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

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Cited articles

Abbasnezhadi, K., Rousseau, A. N., Foulon, E., and Savary, S.: Verification of regional deterministic precipitation analysis products using snow data assimilation for application in meteorological network assessment in sparsely gauged Nordic basins, J. Hydrometeorol., 22, 859–876,, 2021. 
Abbaszadeh, P., Moradkhani, H., and Yan, H. X.: Enhancing hydrologic data assimilation by evolutionary particle filter and Markov Chain Monte Carlo, Adv. Water Resour., 111, 192–204,, 2018. 
Ahmadi, M., Mojallali, H., and Izadi-Zamanabadi, R.: State estimation of nonlinear stochastic systems using a novel meta-heuristic particle filter, Swarm Evol. Comput., 4, 44–53,, 2012. 
Andreadis, K. M. and Lettenmaier, D. P.: Assimilating remotely sensed snow observations into a macroscale hydrology model, Adv. Water Resour., 29, 872–886,, 2006. 
Barnett, T. P., Adam, J. C., and Lettenmaier, D. P.: Potential impacts of a warming climate on water availability in snow-dominated regions, Nature, 438, 303–309,, 2005. 
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.