Open AccessThe Improvement of Turbulent Heat Flux Parameterization for Use in the Tropical Regions Using Low Wind Speed Excess Resistance Parameter
Author(s) -
Akinnubi R.T.,
Adeniyi M.O.
Publication year - 2019
Publication title -
journal of advances in modeling earth systems
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 3.03
H-Index - 58
ISSN - 1942-2466
DOI - 10.1029/2018ms001466
Subject(s) - turbulence , wind speed , sensible heat , root mean square , heat flux , mean squared error , roughness length , momentum (technical analysis) , aerodynamics , parameterized complexity , meteorology , heat transfer , flux (metallurgy) , mathematics , reynolds number , atmosphere (unit) , atmospheric sciences , environmental science , physics , mechanics , wind profile power law , materials science , statistics , economics , metallurgy , finance , quantum mechanics , combinatorics
Abstract Reliable simulation of turbulent heat fluxes needed for modeling land‐atmosphere interactions remains a challenge over the humid tropical region. This may be connected with the inadequate parameterization of the roughness lengths for momentum ( z 0m ) and heat ( z 0h ) transfer usually expressed in terms of excess resistance factor ( κB −1 ). This paper assesses the performance of existing κB −1 schemes developed for high wind speed conditions over the humid tropical region. Thereafter, a more appropriate κB −1 suitable for low wind speed condition is developed for use in the aerodynamic resistance parameterization. Based on observed surface heat fluxes and profile measurements of wind speed and temperature from Nigeria Micrometeorological Experimental site, new κB −1 parameterization was derived through the application of the Monin‐Obukhov similarity theory and Brutsaert theoretical model for heat transfer. The derivedκB − 1 = 6.66Re * 0.02 − 5.47 , where Re * is the Reynolds number. Turbulent flux parameterization with this new formula provides better estimates of heat fluxes with reference to results from existing κB −1 schemes. The R 2 increased by about 85%, while mean bias error and root‐mean‐square error in the parameterized Q H based on the derived κB −1 reduced by about 63% and 66.7%, respectively. Similarly, the R 2 increased by about 38%, while mean bias error and root‐mean‐square error in the parameterized Q E based on the derived κB −1 reduced by about 47.8% and 52.6%, respectively. The derived κB −1 gave better estimates of Q H than Q E during the daytime. The derived κB −1 scheme corrects a well‐documented, large overestimation of turbulent heat fluxes, and it is therefore recommended for use in regions where low wind speed is prevalent.