GEOG 473: Seminar on Geographic Research II

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The use of remotely sensed temperature products is becoming increasingly prevalent in research on climate change due to their accessibility, low cost, and spatial and temporal coverage. These studies use gridded products to model near-surface ambient temperatures as many often lack the resources and time to conduct on- site temperature measurements. Previous studies have compared the effectiveness of these gridded products against each other and weather station data (Vavrus et al. 2015, McEvoy et al. 2014). However, little research has been done to determine the effectiveness of these gridded products in modeling ambient climate. For this study, 65 LogTag temperature sensors were deployed in shielded housing units (Holden 2013) at Kofa wildlife refuge in Southwestern Arizona and 80 LogTag sensors were placed on Mt. Washington of the Snake Range of Eastern Nevada. From 2014 to 2015, the sensors recorded temperature hourly. Sensor-collected temperatures were compared against three widely used gridded temperature products to quantify accuracy of each product on modeling ambient temperatures at a small scale. The gridded products used in this study are the NLDAS dataset, PRISM dataset and Daymet dataset, at all have varying spatial resolutions of 10 km, 4 km and 1 km respectively. The Daymet and PRISM products provided daily maximum and daily minimum temperatures, while NLDAS and the LogTag provided hourly readings. In order to compare these products, it was necessary to interpolate hourly values from daily minimums and maximums. The Cosine fit with variable sunrise consists of a simple cosine curve based on daily minimum and maximum temperatures, with sunrise incorporated to fit daily maximum and minimum temperatures while the Chillr package in R (Luedeling 2016) uses latitude to fit hourly values based on location. The Chillr package in R was compared to the cosine fit with variable sunrise cited in Schaub 1991.

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Creative Commons Attribution-Noncommercial-Share Alike 3.0 License
This work is licensed under a Creative Commons Attribution-Noncommercial-Share Alike 3.0 License.

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