For calculating Flux Densities of momentum, energy and scalars
This service provides two distinct methods for calculating flux densities:
The eddy covariance method is used to determine turbulent fluxes across a boundary layer interface,
as for example, the land-atmosphere or the sea-atmosphere interface. The method is theoretically simple in
concept, provided that fast response sensors (determining atmospheric parameters and scalars at high frequency)
are available. It can directly determine flux densities of energy, momentum and gases or aerosol, in a
semi-continuous manner. A number of assumptions are taken into account and a number of pre calculation and
post calculation data treatment procedures are carried out. Produced results are presented in graphical form
and in an ASCII file for the perusal of the user.
More details on the theory and the inputs to the GUI for
implementing the service, are discussed
The gradient method can be used to calculate the momentum, energy and scalar flux densities.
The method depends upon measurement of vertical gradients of concentration, temperature and wind speed above
the surface (also for example the scalar water vapour concentration). The meteorological variables are utilized
in order to quantify the eddy diffusivity for momentum or sensible and latent heat, which is assumed to be
similar to that for trace gases and aerosols. The technique has been widely used and appears to work well in
instances where analytical precision is sufficient to enable reliable determination of the concentration
gradient of the trace species above the surface and where atmospheric stability is close to neutral.
One proviso for use of the gradient technique is that the species measured are chemically conservative
and thus the flux is invariant with height.
Produced results are presented in graphical form and in an ASCII file for the perusal of the user.
More details on the theory and the inputs to the GUI for implementing the service, are discussed
Please note that the algorithms/models are subject to proprietary rights and not free.
The service is subject to the project NEANIAS and EOSC perusal rules and also subject to international
Please consider acknowledging the
if you use the results of this service in any paper or communication.
NEANIAS is funded by European Union under Horizon 2020 research and innovation
programme via grant agreement No. 863448.