The present study refreshes and improves the work first introduced by Lettau (1951). A linearized land-atmosphere model, forced by times series of incoming radiation at the land-surface, is solved analytically. With this model, the profiles of temperature and heat fluxes in the soil and the Atmospheric Boundary Layer (ABL) can be expressed in terms of temporal Fourier series. Moreover the surface variables (temperature, specific humidity, surface fluxes) are also derived analytically and are expressed as function of both surface parameters (friction velocity, vegetation height, aerodynamic resistance, stomatal conductance) and frequency of the forcing of incoming radiation.

This original approach has several advantages. The model only requires very little data to perform well (time series of incoming radiation at the land-surface, mean daily specific humidity and potential temperature at any given height) and allows theoretically studying the spectral response of coupled land-atmosphere systems to any forcing of incoming radiation at the land-surface. In particular, this model is shown to be helpful for the conception of remote-sensing tools. The spectral analysis of the coupling between the land and the atmosphere helps investigate the relevance of the sensor measurements, according to the temporal resolution of the sensor as well as the penetration depth of its electromagnetic wave.

The model will also be used to analyse the diurnal and (temporal) spectral dependency of surface variables. In particular the Evaporative Fraction (EF) and Bowen Ratio diurnal shapes are explained as function of weather and surface conditions. EF is shown to remain a diurnal constant under restricting conditions and its application in conjonction with remote sensing is discussed. Moreover, the EF pseudo-constant asymptotical value is given as function of surface parameters.

Then, the influence of a white-noise error in the land-surface energy budget is investigated. This error represents inherent internal and external parameterization errors of the land-surface. The repercussion of such an error onto the soil and ABL profiles is first describe. Then the influence of this error on the accuracy of data assimilation (filtering) of remotely sensed surface measurements (such as brightness temperature) is discussed. Finally, the repercussion of this land-surface modeling error on weather forecast will also be presented.

Future improvements of the model will be discussed in the final part of the study.