Articles | Volume 15, issue 22
https://doi.org/10.5194/gmd-15-8541-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-15-8541-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Development and technical paper
| 
22 Nov 2022
Development and technical paper |
| 22 Nov 2022
| 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
Center for Climate/Environment Change Prediction Research, Ewha Womans University, Seoul, 03760, Republic of Korea
Severe Storm Research Center, Ewha Womans University, Seoul, 03760, Republic of Korea
Hyeon-Ju Gim
Korea Institute of Atmospheric Prediction System (KIAPS), Seoul, 07071, Republic of Korea
Ebony Lee
Center for Climate/Environment Change Prediction Research, Ewha Womans University, Seoul, 03760, Republic of Korea
Severe Storm Research Center, Ewha Womans University, Seoul, 03760, Republic of Korea
Department of Climate and Energy System Engineering, Ewha Womans University, Seoul, 03760, Republic of Korea
Seungyeon Lee
Center for Climate/Environment Change Prediction Research, Ewha Womans University, Seoul, 03760, Republic of Korea
Severe Storm Research Center, Ewha Womans University, Seoul, 03760, Republic of Korea
Department of Climate and Energy System Engineering, Ewha Womans University, Seoul, 03760, Republic of Korea
Won Young Lee
Center for Climate/Environment Change Prediction Research, Ewha Womans University, Seoul, 03760, Republic of Korea
Severe Storm Research Center, Ewha Womans University, Seoul, 03760, Republic of Korea
Yong Hee Lee
High Impact Weather Research Department, National Institute of Meteorological Sciences, Gangneung, 25457, Republic of Korea
Claudio Cassardo
Department of Physics and NatRisk Centre, University of Turin, Turin, 10125, Italy
Center for Climate/Environment Change Prediction Research, Ewha Womans University, Seoul, 03760, Republic of Korea
Severe Storm Research Center, Ewha Womans University, Seoul, 03760, Republic of Korea
Department of Climate and Energy System Engineering, Ewha Womans University, Seoul, 03760, Republic of Korea
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The ensembles in the numerical weather prediction system are under-dispersed near the land surface; therefore, an inflation method is required to increase it. In this study, we perturbed soil temperature and soil moisture to represent the near-surface uncertainty. Perturbations were obtained by the optimization algorithm taking into account diurnal variations in soil states. Consequently, it indirectly inflated the temperature and water vapor mixing ratio in the planetary boundary layer.
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Snow cover or snow albedo plays a vital role in the atmosphere and land surface interaction. Especially, direct observation of snow is difficult and scarce. That's why a reliable Land Surface Model (LSM), including snow physical processes, is significant. In this study, we tried to give meaningful insights for improving the LSM in the future by identifying the main variables or parameters used and examining the different formulas for snow-related processes of the eight LSMs.
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Geosci. Model Dev., 14, 6241–6255, https://doi.org/10.5194/gmd-14-6241-2021, https://doi.org/10.5194/gmd-14-6241-2021, 2021
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One of the biggest uncertainties in numerical weather predictions (NWPs) comes from treating subgrid-scale physical processes. Physical processes, such as cumulus, microphysics, and planetary boundary layer processes, are parameterized in NWP models by empirical and theoretical backgrounds. We developed an interface between a micro-genetic algorithm and the WRF model for a combinatorial optimization of physics for heavy rainfall events in Korea. The system improved precipitation forecasts.
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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.
The land surface model (LSM) contains various uncertain parameters, which are obtained by the...
