Articles | Volume 13, issue 10
https://doi.org/10.5194/amt-13-5481-2020
https://doi.org/10.5194/amt-13-5481-2020
Research article
 | 
14 Oct 2020
Research article |  | 14 Oct 2020

Inter-calibrating SMMR brightness temperatures over continental surfaces

Samuel Favrichon, Carlos Jimenez, and Catherine Prigent

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Cited articles

Andersson, A., Fennig, K., Klepp, C., Bakan, S., Graßl, H., and Schulz, J.: The Hamburg Ocean Atmosphere Parameters and Fluxes from Satellite Data – HOAPS-3, Earth Syst. Sci. Data, 2, 215–234, https://doi.org/10.5194/essd-2-215-2010, 2010. a
Berg, W., Sapiano, M. R., Horsman, J., and Kummerow, C.: Improved geolocation and earth incidence angle information for a fundamental climate data record of the SSM/I sensors, IEEE T. Geosci. Remote, 51, 1504–1513, https://doi.org/10.1109/TGRS.2012.2199761, 2013. a, b
Berg, W.: GPM GMI_R Common Calibrated Brightness Temperatures Collocated L1C 1.5 hours 13 km V05, Greenbelt, MD, USA, Goddard Earth Sciences Data and Information Services Center (GES DISC), https://doi.org/10.5067/GPM/GMI/R/1C/05, 2016. a
Berg, W., Bilanow, S., Chen, R., Datta, S., Draper, D., Ebrahimi, H., Farrar, S., Jones, W. L., Kroodsma, R., McKague, D., Payne, V., Wang, J., Wilheit, T., and Yang, J. X.: Intercalibration of the GPM microwave radiometer constellation, J. Atmos. Ocean. Tech., 33, 2639–2654, https://doi.org/10.1175/JTECH-D-16-0100.1, 2016. a
Berg, W., Kroodsma, R., Kummerow, C., and McKague, D.: Fundamental Climate Data Records of Microwave Brightness Temperatures, Remote Sens., 10, 1306, https://doi.org/10.3390/rs10081306, 2018. a, b, c
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Short summary
Long-term monitoring of satellite-derived variables is necessary for a better understanding of the evolution of Earth parameters at global scale. However different instruments' observations used over the years need to be inter-calibrated with each other to provide meaningful information. This paper describes how a linear correction can improve the observations from the Scanning Multichannel Microwave Radiometer over continental surfaces to be more consistent with more recent radiometers.