• Journal of the Korean Association of Geographic Information Studies
• /
• v.1 no.1
• /
• pp.99-108
• /
• 1998

In order to represent the surface characteristics in local meteorological model, soil type, vegetation index, surface roughness length, surface albedo and leaf area index should be prescribed on the surface process parameterization. In this study, the $1^{\circ}/1^{\circ}leaf$ area index, surface roughness length, and snow free surface albedo and fine mesh NDVI with seasonal variation derived from the satellite observation were applied to the land surface process parameterization. From comparison between with and without satellite data in the interactions between biosphere and atmosphere, land and atmosphere, the sensitivity of the simulated heat, energy and water vapor fluxes, ground temperature, wind, canopy water content, specific humidity, and precipitation fields were investigated.

• Journal of Advanced Navigation Technology
• /
• v.23 no.6
• /
• pp.487-494
• /
• 2019

Recently, interest in and use of drones is increasing. In this study, to provide accurate wind prediction at ultra low altitudes of 150 meters or below, the sensitivity of the physical process parameterization and initial conditions was assessed to select the optimal physical process and initial conditions. For this purpose, GFS and LDAPS data were used as initial and boundary conditions, and 7 experiments were constructed using a combination of PBL schemes such as YSU, RUC, ACM2, and LSM such as Noah, RUC, and Pleim. The experiment conducted for 1 month in April 2018. As a result, the RUC-YSU physical process combination using the GFS initial data showed the best performance. This study is meaningful in establishing an optimal modeling method for ultra low altitude wind prediction through experiments using different initial conditions and combination of physical processes.