Articles | Volume 15, issue 5
Development and technical paper
16 Mar 2022
Development and technical paper |  | 16 Mar 2022

Towards physics-inspired data-driven weather forecasting: integrating data assimilation with a deep spatial-transformer-based U-NET in a case study with ERA5

Ashesh Chattopadhyay, Mustafa Mustafa, Pedram Hassanzadeh, Eviatar Bach, and Karthik Kashinath


Total article views: 4,786 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
2,897 1,819 70 4,786 52 38
  • HTML: 2,897
  • PDF: 1,819
  • XML: 70
  • Total: 4,786
  • BibTeX: 52
  • EndNote: 38
Views and downloads (calculated since 12 Apr 2021)
Cumulative views and downloads (calculated since 12 Apr 2021)

Viewed (geographical distribution)

Total article views: 4,786 (including HTML, PDF, and XML) Thereof 4,469 with geography defined and 317 with unknown origin.
Country # Views %
  • 1


Latest update: 27 Nov 2023
Short summary
There is growing interest in data-driven weather forecasting, i.e., to predict the weather by using a deep neural network that learns from the evolution of past atmospheric patterns. Here, we propose three components to add to the current data-driven weather forecast models to improve their performance. These components involve a feature that incorporates physics into the neural network, a method to add data assimilation, and an algorithm to use several different time intervals in the forecast.