Improving the estimation of snow depth in the Noah-MP model by combining particle filter and Bayesian model averaging-Reference-Cited by-同舟云学术

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Improving the estimation of snow depth in the Noah-MP model by combining particle filter and Bayesian model averaging

Published:2023-02 Issue: Volume:617 Page:128877
ISSN:0022-1694
Container-title:Journal of Hydrology
language:en
Short-container-title:Journal of Hydrology

Author:

You Yuanhong,Huang Chunlin,Hou Jinliang,Zhang Ying,Wang Zuo,Zhu Gaofeng

Funder

Natural Science Foundation of Anhui Province

National Natural Science Foundation of China

Publisher

Elsevier BV

Subject

Water Science and Technology

Reference69 articles.

1. A tutorial on particle filters for on-line non-linear/non-Gausssian Bayesin tracking;Arulampalam;IEEE Trans. Signal Process.,2002

2. Assessment of GCM performances for the Arabian Peninsula, Brazil, and Ukraine and indications of regional climate change;Barfus;Environ. Earth Sci.,2014

3. Potential impacts of a warming climate on water availability in snow-dominated regions;Barnett;Nature,2005

4. Assimilation of MODIS snow cover and real time snow depth point data in a snow dynamic model;Boni,2010

5. The Rhone-aggregation land surface scheme intercomparison project: An overview;Boone;J. Clim.,2004

Cited by 4 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Assessment of snow simulation using Noah-MP land surface model forced by various precipitation sources in the Central Tianshan Mountains, Central Asia;Atmospheric Research;2024-04

2. Assessing the Sensitivity of Snow Depth Simulations to Land Surface Parameterizations within Noah-MP in Northern Xinjiang, China;Remote Sensing;2024-02-05

3. A review of efficient applications of genetic algorithms to improve particle filtering optimization problems;Measurement;2024-01

4. Climate change impact assessment on groundwater level changes: A study of hybrid model techniques;IET Signal Processing;2023-06

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