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

Sujeong Lim, Hyeon Ju Gim, Ebony Lee, Seungyeon Lee, Won Young Lee, Yong Hee Lee, Claudio Cassardo, Seon Ki Park

Research output: Contribution to journalArticlepeer-review

3 Scopus citations


Snowfall prediction is important in winter and early spring because snowy conditions generate enormous economic damages. However, there is a lack of previous studies dealing with snow prediction, especially using land surface models (LSMs). Numerical weather prediction models directly interpret the snowfall events, whereas LSMs evaluate the snow cover, snow albedo, and snow depth through interaction with atmospheric conditions. Most LSMs include parameters based on empirical relations, resulting in uncertainties in model solutions. When the initially developed empirical parameters are local or inadequate, we need to optimize the parameter sets for a certain region. In this study, we seek the optimal parameter values in the snow-related processes-snow cover, snow albedo, and snow depth-of the Noah LSM, for South Korea, using the micro-genetic algorithm and the in situ surface observations and remotely sensed satellite data. Snow data from observation stations representing five land cover types-deciduous broadleaf forest, mixed forest, woody savanna, cropland, and urban and built-up lands-are used to optimize five snow-related parameters that calculate the fractional snow cover, maximum snow albedo of fresh snow, and fresh snow density associated with the snow depth. Another parameter, reflecting the dependence of fractional snow cover on the land cover types, is also optimized. Optimization of these six snow-related parameters has led to improvement in the root mean squared errors by 17.0;%, 6.2;%, and 3.3;% in snow depth, snow albedo, and fractional snow cover, respectively. In terms of the mean bias, the underestimation problems of snow depth and overestimation problems of snow albedo have been alleviated through optimization of parameters calculating the fresh snow by about 44.2;% and 31.0;%, respectively.

Original languageEnglish
Pages (from-to)8541-8559
Number of pages19
JournalGeoscientific Model Development
Issue number22
StatePublished - 22 Nov 2022

Bibliographical note

Funding Information:
This work is supported by the Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (grant no. 2018R1A6A1A08025520) and Development of Numerical Weather Prediction and Data Application Techniques (grant no. NTIS-1365003222) funded by the Korea Meteorological Administration. It is partly supported by an NRF grant funded by the Korean government (MSIT) (grant no. NRF-2021R1A2C1095535).

Publisher Copyright:
© 2022 Sujeong Lim et al.


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