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Atmospheric Measurement Techniques An interactive open-access journal of the European Geosciences Union
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Volume 8, issue 3
Atmos. Meas. Tech., 8, 1055–1071, 2015
https://doi.org/10.5194/amt-8-1055-2015
© Author(s) 2015. This work is distributed under
the Creative Commons Attribution 3.0 License.
Atmos. Meas. Tech., 8, 1055–1071, 2015
https://doi.org/10.5194/amt-8-1055-2015
© Author(s) 2015. This work is distributed under
the Creative Commons Attribution 3.0 License.

Research article 04 Mar 2015

Research article | 04 Mar 2015

A layer-averaged relative humidity profile retrieval for microwave observations: design and results for the Megha-Tropiques payload

R. G. Sivira1,*, H. Brogniez1, C. Mallet1, and Y. Oussar2 R. G. Sivira et al.
  • 1Université Versailles St-Quentin; Sorbonne Universités, UPMC Univ. Paris 06; CNRS/INSU, LATMOS-IPSL, Guyancourt, France
  • 2Laboratoire de Physique et d'Etudes des Matériaux (LPEM), UMR8213, ESPCI-ParisTech, Paris, France
  • *now at: Meteo Protect SAS, Paris, France

Abstract. A statistical method trained and optimized to retrieve seven-layer relative humidity (RH) profiles is presented and evaluated with measurements from radiosondes. The method makes use of the microwave payload of the Megha-Tropiques platform, namely the SAPHIR sounder and the MADRAS imager. The approach, based on a generalized additive model (GAM), embeds both the physical and statistical characteristics of the inverse problem in the training phase, and no explicit thermodynamical constraint – such as a temperature profile or an integrated water vapor content – is provided to the model at the stage of retrieval. The model is built for cloud-free conditions in order to avoid the cases of scattering of the microwave radiation in the 18.7–183.31 GHz range covered by the payload. Two instrumental configurations are tested: a SAPHIR-MADRAS scheme and a SAPHIR-only scheme to deal with the stop of data acquisition of MADRAS in January 2013 for technical reasons. A comparison to learning machine algorithms (artificial neural network and support-vector machine) shows equivalent performance over a large realistic set, promising low errors (biases < 2.2%RH) and scatters (correlations > 0.8) throughout the troposphere (150–900 hPa). A comparison to radiosonde measurements performed during the international field experiment CINDY/DYNAMO/AMIE (winter 2011–2012) confirms these results for the mid-tropospheric layers (correlations between 0.6 and 0.92), with an expected degradation of the quality of the estimates at the surface and top layers. Finally a rapid insight of the estimated large-scale RH field from Megha-Tropiques is presented and compared to ERA-Interim.

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