Articles | Volume 15, issue 22
Geosci. Model Dev., 15, 8541–8559, 2022
Geosci. Model Dev., 15, 8541–8559, 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

Automated Synoptic Observing System (ASOS) Korea Meteorological Administration

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

Unified Noah LSM National Center for Atmosphere Research

Fortran Genetic Algorithm Front-End Driver Code D. L. Carroll

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.