Geoscientific Model Development
Geoscientific Model Development
GMD
Articles | Volume 14, issue 9
Articles | Volume 14, issue 9
https://doi.org/10.5194/gmd-14-5467-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-14-5467-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 14, issue 9
Model evaluation paper
 | 
03 Sep 2021
Model evaluation paper |  | 03 Sep 2021

Multi-sensor analyses of the skin temperature for the assimilation of satellite radiances in the European Centre for Medium-Range Weather Forecasts (ECMWF) Integrated Forecasting System (IFS, cycle 47R1)

Sebastien Massart, Niels Bormann, Massimo Bonavita, and Cristina Lupu

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Cited articles

Bonavita, M., Isaksen, L., and Hólm, E.: On the use of EDA background error variances in the ECMWF 4D-Var, Q. J. Roy. Meteor. Soc., 138, 1540–1559, 2012. a
Bormann, N., Lupu, C., Geer, A. J., Lawrence, H., Weston, P., and English, S.: Assessment of the forecast impact of suface-sensitive microwave radiances over land and sea-ice, ECMWF Technical Memoranda, Technical Memorandum No. 804, https://doi.org/10.21957/qyh34roht, 2017. a, b, c
Bormann, N., Lawrence, H., and Farnan, J.: Global observing system experiments in the ECMWF assimilation system, ECMWF Technical Memoranda, Technical Memorandum No. 839, https://doi.org/10.21957/sr184iyz, 2019. a
Browne, P., de Rosnay, P., Zuo, H., Bennett, A., and Dawson, A.: Weakly Coupled Ocean-Atmosphere Data Assimilation in the ECMWF NWP System, Remote Sens.-Basel, 11, 234, https://doi.org/10.3390/rs11030234, 2019. a
Buehner, M.: Ensemble-derived stationary and flow-dependent background-error covariances: Evaluation in a quasi-operational NWP setting, Q. J. Roy. Meteor. Soc., 131, 1013–1043, https://doi.org/10.1256/qj.04.15, 2005. a
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Numerical weather predictions combine data from satellites with atmospheric forecasts. Some satellites measure the radiance emitted by the Earth's surface. To use this data, one must have knowledge of the surface properties, like the temperature of the thin layer above the surface. Error in this temperature leads to a misuse of the satellite data and affects the quality of the weather forecast. We updated our approach to better estimate this temperature, which should help improve the forecast.
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