EGU21-7848, updated on 04 Mar 2021
https://doi.org/10.5194/egusphere-egu21-7848
EGU General Assembly 2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.

Optimization of snow density parameter of Noah Land Surface Model using micro-genetic algorithm for estimating snow depth

Ebony Lee1 and Seon Ki Park1,2,3
Ebony Lee and Seon Ki Park
  • 1Department of Climate and Energy Systems Engineering, Ewha Womans University, Seoul, Republic of Korea
  • 2Department of Environmental Science and Engineering, Ewha Womans University, Seoul, Republic of Korea
  • 3Center for Climate/Environment Change Prediction Research, Ewha Womans University, Seoul, Republic of Korea (spark@ewha.ac.kr)

The Noah Land Surface Model (Noah LSM) estimates snow depth using snow water equivalent and snow density. The snow density is determined by snow compaction, snowmelt water storing, and density of fresh snowfall. The Noah LSM usually underestimates snow depth compared to the ground observations in Korea, which occurs from the beginning of snowfall. We performed an optimal estimation of parameters related to the density of fresh snowfall, using micro-genetic algorithm (μ-GA) that uses the evolution process concept through natural selection and mutation mechanism. Ground observations from 36 sites of the Korea Meteorological Administration, for the recent 10 years (May 2009 – April 2019), are used for offline forcing of the Noah LSM and evaluating the fitness function in μ-GA. Optimized parameters reduced the density of fresh snowfall, and improved the simulated snow depth. The root-mean-square error of snow depth decreased from 8.1 cm to 7.1 cm.

How to cite: Lee, E. and Park, S. K.: Optimization of snow density parameter of Noah Land Surface Model using micro-genetic algorithm for estimating snow depth, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-7848, https://doi.org/10.5194/egusphere-egu21-7848, 2021.

Corresponding presentation materials formerly uploaded have been withdrawn.