Towards Energy-Balance Closure with a Model of Dispersive Heat Fluxes

Research output: Contribution to journalResearch articleContributedpeer-review

Contributors

  • Luise Wanner - , Institute of Hydrology and Meteorology, Chair of Meteorology, TUD Dresden University of Technology, Institute of Meteorology and Climate Research, Karlsruhe Institute of Technology (Author)
  • Martin Jung - , Max Planck Institute for Biogeochemistry (Author)
  • Sreenath Paleri - , University of Wisconsin-Madison, University of Oklahoma, National Oceanic and Atmospheric Administration (Author)
  • Brian J. Butterworth - , University of Colorado Boulder, National Oceanic and Atmospheric Administration (Author)
  • Ankur R. Desai - , University of Wisconsin-Madison (Author)
  • Matthias Sühring - , Leibniz University Hannover (LUH), Pecanode GmbH (Author)
  • Matthias Mauder - , Institute of Hydrology and Meteorology, Chair of Meteorology, TUD Dresden University of Technology, Institute of Meteorology and Climate Research, Karlsruhe Institute of Technology, Institute of Geography and Geoecology (Author)

Abstract

In the last decades the energy-balance-closure problem has been thoroughly investigated from different angles, resulting in approaches to reduce but not completely close the surface energy balance gap. Energy transport through secondary circulations has been identified as a major cause of the remaining energy imbalance, as it is not captured by eddy covariance measurements and can only be measured additionally with great effort. Several models have already been developed to close the energy balance gap that account for factors affecting the magnitude of the energy transport by secondary circulations. However, to our knowledge, there is currently no model that accounts for thermal surface heterogeneity and that can predict the transport of both sensible and latent energy. Using a machine-learning approach, we developed a new model of energy transport by secondary circulations based on a large data set of idealized large-eddy simulations covering a wide range of unstable atmospheric conditions and surface-heterogeneity scales. In this paper, we present the development of the model and show first results of the application on more realistic LES data and field measurements from the CHEESEHEAD19 project to get an impression of the performance of the model and how the application can be implemented on field measurements. A strength of the model is that it can be applied without additional measurements and, thus, can retroactively be applied to other eddy covariance measurements to model energy transport through secondary circulations. Our work provides a promising mechanistic energy balance closure approach to 30-min flux measurements.

Details

Original languageEnglish
Article number25
JournalBoundary-Layer Meteorology
Volume190
Issue number5
Publication statusPublished - May 2024
Peer-reviewedYes

External IDs

ORCID /0000-0002-8789-163X/work/163766089

Keywords

ASJC Scopus subject areas

Keywords

  • Dispersive fluxes, Eddy covariance, Large-eddy simulation, Machine learning, Secondary circulations