Morphing-based fusion of weather modeling and satellite observations for improving estimation of diurnal land surface temperatures
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Keywords: land surface temperature, diurnal cycle, WRF, morphing technique, urban heat island
Abstract Type: Paper Abstract
Authors:
Wei Chen, Iowa State University
Yuyu Zhou,
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Abstract
The trade-off between spatial and temporal resolution in satellite-derived LST and potential bias in weather modelling-derived LST have greatly limited the availability of concurrently high spatiotemporal LST data for wide applications. We proposed a WRFM framework to mitigate those limitations by integrating the Weather Research and Forecasting model (WRF) with the morphing technique. First, we performed the WRF simulation from June to August 2019 in the Des Moines-West Des Moines Metropolitan Statistical Area, IA, USA. Second, we integrated gap-filled MODIS LST and WRF-simulated LST using the morphing technique. Third, we evaluated the improved LST spatial-temporal patterns using satellite observations and in situ LST measurements. The results showed that the WRFM can mitigate bias of magnitude in LST from the WRF simulation with the root mean square error (RMSE) reduced from 6.8 ℃ to 2.5 ℃ while still capturing hourly patterns of LST. Overall, the WRFM effectively integrated complementary advantages of satellite observations and weather modeling and can generate accurate LSTs with high spatiotemporal resolutions in areas (e.g., urban) with complex landscapes.
Morphing-based fusion of weather modeling and satellite observations for improving estimation of diurnal land surface temperatures
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Paper Abstract