ASSESSMENT OF ALGORITHMS FOR LAND SURFACE ANALYSIS DOWN-WELLING LONG-WAVE RADIATION AT THE SURFACE

The Satellite Application Facility on Land Surface Analysis (LSA SAF) has been generating Downwelling Surface Longwave Flux (DSLF) from the Meteosat Second Generation (MSG) satellite, on a pixel-by-pixel basis, since the beginning of 2005. The retrieved DSLF corresponds to instantaneous values, estimated every 30-minutes, for the whole Meteosat disk. DSLF can only be indirectly inferred from remotely sensed data. The LSA SAF approach makes use of separate bulk parameterization schemes suitable for clear and cloudy sky conditions, respectively. DSLF retrievals benefit from the signature of clouds and different cloud types on IR (Infrared) and VIS (Visible) channels, complemented with information on atmosphere water content and near surface air temperature available from Numerical Weather Prediction (NWP) fields. The comparison against in situ measurements (mostly obtained from BSRN Baseline Surface Radiation Network stations) suggests the LSA SAF DSLF is generally underestimated. This is particularly apparent for clear sky cases, with biases of the order of –10 Wm to –20 Wm. Cloudy pixels also tend to exhibit negative biases (mostly within –10 Wm to –30 Wm, for European sites), but higher dispersion than in clear cases. As a step forward to eliminate the detected biases, this work presents an assessment of different DSLF algorithms, valid for clear and cloudy conditions together with a new proposed formulation, applicable to all sky conditions. The different schemes are compared with modelled data – MODTRAN – and with in situ (BSRN) measurements. The modelled fluxes are estimated for the TIGR-like database that samples temperature and humidity profiles within ECMWF (European Centre for Medium-Range Weather Forecasts) re-analyses (ERA-40). This database presents a comprehensive and balanced set of atmospheric profiles, suitable for calibration/validation of radiative models/schemes. The new proposed algorithm expresses the effects of cloud cover, atmospheric temperature and humidity through parameterization of the sky emisssivity and the effective sky temperature. The calibration of this new parameterization scheme makes use of the MODTRAN DSLF values. When the algorithm is evaluated against in situ data, it reveals an overall better performance than the remaining formulations, and proves to be the most stable under moist and dry conditions.