MSG Infrared Emissivity – Direct Retrieval (MEMD)

[LSA-006]

NRT Product Available since Jun 2020

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MSG Infrared emissivity – Direct retrieval is a direct retrieval emissivity dataset based on a Kalman filter approach, a physical scheme that allows the simultaneous retrieval of surface emissivity and temperature (Masiello et al., 2015, 2013). The retrievals are performed hourly and are then composited daily, using all available estimates within the last 3 days. The land surface emissivity is a crucial parameter for the retrieval of Land surface Temperature (LST) from space.

Product Documentation

This Pre-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:
LST was provided by the EUMETSAT Satellite Application Facility on Land Surface Analysis (LSA SAF; Trigo et al., 2011)
http://lsa-saf.eumetsat.int

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

The retrieval of emissivity is based on clear-sky measurements from MSG system in the thermal infrared window (MSG/SEVIRI channels IR8.7, IR10.8 and IR12.0). Retrievals are performed on an hourly basis and are then composited daily. The identification of cloudy pixels is based on the cloud mask generated by the Nowcasting and Very Short Range Forecasting Satellite Application Facility (NWC SAF) software.

Algorithm Description

Estimates of infrared surface emissivity are based on a Kalman filter approach, a physical scheme that allows the simultaneous retrieval of surface emissivity and temperature (Masiello et al., 2015, 2013).  At each observation time, the Kalman filter retrieval involves the linearization of a forward model, which in this case corresponds to a radiative transfer model, and a set of iterations until a cost function is reduced below a given threshold. The CAMEL dataset  is used to define a background for the channel emissivity (state vector and covariance). Information on atmospheric conditions, namely profiles of temperature, water vapour and ozone, are obtained from ECMWF forecasts. Instant emissivity values are estimated from TOA brightness temperatures of SEVIRI infrared channels centered on 8.7, 10.8 and 12.0 mm. Daily values of emissivity are derived for each channel from the hourly instantaneous values, using all available estimates within the last 3 days.

Data Characteristics

The MEMD MSG product is computed within the area covered by the MSG disk, on a daily basis. The emissivity fields and respective Quality Control (QC) and uncertainty data are disseminated in HDF5 format; the relevant information concerning the data fields is included in the HDF5 attributes.

Product Uncertainties

The quality of the MEMD product depends on sensor performance (stability of the spectral response function, signal-to-noise ratio, radiometric resolution and calibration accuracy), accuracy of cloudy pixels identification, and accuracy of atmospheric corrections (atmospheric data and radiative transfer). The reduced spectral contrast of the emissivity of vegetated surfaces decreases the accuracy of the direct temperature-emissivity separation methods. It is assumed that surface emissivity should vary slowly and most of the sharp changes in Kalman-Filter estimated emissivity values are, in most cases, likely to be associated with cloud contamination, or retrieval uncertainties. The MEMD uncertainties are therefore estimated from the t-student standard deviation of the emissivity estimates of the three days used in the composite. The t-student standard deviation particularly useful as it will penalize lower samples sizes, which are associated to higher cloud coverage (and a higher probability of occurrence of cloud contamination). An automatic Quality Control (QC) is performed on the data, and the quality information is provided on a pixel-by-pixel basis. The three confidence levels are considered (above nominal, nominal and below nominal) corresponding to estimated uncertainties of emissivity values (respectively less than 0.01, between 0.01 and 0.02 and above 0.02).

Masiello, G., Serio, C., De Feis, I., Amoroso, M., Venafra, S., Trigo, I.F., Watts, P., 2013. Kalman filter physical retrieval of surface emissivity and temperature from geostationary infrared radiances. Atmos. Meas. Tech. 6, 3613–3634. doi:10.5194/amt-6-3613-2013

Masiello, G., Serio, C., Venafra, S., Liuzzi, G., Goettsche, F., Trigo, I.F., Watts, P., 2015. Kalman filter physical retrieval of surface emissivity and temperature from SEVIRI infrared channels: A validation and intercomparison study. Atmos. Meas. Tech. 8, 2981–2997. doi:10.5194/amt-8-2981-2015