85th AMS Annual Meeting

Thursday, 13 January 2005: 11:15 AM
Development of Land Surface Albedo Parameterization Based on MODIS data (INVITED)
Xin-Zhong Liang, University of Illinois at Urbana-Champaign, Champaign, IL; and M. Xu, W. Gao, K. E. Kunkel, J. R. Slusser, Y. Dai, Q. Min, P. R. Houser, M. Rodell, C. B. Schaaf, and F. Gao
The existing surface albedo scheme in the state-of-the-art Common Land Model (CLM) produces substantial biases from those derived from the MODerate resolution Imaging Spectroradiometer (MODIS) satellite measurements. Here a new parameterization of snow-free land surface albedo is developed using the MODIS products of broadband black-sky and white-sky reflectance and vegetation information as well as the North American and Global Land Data Assimilation System (LDAS) outputs of soil moisture during 2000-2003. This is a dynamic-statistic model, where the dynamic component represents the predictable albedo dependences on solar zenith angle, surface soil moisture, fractional vegetation cover, and leaf plus stem area index, while the statistic part depicts the correction for static effects specific of local surface characteristics. All parameters that define the dynamic and statistic contributions are determined by solving nonlinear constrained optimization problems of a physically-based conceptual model for the minimization of the bulk variances between simulations and observations. They all depend on direct beam or diffuse radiation and visible or near-infrared band. The dynamic parameters are also functions of land cover category, while the statistic factors are specific of geographic location. Comparisons showed that the new parameterization realistically represents surface albedo variations, including the mean, shape and distribution, around each dependent parameter. For composites of all temporal and spatial samples of a same land cover category over North America, correlation coefficients between the new parameterization with the MODIS data range from 0.6 to 0.9, while relative errors vary within 5-20%. This is a substantial improvement over the existing CLM albedo scheme, which has correlation coefficients from 0.5 to 0.5 and relative errors of 20-100%.

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