Preprints
https://doi.org/10.5194/hess-2022-350
https://doi.org/10.5194/hess-2022-350
 
17 Oct 2022
17 Oct 2022
Status: this preprint is currently under review for the journal HESS.

Investigating the performance of Genetic Particle Filter in snow data assimilation across snow climates

Yuanhong You1, Chunlin Huang2, Jinliang Hou2, and Ying Zhang2 Yuanhong You et al.
  • 1College of Geography and Tourism, Anhui Normal University, Wuhu, 241002, China
  • 2Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Lanzhou, 730000, China

Abstract. With the aim of reducing the uncertainty of simulations, data assimilation methodology is increasingly being applied in operational purposes. This study aims to investigate the performance of genetic particle filter which used as snow data assimilation scheme, designed to assimilate ground-based snow depth measurements across different snow climates. We employed the default parameterization scheme combination within Noah-MP model as model operator in the snow data assimilation system. And the feasibility of genetic particle filter used as snow data assimilation scheme was investigated at different sites, at the same time, the impact of measurement frequency, particle number on the filter updating of the snowpack state were also evaluated. The results demonstrated that the genetic particle filter can be used as snow data assimilation scheme and obtain satisfactory assimilation results across different snow climates. We found the particle number is not the crucial factor to impact the filter performance and one hundred particles can sufficient to represent the high dimensionality of the point-scale system. The frequency of measurements can significantly affect the performance of filter updating and a dense ground-based snow observational data always can dominate the accuracy of assimilation results. Finally, we concluded that the genetic particle filter is a suitable candidate approach to snow data assimilation and appropriate for different snow climates.

Yuanhong You et al.

Status: open (until 07 Jan 2023)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on hess-2022-350', Anonymous Referee #1, 12 Nov 2022 reply
  • RC2: 'Comment on hess-2022-350', Anonymous Referee #2, 03 Dec 2022 reply

Yuanhong You et al.

Yuanhong You et al.

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Short summary
This study aims to investigate the performance of genetic particle filter which used as snow data assimilation scheme across different snow climates. The results demonstrated that the genetic algorithm can effectively solve the problem of particle degeneration and impoverishment in PF algorithm. The system has revealed a low sensitivity to the particle number in point-scale application of the ground SD measurement.