Articles | Volume 27, issue 15
https://doi.org/10.5194/hess-27-2919-2023
https://doi.org/10.5194/hess-27-2919-2023
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

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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, https://doi.org/10.1016/j.advwatres.2017.11.011, 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, https://doi.org/10.1016/j.swevo.2011.11.004, 2012. 
Andreadis, K. M. and Lettenmaier, D. P.: Assimilating remotely sensed snow observations into a macroscale hydrology model, Adv. Water Resour., 29, 872–886, https://doi.org/10.1016/j.advwatres.2005.08.004, 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, https://doi.org/10.1038/nature04141, 2005. 
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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.
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