Mathematical Modeling of Diurnal Patterns of Turbulent Heat Fluxes using Modified Aerodynamic Resistance Method
Keywords:Surface energy balance, aerodynamics surface layer resistances, radiometric surface temperature, Mean bias error
The development of improved methods for estimating turbulent heat fluxes is important in effective monitoring of the surface energy balance for climate change prediction. However, different Â parameterized models carried out at the local site of Nigerian Micrometeorological site (NIMEX-1) did not consider the surface heat fluxes-aerodynamics resistance relationships, and these validated models cannot be incorporated into the Climate models because some of the input climate variables are not routinely available in some meteorological stations. This study therefore, aims at improving the diurnal patterns of surface heat fluxes estimates using radiometric surface temperature and aerodynamic surface-layer resistances. Hourly data of air temperature (Ta), soil temperature (Tsoil), global radiation (QL), surface temperature (Ts), wind speed (u), QH and QE were obtained from the NIMEX-1 at Ile-Ife (7.55 oN, 4.55 oE). The QH and QE were estimated using Aerodynamic Resistance Approach algorithm which was modified to reduce the large bias errors between the aerodynamic temperature and surface temperature above the ground level. The algorithms were validated and rated using the following error statistics: coefficient of determination (r2), Mean Bias Error (MBE) and Root Mean Square Error (RMSE). The RMSE and MBE for the modeled QE estimated using ARM and MAR reduced from 28.33Wm-2 to 14.33 Wm-2 and 36.93 to 10.74 Wm-2 respectively while for QH ,the RMSE and MBE reduced from 17.29 Wm-2 to 9.49 Wm-2 and 31.39 to 16.93 Wm-2 respectively. The r2 values ranged from 0.68 to 0.73 and 0.95 to 0.98 for QH and QE respectively. The MAR had the highest r2 and least error values.Â Hence, the proposed modified Aerodynamic resistance models are estimated the diurnal and seasonal turbulent heat fluxes accurately for tropical regions.
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