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Measuring rainshafts: Bringing Python to bear on remote sensing data

Scott Collis
Environmental Sciences Division, Argonne National Laboratory.

Scott Giangrande
Atmospheric Sciences, Brookhaven National Laboratory.

Jonathan Helmus
Environmental Sciences Division, Argonne National Laboratory.

Di Wu
NASA Goddard Space Flight Center.

Ann Fridlind
NASA Goddard Institute of Space Sciences.

Marcus van Lier-Walqui
NASA Goddard Institute of Space Sciences.

Adam Theisen
University of Oklahoma, Cooperative Institute for Mesoscale Meteorological Studies, ARM Climate Research Facility Data Quality Office.


Remote sensing data is complicated, very complicated! It is not only geometrically tricky but also, unlike in-situ methods, indirect as the sensor measures the interaction of the scattering media (eg raindrops) with the probing radiation, not the geophysics. However the problem is made tractable by the large number of algorithms available in the Scientific Python community. While SciPy provides many helpful algorithms for signal processing in this domain, a full software stack from highly specialized file formats from specific sensors to interpretable geospatial analysis requires a common data model for active remote sensing data that can act as a middle layer This paper motivates this work by asking: How big is a rainshaft? What is the natural morphology of rainfall patterns and how well is this represented in fine scale atmospheric models. Rather than being specific to the domain of meteorology, we will break down how we approach this problem in terms of the tools used from numerous Python packages to read, correct, map and reduce the data into a form better able to answer our science questions. This is a "how" paper, covering the Python-ARM Radar Toolkit (Py-ART) containing signal processing using linear programming methods and mapping using k-d trees. We also cover image analysis using SciPy's ndimage sub-module and graphics using matplotlib.


Remote sensing, radar, meteorology, hydrology

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