Preprints
https://doi.org/10.5194/egusphere-2024-668
https://doi.org/10.5194/egusphere-2024-668
02 Apr 2024
 | 02 Apr 2024
Status: this preprint is open for discussion and under review for Atmospheric Measurement Techniques (AMT).

Radar based high resolution ensemble precipitation analyses over the French Alps

Matthieu Vernay, Matthieu Lafaysse, and Clotilde Augros

Abstract. Reliable estimation of precipitation fields at high resolution is a key issue for snow cover modelling in mountainous areas, where the density of precipitation networks is far too low to capture their complex variability with topography. Adequate quantification of the remaining uncertainty in precipitation estimates is also necessary for further assimilation of complementary snow observations in snow models. Radar observations provide spatialised estimates of precipitation with high spatial and temporal resolution, and are often combined with rain gauge observations to improve the accuracy of the estimate. However, radar measurements suffer from significant shortcomings in mountainous areas (in particular, unrealistic spatial patterns due to ground clutter). Precipitation fields simulated by high-resolution numerical weather prediction (NWP) models provide an alternative estimate, but suffer from systematic biases and positioning errors. Even though these uncertainties can be partially described by ensemble NWP systems and systematic errors can be reduced by statistical post-processing, NWP precipitation estimates are still not reliable enough for the requirements of high resolution snow cover modelling.

In this study, better precipitation estimates are obtained through a specific analysis based on a combination of all these available products. First, a pre-processing step is proposed to mitigate the main deficiencies of radar and gauges precipitation estimation products, focusing on reducing unrealistic spatial patterns. This method also provides a spatialised estimate of the associated error in mountainous areas, based on a climatological analysis of both radar and NWP-estimated precipitation. Three ensemble daily precipitation analysis methods are then proposed, first using only the modified precipitation estimates and associated errors, then combining them with ensemble NWP simulations based on the Particle Filter and Ensemble Kalman Filter data assimilation algorithms. The performance of the different precipitation analysis methods is evaluated at a local scale using independent ski resort precipitation observations. The evaluation of the pre-processing step shows its ability to remove the main spatial artefacts coming from the radar measurements and to improve the precipitation estimates at the local scale. The local scale evaluations of the ensemble analyses do not demonstrate an additional benefit of ensemble NWP forecasts, but their contrasted spatial patterns are challenging to evaluate with the available data.

Matthieu Vernay, Matthieu Lafaysse, and Clotilde Augros

Status: open (until 19 May 2024)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
Matthieu Vernay, Matthieu Lafaysse, and Clotilde Augros
Matthieu Vernay, Matthieu Lafaysse, and Clotilde Augros

Viewed

Total article views: 87 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
64 17 6 87 4 4
  • HTML: 64
  • PDF: 17
  • XML: 6
  • Total: 87
  • BibTeX: 4
  • EndNote: 4
Views and downloads (calculated since 02 Apr 2024)
Cumulative views and downloads (calculated since 02 Apr 2024)

Viewed (geographical distribution)

Total article views: 80 (including HTML, PDF, and XML) Thereof 80 with geography defined and 0 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 
Latest update: 13 Apr 2024
Download
Short summary
This paper provides a comprehensive evaluation of the quality of radar-based precipitation estimation in mountainous areas and presents a method to mitigate the main shortcomings identified. It then compares three different ensemble analysis methods that combine radar-based precipitation estimates with forecasts from an ensemble numerical weather prediction model.