Advanced geostationary imagers produce data at a higher spatial, spectral, and temporal than previously available to the meteorological community. This imagery is increasingly difficult to analyze with traditional visualization software due to its large size. SIFT development began in 2015 to provide a fluid, lightweight, and easy-to-use analysis experience for training workshops on meteorological satellite imagery. Beyond image visualization, SIFT includes tools for the user to probe for data values at a specific point, compare a selected geographic region across multiple images via scatter plots, pan and zoom to specific areas of an image, and animate a series of images. SIFT can combine spectral bands to make custom Red-Green-Blue (RGB) composites, band differences, and other arithmetic band composites. To accomplish this, SIFT is built on powerful Python libraries like VisPy, NumPy, Numba, PyQt4, and matplotlib, with minimal loss of performance that may otherwise be expected from visualizing large datasets. SIFT relies heavily on the computer’s graphics processing unit (GPU) to display and enhance the imagery being analyzed and provide a responsive user experience.
Open for community development, SIFT users and features continue to grow. SIFT is freely available with short tutorials and a user guide online. The mandate for the software, its development, realized applications, and envisioned role in science and training are explained.
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