Premium
Introducing and evaluating a new building‐height categorization based on the fractal dimension into the coupled WRF /urban model
Author(s) -
Li Yuhuan,
Miao Shiguang,
Chen Fei,
Liu Yonghong
Publication year - 2016
Publication title -
international journal of climatology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.58
H-Index - 166
eISSN - 1097-0088
pISSN - 0899-8418
DOI - 10.1002/joc.4903
Subject(s) - weather research and forecasting model , environmental science , fractal dimension , meteorology , wind speed , categorization , precipitation , fractal , computer science , geography , mathematics , mathematical analysis , artificial intelligence
Model simulations of the boundary‐layer structures and summer rainfall in cities are heavily affected by urban morphology, which are defined by a number of urban canopy parameters ( UCPs ). In the default Weather Research Forecasting ( WRF ) model/urban canopy model ( UCM ) system, UCPs are specified in a lookup table as function of three different urban land‐use types, which cannot totally capture the heterogeneity in building heights ( BHs ). In this article, a method of using a fractal dimension ( FD ) was developed to define new categories of urban BH for characterizing the vertical urban canopy morphology. Combined with the default three urban land‐use categories in WRF / UCM system, the BH categorization improves the description of the urban canopy characteristics. WRF / UCM multi‐day simulations showed that the new method produced lower air temperatures and higher 10‐m wind speeds in densely urbanized areas in Beijing, and performed better in capturing the temperature variations and spatial distributions of the 10‐m wind speed. A rainfall case was also simulated by WRF / UCM using the two methods, and the new BH method yielded a more accurate simulation of precipitation. The difference of 24‐h total precipitation between WRF simulations using the two methods can reach 40 mm, indicating a high sensitivity to urban surface representations. Results showed that the introduction of FD ‐based BH categorization is a simple and yet efficient way to improve WRF / UCM performance in the areas where detailed information on BHs is not available.