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Atmospheric Measurement Techniques An interactive open-access journal of the European Geosciences Union
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Preprints
https://doi.org/10.5194/amt-2020-166
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/amt-2020-166
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

  13 May 2020

13 May 2020

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A revised version of this preprint was accepted for the journal AMT and is expected to appear here in due course.

Improvement of numerical weather prediction model analysis during fog conditions through the assimilation of ground-based microwave radiometer observations: a 1D-Var study

Pauline Martinet1, Domenico Cimini2, Frédéric Burnet1, Benjamin Ménétrier1, Yann Michel1, and Vinciane Unger1 Pauline Martinet et al.
  • 1CNRM UMR 3589, Meteo-France/CNRS, Toulouse, France
  • 2IMAA-CNR, Potenza, Italy

Abstract. This paper investigates the potential benefit of ground-based microwave radiometers (MWRs) to improve the ini- tial state (analysis) of current numerical weather prediction (NWP) systems during fog conditions. To that end, temperature,humidity and liquid water path (LWP) retrievals have been performed using a one-dimensional variational technique (1D-Var) during a fog dedicated field-experiment performed over winter 2016–2017 in France. In-situ measurements from a 120 m tower and radiosoundings are used to assess the improvement brought by the 1D-Var analysis to the background. A sensitivity study demonstrates the importance of the cross-correlations between temperature and specific humidity in the background-error-covariance matrix as well as the bias-correction applied on MWR raw measurements. With the optimal 1D-Var configuration, a root-mean-square error smaller than 1.5 K (resp. 0.8 K) for temperature and 1 g kg−1 (resp. 0.5 g kg−1) for humidity is obtained up to 6 km altitude (resp. within the fog layer up to 250 m). A thin-radiative fog case study has shown that the assimilation of MWR observations was able to correct large temperature errors of the AROME model as well as vertical and temporal errors observed in the fog lifecycle. During missed fog profiles, 1D-Var increments pull towards lower temperature close to the ground and higher temperature above 100 m altitude, i.e. higher atmospheric stability. The largest analysis increments and background errors are observed during false alarms when the AROME forecasts tend to significantly overestimate the temperature cooling. The impact on specific humidity was found neutral to slightly positive. The impact on LWP was found significant with 1D-Var increments within 200 g m−2 and RMSE with respect to MWR statistical regressions decreased from 101 g m−2 in the background to 27 g m−2 in the 1D-Var analysis. These encouraging results led to the deployment of 8 MWRs during the international SOFOG3D (SOuth FOGs 3D experiment for fog processes study) experiment conducted by Météo-France.

Pauline Martinet et al.

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Pauline Martinet et al.

Pauline Martinet et al.

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Latest update: 27 Nov 2020
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Short summary
Each year large human and economical losses are due to fog episodes. However, fog forecasts remain quite inaccurate partly due to a lack of observations in the atmospheric boundary layer. The benefit of ground-based microwave radiometers has been investigated and has demonstrated their capability of significantly improving the initial state of temperature and liquid water content profiles in current numerical weather prediction model paving the way for improved fog forecasts in the future.
Each year large human and economical losses are due to fog episodes. However, fog forecasts...
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