Applying micro genetic algorithm to numerical model for luminous intensity distribution of planar prism LED luminaire

<p>The ensemble data assimilation system is beneficial to represent the initial uncertainties and flow-dependent background error covariance (BEC). In particular, the inevitable model uncertainties can be expressed by ensemble spread, that is the standard deviation of ensemble BEC. However, the ensemble spread generally suffers from under-estimated problems. To alleviate this problem, recent studies employed stochastic perturbation schemes to increases the ensemble spreads by adding the random forcing in the model tendencies (i.e., physical or dynamical tendencies) or parameterization schemes (i.e., PBL, convective scheme, etc.). In this study, we focus on the near-surface uncertainties which are affected by the interactions between the land and atmosphere process. The land surface model (LSM) provides various fluxes as the lower boundary condition to the atmosphere, influencing the accuracy of hourly-to-seasonal scale weather forecasting, but the surface uncertainties were not much addressed yet. In this study, we developed the stochastically perturbed parameterization (SPP) scheme for the Noah LSM. The Weather Research and Forecasting (WRF) ensemble system is used for regional weather forecasting over East Asia, especially over the Korean Peninsula. As a testbed experiment with the newly-developed Noah LSM-SPP system, we first perturbed the soil temperature &#8212; a crucial variable for the near-surface forecasts by affecting sensible heat fluxes, land surface skin temperature and surface air temperature, and hence lower-tropospheric temperature. Here, the random forcing used in perturbation is made by the tuning parameters for amplitude, length scale, and time scales: they are commonly determined empirically by trial and error. In order to find optimal tuning parameter values, we applied a global optimization algorithm &#8212; the micro-genetic algorithm (micro-GA) &#8212; to achieve the smallest root-mean-squared errors. Our results indicate that optimization of the random forcing parameters contributes to an increase in the ensemble spread and a decrease in the ensemble mean errors in the near-surface and lower-troposphere uncertainties. Further experiments will be conducted by including soil moisture in the testbed.</p>

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Application of a micro-genetic algorithm for gait development on a bio-inspired robotic pectoral fin

2013 IEEE/RSJ International Conference on Intelligent Robots and Systems ◽

10.1109/iros.2013.6696897 ◽

2013 ◽

Cited By ~ 3

Author(s):

Jeff C. Kahn ◽

James L. Tangorra

Keyword(s):

Genetic Algorithm ◽

Pectoral Fin ◽

Micro Genetic Algorithm

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Combinational Optimization of the WRF Physical Parameterization Schemes to Improve Numerical Sea Breeze Prediction Using Micro-Genetic Algorithm

Applied Sciences ◽

10.3390/app112311221 ◽

2021 ◽

Vol 11 (23) ◽

pp. 11221

Author(s):

Ji Won Yoon ◽

Sujeong Lim ◽

Seon Ki Park

Keyword(s):

Genetic Algorithm ◽

Wind Speed ◽

Land Surface ◽

Wrf Model ◽

Longwave Radiation ◽

Sea Breeze ◽

Meteorological Variables ◽

Parameterization Schemes ◽

Micro Genetic Algorithm ◽

Physical Parameterization

This study aims to improve the performance of the Weather Research and Forecasting (WRF) model in the sea breeze circulation using the micro-Genetic Algorithm (micro-GA). We found the optimal combination of four physical parameterization schemes related to the sea breeze system, including planetary boundary layer (PBL), land surface, shortwave radiation, and longwave radiation, in the WRF model coupled with the micro-GA (WRF-μGA system). The optimization was performed with respect to surface meteorological variables (2 m temperature, 2 m relative humidity, 10 m wind speed and direction) and a vertical wind profile (wind speed and direction), simultaneously for three sea breeze cases over the northeastern coast of South Korea. The optimized set of parameterization schemes out of the WRF-μGA system includes the Mellor–Yamada–Nakanishi–Niino level-2.5 (MYNN2) for PBL, the Noah land surface model with multiple parameterization options (Noah-MP) for land surface, and the Rapid Radiative Transfer Model for GCMs (RRTMG) for both shortwave and longwave radiation. The optimized set compared with the various other sets of parameterization schemes for the sea breeze circulations showed up to 29 % for the improvement ratio in terms of the normalized RMSE considering all meteorological variables.

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