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| Titel |
Coupling the high-complexity land surface model ACASA to the mesoscale model WRF |
| VerfasserIn |
L. Xu, R. D. Pyles, K. T. Paw U, S. H. Chen, E. Monier |
| Medientyp |
Artikel
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| Sprache |
Englisch
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| ISSN |
1991-959X
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| Digitales Dokument |
URL |
| Erschienen |
In: Geoscientific Model Development ; 7, no. 6 ; Nr. 7, no. 6 (2014-12-10), S.2917-2932 |
| Datensatznummer |
250115792
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| Publikation (Nr.) |
 copernicus.org/gmd-7-2917-2014.pdf |
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| Zusammenfassung |
In 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. |
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