a simple two-dimensional parameterisation for flux footprint prediction (ffp)
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2015
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Abstract
Flux footprint models are often used for interpretation of flux tower
measurements, to estimate position and size of surface source areas, and the
relative contribution of passive scalar sources to measured fluxes. Accurate
knowledge of footprints is of crucial importance for any upscaling exercises
from single site flux measurements to local or regional scale. Hence,
footprint models are ultimately also of considerable importance for improved
greenhouse gas budgeting. With increasing numbers of flux towers within large
monitoring networks such as FluxNet, ICOS (Integrated Carbon Observation System), NEON (National Ecological Observatory Network), or AmeriFlux, and with increasing temporal range of observations from such
towers (of the order of decades) and availability of airborne flux
measurements, there has been an increasing demand for reliable footprint
estimation. Even though several sophisticated footprint models have been
developed in recent years, most are still not suitable for application to
long time series, due to their high computational demands. Existing fast
footprint models, on the other hand, are based on surface layer theory and
hence are of restricted validity for real-case applications.
To remedy such shortcomings, we present the two-dimensional parameterisation for Flux Footprint Prediction (FFP), based on a novel scaling approach for the crosswind distribution of the flux footprint and on an improved version of the footprint parameterisation of Kljun et al. (2004b). Compared to the latter, FFP now provides not only the extent but also the width and shape of footprint estimates, and explicit consideration of the effects of the surface roughness length. The footprint parameterisation has been developed and evaluated using simulations of the backward Lagrangian stochastic particle dispersion model LPDM-B (Kljun et al., 2002). Like LPDM-B, the parameterisation is valid for a broad range of boundary layer conditions and measurement heights over the entire planetary boundary layer. Thus, it can provide footprint estimates for a wide range of real-case applications.
The new footprint parameterisation requires input that can be easily determined from, for example, flux tower measurements or airborne flux data. FFP can be applied to data of long-term monitoring programmes as well as be used for quick footprint estimates in the field, or for designing new sites.
To remedy such shortcomings, we present the two-dimensional parameterisation for Flux Footprint Prediction (FFP), based on a novel scaling approach for the crosswind distribution of the flux footprint and on an improved version of the footprint parameterisation of Kljun et al. (2004b). Compared to the latter, FFP now provides not only the extent but also the width and shape of footprint estimates, and explicit consideration of the effects of the surface roughness length. The footprint parameterisation has been developed and evaluated using simulations of the backward Lagrangian stochastic particle dispersion model LPDM-B (Kljun et al., 2002). Like LPDM-B, the parameterisation is valid for a broad range of boundary layer conditions and measurement heights over the entire planetary boundary layer. Thus, it can provide footprint estimates for a wide range of real-case applications.
The new footprint parameterisation requires input that can be easily determined from, for example, flux tower measurements or airborne flux data. FFP can be applied to data of long-term monitoring programmes as well as be used for quick footprint estimates in the field, or for designing new sites.
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kljun2015geoscientifica
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| Authors | ;N. Kljun;P. Calanca;M. W. Rotach;H. P. Schmid |
| Journal | international journal of quantum chemistry |
| Year | 2015 |
| DOI |
10.5194/gmd-8-3695-2015
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