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Natural Hazards and Earth System Sciences An interactive open-access journal of the European Geosciences Union
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Short summary
Impact of assimilation of various observation data set on the forecasts is studied in a mesoscale weather model. The ground-based GNSS Zenithal Total Delay data set providing information on humidity has the largest impact on the analyses and the forecasts. Indeed it represents an evenly spread and frequent data set available at each analysis time over the model domain. Moreover, the reprocessing of these data also improves the forecast quality but this impact is not statistically significant.
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https://doi.org/10.5194/nhess-2020-153
https://doi.org/10.5194/nhess-2020-153

  25 May 2020

25 May 2020

Review status: a revised version of this preprint was accepted for the journal NHESS and is expected to appear here in due course.

Data impact studies with the AROME WMED reanalysis of the HyMeX SOP1

Nadia Fourrié, Mathieu Nuret, Pierre Brousseau, and Olivier Caumont Nadia Fourrié et al.
  • CNRM,Université de Toulouse, Météo-France, CNRS, Toulouse, France

Abstract. This paper presents the results of several observing system experiments (OSEs) performed with AROME-WMED. This model is the HyMeX (Hydrological cycle in the Mediterranean Experiment) dedicated version (Fourrié et al., 2019) of the French operational meso-scale model AROME. The second and final reanalyses assimilated most of all available data for a 2 month period corresponding to the first Special Observation Period of HyMeX. In order to assess the impact of various observation data set assimilation on the forecasts, several OSEs or also-called denial experiments, were carried out. In this study, impact of a dense reprocessed network of high quality Global Navigation Satellite System (GNSS) Zenithal Total Delay (ZTD) observations, reprocessed wind-Profilers, lidar-derived vertical profiles of humidity (ground and airborne) and Spanish radar data, is thus discussed.

Among the evaluated observations, it is found that the ground-based GNSS ZTD data set provides the largest impact on the analyses and the forecasts as it represents an evenly spread and frequent data set providing information at each analysis time over the AROME-WMED domain. The impact of the reprocessing of GNSS ZTD data also improves the forecast quality but this impact is not statistically significant. The assimilation of the Spanish radar data improves the very short term forecast quality as well as the short term forecasts but this impact remains located over Spain. Marginal impact from wind profilers was observed on wind background quality. No impacts have been found regarding lidar data as they represent a very small data set.

Nadia Fourrié et al.

 
Status: closed
Status: closed
AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment
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Status: closed
Status: closed
AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment
Printer-friendly Version - Printer-friendly version Supplement - Supplement

Nadia Fourrié et al.

Data sets

AROME_WMED reanalysis_2 N. Fourrié - CNRM (PI or Lead scientist) M. Nuret - CNRM (PI or Lead scientist) https://doi.org/10.14768/MISTRALS-HYMEX.1492

Nadia Fourrié et al.

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Short summary
Impact of assimilation of various observation data set on the forecasts is studied in a mesoscale weather model. The ground-based GNSS Zenithal Total Delay data set providing information on humidity has the largest impact on the analyses and the forecasts. Indeed it represents an evenly spread and frequent data set available at each analysis time over the model domain. Moreover, the reprocessing of these data also improves the forecast quality but this impact is not statistically significant.
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