NRT Product available since Jun 2020
The MNSLF product provides estimates of daily net and upward surface longwave fluxes, based on SEVIRI observations, and accumulated over 24-h periods. The upward surface longwave flux (USLF) is based on the all-sky land surface temperature (LST) product (MLST-AS; LSA-005). The net surface longwave flux (NSLF) is derived from the USLF and the daily downward surface longwave flux (DSLF) product (DIDSLF; LSA-206).
This Operational product is documented in the following documents:
Please see Product Peer-Review publications in References.
The use of LSA SAF products in publications is kindly requested to be duly acknowledged:
“The MDSSFTD product (Carrer et al., 2019a,b) was developed by CNRM/Météo-France thanks to the support of EUMETSAT. MDSSFTD data were provided by the EUMETSAT Satellite Application Facility on Land Surface Analysis (LSA SAF; Trigo et al., 2011)
Trigo, I. F., C. C. DaCamara, P. Viterbo, J.-L. Roujean, F. Olesen, C. Barroso, F. Camacho-de Coca, D. Carrer, S. C. Freitas, J. García-Haro, B. Geiger, F. Gellens-Meulenberghs, N. Ghilain, J. Meliá, L. Pessanha, N. Siljamo, and A. Arboleda, 2011: The Satellite Application Facility on Land Surface Analysis. Int. J. Remote Sens., 32, 2725-2744, doi: 10.1080/01431161003743199
Carrer, D., Ceamanos, X., Moparthy, S., Vincent, C., C Freitas, S., & Trigo, I. F. (2019a). Satellite Retrieval of Downwelling Shortwave Surface Flux and Diffuse Fraction under All Sky Conditions in the Framework of the LSA SAF Program (Part 1: Methodology). Remote Sensing, 11(21), 2532.
Carrer, D., Moparthy, S., Vincent, C., Ceamanos, X., C Freitas, S., & Trigo, I. F. (2019b). Satellite Retrieval of Downwelling Shortwave Surface Flux and Diffuse Fraction under All Sky Conditions in the Framework of the LSA SAF Program (Part 2: Evaluation). Remote Sensing, 11(22), 2630.
To our knowledge, this is the first time net longwave fluxes are proposed to be operationally derived from geostationary satellites. This is a relevant variable for the assessment and monitoring of the surface net radiation, however, only the upward component is directly linked to top-of-atmosphere observations in the thermal infrared window channels, via their close link with surface temperature. The downward component is only indirectly linked, since the infrared radiation reaching the surface is mostly emitted by the first tens to hundred meters of atmosphere (Trigo et al, 2010).
The net long-wave (NSLF) product is estimated from the down-welling long-wave surface flux product (DSLF; LSA-202) and the all-weather LST (LSA-005), which is the main contributor to the up-welling long-wave flux. It corresponds to the sum of the surface emitted radiation (e LST4, where e is the surface broad-band emissivity) and the atmospheric radiation reflected by the surface ( (1-e).DSLF ).
The adopted algorithm to compute DSLF consists of an hybrid method based on two different bulk parameterisation schemes (e.g., Prata, 1996; Josey et al, 2003) using as input ECMWF forecasts of 2m temperature, 2m dew point temperature and total column water vapour as well as the two cloud products from NWC SAF (Cloud Mask and Effective Cloudiness).
The upward surface longwave flux (USLF) is based on the all-sky land surface temperature (LST) product (MLST-AS; LSA-005), which combines clear-sky LST retrievals (MLST; LSA-001) with cloudy LST estimates obtained via a land surface energy balance model forced with downwelling short and long-wave radiation derived from SEVIRI, together with albedo and vegetation parameters also estimated from SEVIRI observations (Martins et al, 2019).
The NSLF product is computed within the area covered by the MSG disk, on a daily basis. For each time-slot, the NSLF and USLF fields and respective number of 30-minute slots used to derive the fields are disseminated in HDF5 format; the relevant information concerning the data fields is included in the HDF5 attributes.
The quality of the DLSF product depends on the accuracy of both cloudy pixel detection and atmospheric column characterisation (temperature and humidity). The quality of the USLF data mainly depends on the quality of the LST data (MLST-AS). The overall accuracy of the product is 10% (or 10 Wm-2). Limitations were found when the NSLF is close to neutral, associated to a slight overestimation of radiation loss (likely related to an underestimation of DSLF). Also, for surfaces with very strong radiative loss (e.g. deserts) the product tends to overestimate that loss (more details available in the Validation Report). We also advise users to take care when there are missing data in the daily composite: a variable is provided the number of slots used (n_slots_processed), we recommend discarding data with less than 45 slots.