Abstract
Abstract. Surface water and energy fluxes are essential components of the Earth system. Surface latent heat fluxes provide major energy input to the atmosphere. Despite the importance of these fluxes, state-of-the-art data sets of surface energy and water fluxes largely differ. The present paper introduces a new framework for the estimation of surface energy and water fluxes at the land surface, which allows for temporally and spatially high-resolved flux estimates at the quasi-global scale (50° S, 50° N) (High resOlution Land Atmosphere Parameters from Space – HOLAPS v1.0). The framework makes use of existing long-term satellite and reanalysis data records and ensures internally consistent estimates of the surface radiation and water fluxes. The manuscript introduces the technical details of the developed framework and provides results of a comprehensive sensitivity and evaluation study. Overall the root mean square difference (RMSD) was found to be 51.2 (30.7) W m−2 for hourly (daily) latent heat flux, and 84 (38) W m−2 for sensible heat flux when compared against 48 FLUXNET stations worldwide. The largest uncertainties of latent heat flux and net radiation were found to result from uncertainties in the solar radiation flux obtained from satellite data products.
Highlights
Water and energy fluxes between the land surface and atmosphere are essential components of the Earth system
The present paper introduces a new framework for the estimation of surface energy and water fluxes at the land surface, which allows for temporally and spatially high-resolved flux estimates at the quasi-global scale (50◦ S, 50◦ N) (High resOlution Land Atmosphere Parameters from Space – HOLAPS v1.0)
We hereby focus on the accuracy of the surface energy and water fluxes estimated by HOLAPS and evaluate the surface net radiation (RN), solar radiation (Rg) and the surface latent (LE) and sensible heat (H ) fluxes for all experiments
Summary
Water and energy fluxes between the land surface and atmosphere are essential components of the Earth system. Different approaches exist to infer land turbulent surface fluxes by either one of the following methods (Kalma et al, 2008; Wang and Dickinson, 2012): (1) simulations by an offline land surface model (Roads and Betts, 2000); (2) empirical statistical models, e.g., obtained by machine learning techniques or neural networks (Jung et al, 2011); (3) surface energy balance models forced either by satellite remote sensing or reanalysis data (Bastiaanssen et al, 1998; Su, 2002); (4) methods based on Penman–Monteith or Priestley– Taylor equations (Fisher et al, 2008; Miralles et al, 2011; Mu et al, 2007; Zhang et al, 2015); and (5) spatial variability methods (Roerink et al, 2000; Peng et al, 2013b; Peng and Loew, 2014). For field and continental scale agricultural applications, ALEXI/DisALEXI (Anderson et al, 2007; Norman et al, 2003) already have the ability to provide very high spatial resolution surface fluxes (up to 10 m resolution) with the Published by Copernicus Publications on behalf of the European Geosciences Union
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