Preprints
https://doi.org/10.5194/egusphere-2022-1222
https://doi.org/10.5194/egusphere-2022-1222
21 Nov 2022
 | 21 Nov 2022

Data Driven Regional Weather Forecasting

Randall Clark, Luke Fairbanks, Ramon Sanchez, Pacharadech Wacharanan, and Henry Abarbanel

Abstract. Using data alone, without knowledge of underlying physical models, nonlinear discrete time regional forecasting dynamical rules are constructed employing well tested methods from applied mathematics and nonlinear dynamics. Observations of environmental variables such as wind velocity, temperature, pressure, etc allow the development of forecasting rules that predict the future of these variables only. A regional set of observations with appropriate sensors allows one to forgo standard considerations of spatial resolution and uncertainties in the properties of detailed physical models. Present global or regional models require specification of details of physical processes globally or regionally, and the ensuing, often heavy, computational requirements provide information of the time variation of many quantities not of interest locally. In this paper we formulate the construction of data driven forecasting (DDF) models of geophysical processes and demonstrate how this works within the familiar example of a ‘global’ model of shallow water flow on a mid-latitude β plane. A sub-region, where observations are made, of the global flow is selected. A discrete time dynamical forecasting system is constructed from these observations. DDF forecasting accurately predicts the future of observed variables.

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Randall Clark, Luke Fairbanks, Ramon Sanchez, Pacharadech Wacharanan, and Henry Abarbanel

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on egusphere-2022-1222', Yicun Zhen, 30 Nov 2022
    • CC1: 'Reply on RC1 from all the Authors', Randall Clark, 02 Dec 2022
      • RC2: 'Reply on CC1', Yicun Zhen, 02 Dec 2022
  • RC3: 'Comment on egusphere-2022-1222', Said Ouala, 09 Jan 2023
  • EC1: 'Comment on egusphere-2022-1222', Pierre Tandeo, 12 Jan 2023
  • AC1: 'Comment on egusphere-2022-1222', Luke Fairbanks, 08 Feb 2023
  • AC2: 'Comment on egusphere-2022-1222', Luke Fairbanks, 08 Feb 2023
  • AC3: 'Comment on egusphere-2022-1222', Luke Fairbanks, 09 Feb 2023

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on egusphere-2022-1222', Yicun Zhen, 30 Nov 2022
    • CC1: 'Reply on RC1 from all the Authors', Randall Clark, 02 Dec 2022
      • RC2: 'Reply on CC1', Yicun Zhen, 02 Dec 2022
  • RC3: 'Comment on egusphere-2022-1222', Said Ouala, 09 Jan 2023
  • EC1: 'Comment on egusphere-2022-1222', Pierre Tandeo, 12 Jan 2023
  • AC1: 'Comment on egusphere-2022-1222', Luke Fairbanks, 08 Feb 2023
  • AC2: 'Comment on egusphere-2022-1222', Luke Fairbanks, 08 Feb 2023
  • AC3: 'Comment on egusphere-2022-1222', Luke Fairbanks, 09 Feb 2023
Randall Clark, Luke Fairbanks, Ramon Sanchez, Pacharadech Wacharanan, and Henry Abarbanel
Randall Clark, Luke Fairbanks, Ramon Sanchez, Pacharadech Wacharanan, and Henry Abarbanel

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Short summary
We've trained a machine learning system, based on data assimilation and data driven forecasting fundamentals, to predict 'shallow water' fluid dynamics, such as atmosphere, using limited amounts of data from regional measurements within a twin experiment. The model performs well and is robust to noise, leveraging intuition of physical dynamics at play in the shallow water system to help the machine learning system contextualize the data.