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Genetic Programming for Estimation of Heat Flux between the Atmosphere and Sea Ice in Polar Regions

Published: 11 July 2015 Publication History

Abstract

The Earth surface and atmosphere exchange heat via turbulent fluxes. An accurate description of the heat exchange is essential in modelling the weather and climate. In these models the heat fluxes are described applying the Monin-Obukhov similarity theory, where the flux depends on the air-surface temperature difference and wind speed. The theory makes idealized assumptions and the resulting estimates often have large errors. This is the case particularly in conditions when the air is warmer than the Earth surface, i.e., the atmospheric boundary layer is stably stratified, and turbulence is therefore weak. This is a common situation over snow and ice in the Arctic and Antarctic. In this paper, we present alternative models for heat flux estimation evolved by means of genetic programming (GP). To this aim, we utilize the best heat flux data collected in the Arctic and Antarctic sea ice zones. We obtain GP models that are more accurate, robust, and conceptually novel from the viewpoint of meteorology. Contrary to the Monin-Obukhov theory, the GP equations are not solely based on the air-surface temperature difference and wind speed, but include also radiative fluxes that improve the performance of the method. These results open the door to a new class of approaches to heat flux prediction with potential applications in weather and climate models.

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  1. Genetic Programming for Estimation of Heat Flux between the Atmosphere and Sea Ice in Polar Regions

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      cover image ACM Conferences
      GECCO '15: Proceedings of the 2015 Annual Conference on Genetic and Evolutionary Computation
      July 2015
      1496 pages
      ISBN:9781450334723
      DOI:10.1145/2739480
      Publication rights licensed to ACM. ACM acknowledges that this contribution was authored or co-authored by an employee, contractor or affiliate of a national government. As such, the Government retains a nonexclusive, royalty-free right to publish or reproduce this article, or to allow others to do so, for Government purposes only.

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      Published: 11 July 2015

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      Author Tags

      1. genetic programming
      2. heat flux
      3. meteorology
      4. modeling
      5. symbolic regression

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      GECCO '15 Paper Acceptance Rate 182 of 505 submissions, 36%;
      Overall Acceptance Rate 1,669 of 4,410 submissions, 38%

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      • (2021)Inverse design of organic light-emitting diode structure based on deep neural networksNanophotonics10.1515/nanoph-2021-0434Online publication date: 3-Nov-2021
      • (2016)Evolving Spatially Aggregated Features from Satellite Imagery for Regional ModelingParallel Problem Solving from Nature – PPSN XIV10.1007/978-3-319-45823-6_66(707-716)Online publication date: 31-Aug-2016

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