Coupling the High Complexity Land Surface Model ACASA to the Mesoscale Model WRF
In this study, the Weather Research and Forecasting Model (WRF) 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 sche...
| Main Authors: | , , , , |
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| Format: | Technical Report |
| Language: | en_US |
| Published: |
MIT Joint Program on the Science and Policy of Global Change
2014
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| Online Access: | http://hdl.handle.net/1721.1/91462 |
| _version_ | 1811079597358317568 |
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| author | Xu, L. Pyles, R.D. Paw U, K.T. Chen, S.-H. Monier, E. |
| author_facet | Xu, L. Pyles, R.D. Paw U, K.T. Chen, S.-H. Monier, E. |
| author_sort | Xu, L. |
| collection | MIT |
| description | In this study, the Weather Research and Forecasting Model (WRF) 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 are simple and lack the capability to simulate carbon dioxide (for example, the popular NOAH LSM). ACASA is a complex multilayer land surface model with interactive canopy physiology and full surface hydrological processes. It allows microenvironmental variables such as air and surface temperatures, wind speed, humidity, and carbon dioxide concentration to vary vertically. //
Simulations of surface conditions such as air temperature, dew point temperature, and relative humidity from WRF-ACASA and WRF-NOAH are compared with surface observation from over 700 meteorological stations in California. Results show that the increase in complexity in the WRF-ACASA model not only maintains model accuracy, it 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 impacts of various land surface and model components on atmospheric and surface conditions. |
| first_indexed | 2024-09-23T11:17:32Z |
| format | Technical Report |
| id | mit-1721.1/91462 |
| institution | Massachusetts Institute of Technology |
| language | en_US |
| last_indexed | 2024-09-23T11:17:32Z |
| publishDate | 2014 |
| publisher | MIT Joint Program on the Science and Policy of Global Change |
| record_format | dspace |
| spelling | mit-1721.1/914622019-04-11T06:31:54Z Coupling the High Complexity Land Surface Model ACASA to the Mesoscale Model WRF Xu, L. Pyles, R.D. Paw U, K.T. Chen, S.-H. Monier, E. In this study, the Weather Research and Forecasting Model (WRF) 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 are simple and lack the capability to simulate carbon dioxide (for example, the popular NOAH LSM). ACASA is a complex multilayer land surface model with interactive canopy physiology and full surface hydrological processes. It allows microenvironmental variables such as air and surface temperatures, wind speed, humidity, and carbon dioxide concentration to vary vertically. // Simulations of surface conditions such as air temperature, dew point temperature, and relative humidity from WRF-ACASA and WRF-NOAH are compared with surface observation from over 700 meteorological stations in California. Results show that the increase in complexity in the WRF-ACASA model not only maintains model accuracy, it 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 impacts of various land surface and model components on atmospheric and surface conditions. This work is supported in part by the National Science Foundation under Awards No.ATM-0619139 and EF-1137306. The Joint Program on the Science and Policy of Global Change is funded by a number of federal agencies and a consortium of 40 industrial and foundation sponsors. 2014-11-05T15:59:21Z 2014-11-05T15:59:21Z 2014-08 Technical Report http://hdl.handle.net/1721.1/91462 Report 265 en_US MIT Joint Program Report Series;265 application/pdf MIT Joint Program on the Science and Policy of Global Change |
| spellingShingle | Xu, L. Pyles, R.D. Paw U, K.T. Chen, S.-H. Monier, E. Coupling the High Complexity Land Surface Model ACASA to the Mesoscale Model WRF |
| title | Coupling the High Complexity Land Surface Model ACASA to the Mesoscale Model WRF |
| title_full | Coupling the High Complexity Land Surface Model ACASA to the Mesoscale Model WRF |
| title_fullStr | Coupling the High Complexity Land Surface Model ACASA to the Mesoscale Model WRF |
| title_full_unstemmed | Coupling the High Complexity Land Surface Model ACASA to the Mesoscale Model WRF |
| title_short | Coupling the High Complexity Land Surface Model ACASA to the Mesoscale Model WRF |
| title_sort | coupling the high complexity land surface model acasa to the mesoscale model wrf |
| url | http://hdl.handle.net/1721.1/91462 |
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