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Atmospheric Measurement Techniques An interactive open-access journal of the European Geosciences Union
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Volume 10, issue 10
Atmos. Meas. Tech., 10, 3947–3961, 2017
https://doi.org/10.5194/amt-10-3947-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 3.0 License.
Atmos. Meas. Tech., 10, 3947–3961, 2017
https://doi.org/10.5194/amt-10-3947-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 3.0 License.

Research article 25 Oct 2017

Research article | 25 Oct 2017

Long-term observations minus background monitoring of ground-based brightness temperatures from a microwave radiometer network

Francesco De Angelis et al.
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Baldauf, M., Seifert, A., Forstner, J., Majewski, D., Raschendorfer, M., and Reinhardt, T.: Operational convective-scale numerical weather prediction with the COSMO model: Description and sensitivities, Mon. Weather Rev., 139, 3887–3905, https://doi.org/10.1175/mwr-d-10-05013.1, 2011.
Blumberg, W. G., Turner, D. D., Löhnert, U., and Castleberry, S.: Ground-Based Temperature and Humidity Profiling Using Spectral Infrared and Microwave Observations. Part II: Actual Retrieval Performance in Clear-Sky and Cloudy Conditions, J. Appl. Meteor. Climatol., 54, 2305–2319, https://doi.org/10.1175/JAMC-D-15-0005.1, 2015.
Brousseau, P., Berre, L., Bouttier, F., and Desroziers, G.: Background error covariances for a convective scale data assimilation system: AROME 3D-Var, Q. J. Roy. Meteor. Soc., 137, 409–422, https://doi.org/10.1002/qj.750, 2011.
Brousseau, P., Desroziers, G., Bouttier, F., and Chapnik, B.: A posteriori diagnostics of the impact of observations on the AROME-France convective-scale data assimilation system Quart, J. Roy. Meteor. Soc., 140, 982–994, 2014.
Bubnová, R., Hello, G., Bénard, P., and Geleyn, J.-F.: Integration of the fully elastic equations cast in the hydrostatic pressure terrain-following in the framework of the ARPEGE/ALADIN NWP system, Mon. Weather Rev., 123, 515–535, https://doi.org/10.1175/1520-0493(1995)123<0515:IOTFEE>2.0.CO;2, 1995.
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Modern data assimilation systems require knowledge of the typical differences between observations and model background (O–B). This work illustrates a 1-year O–B analysis for ground-based microwave radiometer (MWR) observations in clear-sky conditions for a prototype network of six MWRs in Europe. Observations are MWR brightness temperatures (TB). Background profiles extracted from the output of a convective-scale model are used to simulate TB through the radiative transfer model RTTOV-gb.
Modern data assimilation systems require knowledge of the typical differences between...
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