scholarly journals Simulating Impacts of Real-World Wind Farms on Land Surface Temperature Using the WRF Model: Validation with Observations

2017 ◽  
Vol 145 (12) ◽  
pp. 4813-4836 ◽  
Author(s):  
Geng Xia ◽  
Matthew C. Cervarich ◽  
Somnath Baidya Roy ◽  
Liming Zhou ◽  
Justin R. Minder ◽  
...  

This study simulates the impacts of real-world wind farms on land surface temperature (LST) using the Weather Research and Forecasting (WRF) Model driven by realistic initial and boundary conditions. The simulated wind farm impacts are compared with the observations from the Moderate Resolution Imaging Spectroradiometer (MODIS) and the first Wind Forecast Improvement Project (WFIP) field campaign. Simulations are performed over west-central Texas for the month of July throughout 7 years (2003–04 and 2010–14). Two groups of experiments are conducted: 1) direct validations of the simulated LST changes between the preturbine period (2003–04) and postturbine period (2010–14) validated against the MODIS observations; and 2) a model sensitivity test of LST to the wind turbine parameterization by examining LST differences with and without the wind turbines for the postturbine period. Overall, the WRF Model is moderately successful at reproducing the observed spatiotemporal variations of the background LST but has difficulties in reproducing such variations for the turbine-induced LST change signals at pixel levels. However, the model is still able to reproduce coherent and consistent responses of the observed LST changes at regional scales. The simulated wind farm–induced LST warming signals agree well with the satellite observations in terms of their spatial coupling with the wind farm layout. Moreover, the simulated areal mean warming signal (0.20°–0.26°C) is about a tenth of a degree smaller than that from MODIS (0.33°C). However, these results suggest that the current wind turbine parameterization tends to induce a cooling effect behind the wind farm region at nighttime, which has not been confirmed by previous field campaigns and satellite observations.

2019 ◽  
Vol 53 (3-4) ◽  
pp. 1723-1739 ◽  
Author(s):  
Geng Xia ◽  
Liming Zhou ◽  
Justin R. Minder ◽  
Robert G. Fovell ◽  
Pedro A. Jimenez

2020 ◽  
Vol 12 (16) ◽  
pp. 2573
Author(s):  
Si-Bo Duan ◽  
Xiao-Jing Han ◽  
Cheng Huang ◽  
Zhao-Liang Li ◽  
Hua Wu ◽  
...  

Land surface temperature (LST) is an important variable in the physics of land–surface processes controlling the heat and water fluxes over the interface between the Earth’s surface and the atmosphere. Space-borne remote sensing provides the only feasible way for acquiring high-precision LST at temporal and spatial domain over the entire globe. Passive microwave (PMW) satellite observations have the capability to penetrate through clouds and can provide data under both clear and cloud conditions. Nonetheless, compared with thermal infrared data, PMW data suffer from lower spatial resolution and LST retrieval accuracy. Various methods for estimating LST from PMW satellite observations were proposed in the past few decades. This paper provides an extensive overview of these methods. We first present the theoretical basis for retrieving LST from PMW observations and then review the existing LST retrieval methods. These methods are mainly categorized into four types, i.e., empirical methods, semi-empirical methods, physically-based methods, and neural network methods. Advantages, limitations, and assumptions associated with each method are discussed. Prospects for future development to improve the performance of LST retrieval methods from PMW satellite observations are also recommended.


2012 ◽  
Vol 41 (2) ◽  
pp. 307-326 ◽  
Author(s):  
Liming Zhou ◽  
Yuhong Tian ◽  
Somnath Baidya Roy ◽  
Yongjiu Dai ◽  
Haishan Chen

2012 ◽  
Vol 16 (8) ◽  
pp. 6432-6437 ◽  
Author(s):  
Jenell M. Walsh-Thomas ◽  
Guido Cervone ◽  
Peggy Agouris ◽  
Germana Manca

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