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dc.contributor.authorPyles, R. D.
dc.contributor.authorPaw U, K. T.
dc.contributor.authorChen, S. H.
dc.contributor.authorXu, Liyi
dc.contributor.authorMonier, Erwan
dc.date.accessioned2015-01-12T16:14:50Z
dc.date.available2015-01-12T16:14:50Z
dc.date.issued2014-12
dc.date.submitted2014-10
dc.identifier.issn1991-9603
dc.identifier.issn1991-959X
dc.identifier.urihttp://hdl.handle.net/1721.1/92785
dc.description.abstractIn this study, the Weather Research and Forecasting (WRF) model is coupled with the Advanced Canopy–Atmosphere–Soil Algorithm (ACASA), a high-complexity land surface model. Although WRF is a state-of-the-art regional atmospheric model with high spatial and temporal resolutions, the land surface schemes available in WRF, such as the popular NOAH model, are simple and lack the capability of representing the canopy structure. In contrast, ACASA is a complex multilayer land surface model with interactive canopy physiology and high-order turbulence closure that allows for an accurate representation of heat, momentum, water, and carbon dioxide fluxes between the land surface and the atmosphere. It allows for microenvironmental variables such as surface air temperature, wind speed, humidity, and carbon dioxide concentration to vary vertically within and above the canopy. Surface meteorological conditions, including air temperature, dew point temperature, and relative humidity, simulated by WRF-ACASA and WRF-NOAH are compared and evaluated with observations from over 700 meteorological stations in California. Results show that the increase in complexity in the WRF-ACASA model not only maintains model accuracy but also properly accounts for the dominant biological and physical processes describing ecosystem–atmosphere interactions that are scientifically valuable. The different complexities of physical and physiological processes in the WRF-ACASA and WRF-NOAH models also highlight the impact of different land surface models on atmospheric and surface conditions.en_US
dc.description.sponsorshipNational Science Foundation (U.S.) (Award ATM-0619139)en_US
dc.description.sponsorshipNational Science Foundation (U.S.) (Award EF-1137306)en_US
dc.description.sponsorshipNational Science Foundation (U.S.) (Massachusetts Institute of Technology. Subaward 5710003122)en_US
dc.language.isoen_US
dc.publisherCopernicus GmbHen_US
dc.relation.isversionofhttp://dx.doi.org/10.5194/gmd-7-2917-2014en_US
dc.rightsCreative Commons Attributionen_US
dc.rights.urihttp://creativecommons.org/licenses/by/3.0/en_US
dc.sourceCopernicus Publicationsen_US
dc.titleCoupling the high-complexity land surface model ACASA to the mesoscale model WRFen_US
dc.typeArticleen_US
dc.identifier.citationXu, L., R. D. Pyles, K. T. Paw U, S. H. Chen, and E. Monier. “Coupling the High-Complexity Land Surface Model ACASA to the Mesoscale Model WRF.” Geoscientific Model Development 7, no. 6 (2014): 2917–2932.en_US
dc.contributor.departmentMassachusetts Institute of Technology. Center for Global Change Scienceen_US
dc.contributor.departmentMassachusetts Institute of Technology. Joint Program on the Science & Policy of Global Changeen_US
dc.contributor.mitauthorXu, Liyien_US
dc.contributor.mitauthorMonier, Erwanen_US
dc.relation.journalGeoscientific Model Developmenten_US
dc.eprint.versionFinal published versionen_US
dc.type.urihttp://purl.org/eprint/type/JournalArticleen_US
eprint.statushttp://purl.org/eprint/status/PeerRevieweden_US
dspace.orderedauthorsXu, L.; Pyles, R. D.; Paw U, K. T.; Chen, S. H.; Monier, E.en_US
dc.identifier.orcidhttps://orcid.org/0000-0001-5533-6570
mit.licensePUBLISHER_CCen_US


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