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  • Author or Editor: Zhuo Wang x
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Zhuo Wang
and
Xubin Zeng

Abstract

Snow albedo plays an important role in land models for weather, climate, and hydrometeorological studies, but its treatment in various land models still contains significant deficiencies. Complementary to previous studies that evaluated the snow albedo as part of an overall land model study, the snow albedo formulations as used in four major weather forecasting and climate models [European Centre for Medium-Range Weather Forecasts (ECMWF), National Centers for Environmental Prediction (NCEP) “Noah” land model, National Center for Atmospheric Research (NCAR) Community Land Model (CLM3), and NCEP global model] were directly evaluated here using multiyear Boreal Ecosystem–Atmosphere Study (BOREAS) in situ data over grass and forest sites. First, four idealized cases over grass and forest sites were designed to understand better the different albedo formulations in these models. Then the BOREAS data were used to evaluate snow albedo and relevant formulations and to identify deficiencies of each model. Based on these analyses, suggestions that involve only minor changes in parameters or formulations were made to significantly reduce these deficiencies of each model. For the ECMWF land model, using the square root of snow water equivalent (SWE), rather than SWE itself, in the computation of snow fraction would significantly reduce the underestimation of albedo over grass. For the NCEP Noah land model, reducing (increasing) the critical SWE for full snow cover over short (tall) vegetation would reduce the underestimate (overestimate) of snow albedo over the grass (forest) site. For the NCAR CLM3, revising the coefficient used in the ground snow-fraction computation would substantially reduce the albedo underestimation over grass. For the albedo formulations in the NCEP global model, replacing the globally constant fresh snow albedo by the vegetation-type-dependent Moderate-Resolution Imaging Spectroradiometer (MODIS) maximum snow albedo would significantly improve the overestimation of model albedo over forest.

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Fanglin Yang
,
Kenneth Mitchell
,
Yu-Tai Hou
,
Yongjiu Dai
,
Xubin Zeng
,
Zhuo Wang
, and
Xin-Zhong Liang

Abstract

This study examines the dependence of surface albedo on solar zenith angle (SZA) over snow-free land surfaces using the intensive observations of surface shortwave fluxes made by the U.S. Department of Energy Atmospheric Radiation Measurement (ARM) Program and the National Oceanic and Atmospheric Administration Surface Radiation Budget Network (SURFRAD) in 1997–2005. Results are used to evaluate the National Centers for Environmental Prediction (NCEP) Global Forecast Systems (GFS) parameterization and several new parameterizations derived from the Moderate Resolution Imaging Spectroradiometer (MODIS) products. The influence of clouds on surface albedo and the albedo difference between morning and afternoon observations are also investigated. A new approach is taken to partition the observed upward flux so that the direct-beam and diffuse albedos can be separately computed. The study focused first on the ARM Southern Great Plains Central Facility site. It is found that the diffuse albedo prescribed in the NCEP GFS matched closely with the observations. The direct-beam albedo parameterized in the GFS is largely underestimated at all SZAs. The parameterizations derived from the MODIS product underestimated the direct-beam albedo at large SZAs and slightly overestimated it at small SZAs. Similar results are obtained from the analyses of observations at other stations. It is also found that the morning and afternoon dependencies of direct-beam albedo on SZA differ among the stations. Attempts are made to improve numerical model algorithms that parameterize the direct-beam albedo as a product of the direct-beam albedo at SZA = 60° (or the diffuse albedo), which varies with surface type or geographical location and/or season, and a function that depends only on SZA. A method is presented for computing the direct-beam albedos over these snow-free land points without referring to a particular land-cover classification scheme, which often differs from model to model.

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