Using MODIS BRDF/Albedo Data to Evaluate and Improve Land Surface Albedo in Weather and Climate Models

Persistent Link:
http://hdl.handle.net/10150/195109
Title:
Using MODIS BRDF/Albedo Data to Evaluate and Improve Land Surface Albedo in Weather and Climate Models
Author:
Wang, Zhuo
Issue Date:
2005
Publisher:
The University of Arizona.
Rights:
Copyright © is held by the author. Digital access to this material is made possible by the University Libraries, University of Arizona. Further transmission, reproduction or presentation (such as public display or performance) of protected items is prohibited except with permission of the author.
Abstract:
Land surface albedo plays a key role in the surface-atmosphere internaction, because it greatly influences the shortwave radiation absorbed by the surface. Surface albedo depends on soil characteristics and vegetation types. Error in the specification of albedos of soil and vegetation may cause biases in the computation of ground temperature and surface fluxes, therefore accurate albedo estimates are essential for an accurate simulation of the Earth's climate. The study demonstrates the importance of MODIS data in assessing and improving albedo parameterization in weather forecast and climate models as well as the remote sensing retrieval of surface solar fluxes through a series of three papers. First, the NCAR Community Climate System Model (CCSM2) albedo is evaluated using the MODIS BRDF and albedo data. The model and MODIS albedo differences are related to the deficiences in the model simulation of snow cover and soil moisture and in the model's specification of leaf and stem area indexes. They are also partially caused by the deficiency of the two-stream method. Second, motivated by these analyses, a new formulation for surface albedo is developed. Over desert, most land models assume that the bare soil albedo is a function of soil color and soil moisture but independent of solar zenith angle (SZA). However, analysis of MODIS BRDF/albedo data and in situ data indicates that bare soil albedo does vary with SZA. Furthermore this SZA dependence is found to affect the surface energy fluxes and temperature in the offline land surface model sensitivity tests. Finally, the MODIS BRDF algorithm is reformulated to derive a new two-parameter scheme for the computation of land surface albedo and its SZA dependence for use in weather and climate models as well as the remote sensing retrieval of surface solar fluxes. In this formulation, the season- and pixel-dependent black-sky albedo at 60 deg SZA can be directly prescribed using the MODIS BRDF data while the two parameters are taken as a function of vegetation type only. Comparison of this formulation with those used in weather, climate, and data assimilation models (at NCAR, NCEP, and NASA) as well as those used in remote sensing groups (University of Maryland, ISCCP-FD, and CERES/TRMM) reveals the deficiencies in the land surface albedo treatment in these models and remote sensing retrieval algorithm along with suggestions for improvement.
Type:
text; Electronic Dissertation
Keywords:
land/atmosphere internactions; radiative processes; remote sensing; albedo
Degree Name:
PhD
Degree Level:
doctoral
Degree Program:
Atmospheric Sciences; Graduate College
Degree Grantor:
University of Arizona
Advisor:
Zeng, Xubin
Committee Chair:
Zeng, Xubin

Full metadata record

DC FieldValue Language
dc.language.isoENen_US
dc.titleUsing MODIS BRDF/Albedo Data to Evaluate and Improve Land Surface Albedo in Weather and Climate Modelsen_US
dc.creatorWang, Zhuoen_US
dc.contributor.authorWang, Zhuoen_US
dc.date.issued2005en_US
dc.publisherThe University of Arizona.en_US
dc.rightsCopyright © is held by the author. Digital access to this material is made possible by the University Libraries, University of Arizona. Further transmission, reproduction or presentation (such as public display or performance) of protected items is prohibited except with permission of the author.en_US
dc.description.abstractLand surface albedo plays a key role in the surface-atmosphere internaction, because it greatly influences the shortwave radiation absorbed by the surface. Surface albedo depends on soil characteristics and vegetation types. Error in the specification of albedos of soil and vegetation may cause biases in the computation of ground temperature and surface fluxes, therefore accurate albedo estimates are essential for an accurate simulation of the Earth's climate. The study demonstrates the importance of MODIS data in assessing and improving albedo parameterization in weather forecast and climate models as well as the remote sensing retrieval of surface solar fluxes through a series of three papers. First, the NCAR Community Climate System Model (CCSM2) albedo is evaluated using the MODIS BRDF and albedo data. The model and MODIS albedo differences are related to the deficiences in the model simulation of snow cover and soil moisture and in the model's specification of leaf and stem area indexes. They are also partially caused by the deficiency of the two-stream method. Second, motivated by these analyses, a new formulation for surface albedo is developed. Over desert, most land models assume that the bare soil albedo is a function of soil color and soil moisture but independent of solar zenith angle (SZA). However, analysis of MODIS BRDF/albedo data and in situ data indicates that bare soil albedo does vary with SZA. Furthermore this SZA dependence is found to affect the surface energy fluxes and temperature in the offline land surface model sensitivity tests. Finally, the MODIS BRDF algorithm is reformulated to derive a new two-parameter scheme for the computation of land surface albedo and its SZA dependence for use in weather and climate models as well as the remote sensing retrieval of surface solar fluxes. In this formulation, the season- and pixel-dependent black-sky albedo at 60 deg SZA can be directly prescribed using the MODIS BRDF data while the two parameters are taken as a function of vegetation type only. Comparison of this formulation with those used in weather, climate, and data assimilation models (at NCAR, NCEP, and NASA) as well as those used in remote sensing groups (University of Maryland, ISCCP-FD, and CERES/TRMM) reveals the deficiencies in the land surface albedo treatment in these models and remote sensing retrieval algorithm along with suggestions for improvement.en_US
dc.typetexten_US
dc.typeElectronic Dissertationen_US
dc.subjectland/atmosphere internactionsen_US
dc.subjectradiative processesen_US
dc.subjectremote sensingen_US
dc.subjectalbedoen_US
thesis.degree.namePhDen_US
thesis.degree.leveldoctoralen_US
thesis.degree.disciplineAtmospheric Sciencesen_US
thesis.degree.disciplineGraduate Collegeen_US
thesis.degree.grantorUniversity of Arizonaen_US
dc.contributor.advisorZeng, Xubinen_US
dc.contributor.chairZeng, Xubinen_US
dc.contributor.committeememberMullen, Stevenen_US
dc.contributor.committeememberKursinski, Roberten_US
dc.contributor.committeememberSchowengerdt, Roberten_US
dc.identifier.proquest1318en_US
dc.identifier.oclc71794954en_US
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