A genetic particle filter scheme for univariate snow cover assimilation into Noah-MP model across snow climates

You, Yuanhong; Huang, Chunlin; Wang, Zuo; Hou, Jinliang; Zhang, Ying; Xu, Peipei

doi:https://doi.org/10.5194/hess-27-2919-2023

Articles | Volume 27, issue 15

https://doi.org/10.5194/hess-27-2919-2023

© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.

Special issue:

Experiments in Hydrology and Hydraulics

https://doi.org/10.5194/hess-27-2919-2023

© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.

Articles | Volume 27, issue 15

Research article

|

09 Aug 2023

Research article |

| 09 Aug 2023

A genetic particle filter scheme for univariate snow cover assimilation into Noah-MP model across snow climates

Yuanhong You, Chunlin Huang, Zuo Wang, Jinliang Hou, Ying Zhang, and Peipei Xu

Data sets

Meteorological Station Observation Dataset The China Meteorological Administration http://data.cma.cn/data/cdcindex/cid/f0fb4b55508804ca.html

China meteorological forcing dataset (1979-2018) TPDC https://data.tpdc.ac.cn/zh-hans/data/8028b944-daaa-4511-8769-965612652c49

Model code and software

High-Resolution Land Data Assimilation System (HRLDAS) NCAR https://ral.ucar.edu/model/high-resolution-land-data-assimilation-system-hrldas

Short summary

This study aims to investigate the performance of a genetic particle filter which was used as a 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 a particle filter algorithm. The system has revealed a low sensitivity to the particle number in point-scale application of the ground snow depth measurement.