Journal
METEOROLOGY AND ATMOSPHERIC PHYSICS
Volume 131, Issue 5, Pages 1235-1248Publisher
SPRINGER WIEN
DOI: 10.1007/s00703-018-0634-1
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Funding
- National Key Projects of Ministry of Science and Technology of China [2017YFA0604002, 2016YFC0203304, 2015DFA20870]
- National Natural Science Foundation of China [41275022]
- Startup Foundation for Introducing Talent of NUIST [2017R088]
- National Center for Atmospheric Research/Water System Program
- U.S. Department of Agriculture/National Institute of Food and Agriculture [20156700323460]
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Mesoscale models with urban canopy models (UCM) have been increasingly used to study urban boundary layer processes. Using the data from a high-resolution Doppler lidar, automatic weather stations (AWS), and a flux tower located in the urban site, we assessed the performance of the urbanized Weather Research and Forecasting (WRF) model through three urban canopy models (the single-layer UCM, and the multi-layer BEP and BEM models) and four planetary boundary layer (PBL) schemes (the non-local first-order YSU, SH and ACM2 schemes, as well as the local TKE-based BouLac scheme) for one cloudy and one clear sky days. Results show that the WRF-Urban generally overestimates the sensible heat flux and underestimates the latent heat flux. The simulated 2-m temperature and 10-m wind speed are more sensitive to UCMs than to PBL schemes. Using the BouLac PBL scheme and the multi-layer BEP generates the best agreement with AWS observations. Simulations with the multi-layer BEM produce the highest mixing-layer heights. The convective boundary layer (CBL) from the single-layer UCM experiment develops at the slowest pace when compared with other two multi-layer UCMs. When the single-layer UCM is used, simulations with the non-local mixing YSU, SH and ACM2 schemes perform better than the TKE-based scheme (BouLac) for representing the CBL structure. Additionally, the scale-aware SH scheme considering the effect of grid resolution on the vertical dimension, simulates the potential temperature profiles that are closest to observations.
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