Atmospheric Science
ATPAD: An Accessible Tool for Atmospheric Data Processing and Visualization is a Python-based project that enables the analysis and visualization of pre-processed databases in an easy and freely accessible manner. As an example, we apply ATPAD to process and visualize data from the University Network of Atmospheric Observatories (RUOA) of the National Autonomous University of Mexico (UNAM), using three different stations located across Mexico. The access to the analyzed data-set an be found here.
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Access to continuous, quality assessed meteorological data is critical for understanding the climatology and atmospheric dynamics of a region. Research facilities like Oak Ridge National Laboratory (ORNL) rely on such data to assess site-specific climatology, model potential emissions, establish safety baselines, and prepare for emergency scenarios. To meet these needs, on-site towers at ORNL collect meteorological data at 15-minute and hourly intervals.
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This dataset includes 30 hyperspectral cloud images captured during the Summer and Fall of 2022 at Auburn University at Montgomery, Alabama, USA (Latitude N, Longitude W) using aResonon Pika XC2 Hyperspectral Imaging Camera. Utilizing the Spectronon software, the images were recorded with integration times between 9.0-12.0 ms, a frame rate of approximately 45 Hz, and a scan rate of 0.93 degrees per second. The images are calibrated to give spectral radiance in microflicks at 462 spectral bands in the 400 – 1000 nm wavelength region with a spectral resolution of 1.9 nm.
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