gridded data to determine the horizontal-scale dependence of quantities such as kinetic energy, vertical velocity, or perturbation potential temperature. This paper discusses several important considerations for the pr
Autor:
Jonathan A. Weyn, Dale R. Durran
Publikováno v:
Journal of the Atmospheric Sciences. 74:2191-2210
Recent work has suggested that modest initial relative errors on scales of O(100) km in a numerical weather forecast may exert more control on the predictability of mesoscale convective systems at lead times beyond about 5 h than 100% relative errors
Autor:
Jonathan A. Weyn, Dale R. Durran
Publikováno v:
Bulletin of the American Meteorological Society. 97:237-243
One important limitation on the accuracy of weather forecasts is imposed by unavoidable errors in the specification of the atmosphere’s initial state. Much theoretical concern has been focused on the limits to predictability imposed by small-scale