Python provides a great number of tools for accessing geophysical data provided by NASA via its MODIS satellites or the data from the National Weather Service. Examples of useful modules include tools that provide numerical operations (numpy/scipy), searching (lists), access to scientific data formats (h5py/gdal), geodetic transformation (gdal) and database bindings (mysql). Further, most modules work “out of the box” and are cross-platform compatible. It is therefore a good platform for building our software which attempts to provide large-scale geophysical data processing such as processing satellite data covering the entire United States for the span of one of multiple years. The software has four intended purposes: data acquisition, indexing, processing and data mining. An object-oriented approach was used in the code design for the purpose of extensibility and design quality. The output products are accessible on the web in various formats such as KML. We will showcase the system using air quality calculations based on the PM 2.5 (Particular Matter) standard. These products include time-series as well as spatial data products.