Replicating Sensible and Latent Heat Flux Diagnosis with Multilayer Perceptrons on Multi-Year Falkenberg Tower Data

Vertical energy transport from the land surface into the atmosphere in the form of sensible and latent heat flux must be well represented in numerical weather prediction models to allow accurate estimates of near-surface atmospheric variables. Traditionally, these heat fluxes are parameterized relying on Monin-Obukhov Similarity Theory (MOST), which is based on differences in wind speed, air temperature, and humidity between adjacent measurement or model levels. Recently, Wulfmeyer et al. (2024) estimated heat flux with machine learning at much higher accuracy compared to MOST. Their ML model proposed the incorporation of additional predictor variables when estimating latent heat flux (such as solar radiation), which stands in contrast to the classical MOST approach. However, the analysis in Wulfmeyer et al. (2024) is based on a rather short data period in August 2017 at three nearby locations in Oklahoma, USA, which limits the generalizability of the results. Here, we replicate and expand the findings from Wulfmeyer et al. (2024) on a dataset from the boundary layer field site (GM) Falkenberg of the German Meteorological Service over a period of twelve years, covering various seasons and synoptic weather situations. Our findings support the role of incoming shortwave radiation not only for latent but also for sensible heat flux estimates, particularly for other parts of the year. The results thus underline the potential to develop more advanced flux parameterizations beyond MOST. In future research, we intend to investigate the role of other predictor variables, such as vapor pressure deficit or soil moisture, to assess the generalizability of the relations, to judge their performance under extreme conditions, and to derive simple but universally applicable parameterizations.