Document Type : Full length article
Physical Geography Department, Faculty of Geography, University of Tehran
Physical geography department, Geography faculty, Tehran university, Tehran, Iran
Physical Geography department, Faculty of geography, University of Tehran
The wind has always been considered an energy source from two perspectives: pattern and behavior in urban contexts and potential in suburban environments. There are usually two major strategies for this purpose: one based on observational data and the other providing simulation data with the creation of climate models at various numerical scales (Han et al., 2014: 17). Numerical models are used in most studies to evaluate regional winds nowadays (Haman et al., 2010: 954; Shimada et al., 2011: 21). Simulated weather research and forecasting (WRF) has been used to conduct studies on this topic (Liu et al., 582: 2018; Salvaso et al., 276: 2018; Matar et al., 22: 2016; Charabi et al., 1: 2019; Tokhtenhagen et al., 119: 2020). The sensitivity and performance of the WRF model to initial and boundary conditions, as well as its impact on wind simulation, are investigated in this study. A planetary boundary layer scheme is also chosen to simulate the wind field in the city of Tehran.
Materials and methods
The Meteorological Organization provided observational data on wind direction and speed for Mehrabad, Chitgar, Geophysical, and North Tehran (Shemiran) synoptic stations from 2018 on a three-hour time scale (Table 2). Data analysis time series from two databases, the National Environmental Forecasting Center (NCEP-FNL) and the European Center for Medium-Term Weather Forecasting (ECMWF-) ERA5), were used as the initial and boundary conditions to achieve the frequency and distribution of wind direction and velocity for January, May, July, and October. The WRF model, version 4.1.2, was used to simulate the components of wind speed and direction using boundary condition data in this investigation. The RRTM longwave radiation model, the Goddard shortwave radiation design, the Noah surface model, the WSM6 microphysical schema, the two-dimensional Cumulus Betts-Miller-Janjic schema, and the three-dimensional Grell-Freitas schema were all employed in the study. The MRF Medium-Range Prediction Model, the Younesi University YSU Scheme, the MYJ Scheme, the second ACM2 Asymmetric Convection Scheme, the QNSE Normal Gaussian Scale, and the second and third MYNN Turbulence Scale are all used to test the performance sensitivity of the planetary boundary layer schemas.
Result and discussion
By checking the characteristics of the observation stations according to table 9, all the selected stations have an average height difference of at least 110 meters, and the difference between the lowest (Mehrabad) and the highest (Shimiran) station is 360 meters. According to the results from the selected stations, this feature can be effective in the accuracy of the simulations by the weather prediction research model. It can be stated that the model cannot correctly simulate the topography due to the low horizontal resolution in the inner domain (7 km) and static data (such as DEM and land cover (by default, these data in the model have a horizontal resolution of approximately 1 km)) to do Therefore, it is not possible to establish a meaningful relationship between the height difference of the stations and the output of the model. Still, the lack of proper introduction of the elevations of the land to the model causes the performance of the model to be weak so that it can simulate the surface currents resulting from local factors correctly.
According to the analyzes done with wind and statistics, it seems that the weather research and forecasting model is more weak in estimating the wind direction in the months when the average monthly wind speed is lower, and it can be said that in the months of July and October, the wind is generally controlled by local factors with Low speed is formed, on the other hand, due to static data with low spatial resolution, the morphology and morphology of the model is weak and due to the dependence of surface currents on topography, it causes a large error in the estimation of the wind direction by the model in the mentioned months, but this weakness in The cold months decrease with the passage of dynamic systems and the increase of the monthly average wind speed, but contrary to the wind direction, the wind speed estimation outputs by the model show that the increase of the monthly average wind speed causes a decrease in the accuracy of the model in the estimation of the wind speed variable, that is why in all the statistics, July has the best simulation in wind speed variable.
From the results of these studies, the selected configuration for the direction may not necessarily be associated with the desired results for the speed. It may even be possible to achieve the best output in the months of the year with different configurations. According to the selected boundary configurations and data, the results of this study seem to be consistent with the research of Santos et al. (2013), Gholami et al., Ghafarian et al. (2018), and Laighi et al. (2015) are confirmed.