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Advances in Science and Research Contributions in Applied Meteorology and Climatology
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Volume 12, issue 1
Adv. Sci. Res., 12, 137–140, 2015
https://doi.org/10.5194/asr-12-137-2015
© Author(s) 2015. This work is distributed under
the Creative Commons Attribution 3.0 License.
Adv. Sci. Res., 12, 137–140, 2015
https://doi.org/10.5194/asr-12-137-2015
© Author(s) 2015. This work is distributed under
the Creative Commons Attribution 3.0 License.

  30 Jun 2015

30 Jun 2015

Diurnal temperature cycle deduced from extreme daily temperatures and impact over a surface reanalysis system

F. Besson1, E. Bazile2, C. Soci2, J.-M. Soubeyroux1, G. Ouzeau1, and M. Perrin3 F. Besson et al.
  • 1Meteo-France, DCSC/AVH, Toulouse, France
  • 2Meteo-France, CNRM/GAME, Toulouse, France
  • 3University of Toulouse III – Paul Sabatier, Toulouse, France

Abstract. Due to the evolution of the observation network, hourly 2 m temperature analysis performed by reanalysis systems shows temporal inhomogeneities. The observation network gap is less present for extreme daily temperature observations. In order to reduce inhomogeneities and enable a climatological use of temperature analysis, information from extreme temperatures could be useful. In this study, the diurnal temperature cycle has been reconstructed for stations which only record extreme temperatures. These new "pseudo" hourly temperature observations are then provided to the analysis system. Two methods have been used to deduce hourly temperatures from extremes and compared to real observations. The results have shown that using those new pseudo-observations as an input for two different reanalysis systems enables reducing the bias in temperature analysis.

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Due to the evolution of the observation network, hourly 2m temperature analysis performed by reanalysis systems shows temporal inhomogeneities. In this study, the diurnal temperature cycle has been reconstructed for stations which only record extreme temperatures to produce new “pseudo” hourly temperature observations. Then they are provided to analysis systems; the results have shown that it enables reducing the bias in temperature analysis.
Due to the evolution of the observation network, hourly 2m temperature analysis performed by...
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