Articles | Volume 15, issue 22
Geosci. Model Dev., 15, 8541–8559, 2022
https://doi.org/10.5194/gmd-15-8541-2022
Geosci. Model Dev., 15, 8541–8559, 2022
https://doi.org/10.5194/gmd-15-8541-2022
Development and technical paper
22 Nov 2022
Development and technical paper | 22 Nov 2022

Optimization of snow-related parameters in the Noah land surface model (v3.4.1) using a micro-genetic algorithm (v1.7a)

Sujeong Lim et al.

Data sets

MODIS/Terra Snow Cover Daily L3 Global 500m SIN Grid D. K. Hall and G. A. Riggs https://doi.org/10.5067/MODIS/MOD10A1.061

Automated Synoptic Observing System (ASOS) Korea Meteorological Administration https://data.kma.go.kr/data/grnd/selectAsosRltmList.do?pgmNo=36

Model code and software

Code and Data: Optimization of Snow-Related Parameters in Noah Land Surface Model (v3.4.1) Using Micro-Genetic Algorithm (v1.7a) Sujeong Lim, Hyeon-Ju Gim, Ebony Lee, Seungyeon Lee, Won Young Lee, Yong Hee Lee, Claudio Cassardo, and Seon Ki Park https://doi.org/10.5281/zenodo.6873384

Unified Noah LSM National Center for Atmosphere Research https://ral.ucar.edu/solutions/products/unified-wrf-noah-lsm

Fortran Genetic Algorithm Front-End Driver Code D. L. Carroll https://cuaerospace.com/products-services/genetic-algorithm/ga-drive-free-downloadT

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Short summary
The land surface model (LSM) contains various uncertain parameters, which are obtained by the empirical relations reflecting the specific local region and can be a source of uncertainty. To seek the optimal parameter values in the snow-related processes of the Noah LSM over South Korea, we have implemented an optimization algorithm, a micro-genetic algorithm using the observations. As a result, the optimized snow parameters improve snowfall prediction.