The Geolocated Information Processing System (GeoIPS) – A Platform for Collaborative Development

There is an ever-growing number of disparate data sources available for advanced environmental exploitation and characterization including: numerical model outputs, rapid refresh next generation geostationary weather satellites, polar orbiting microwave imagers and sounders, radar data, direct observations from ships and weather stations, climatology, elevation and emissivity databases, and many more data types, both static and dynamic. The amount of information that can be gained by combining these datasets in unique ways is far greater than from any single data type alone, but it can be a cumbersome process integrating these datasets of varying size, shape, resolution, and projection.

With the plethora of weather satellites coming on line and the added benefit from combining datasets into new products, it is imperative to develop a sustainable, open source, community supported, efficient, modular processing platform which enables future functionality and facilitates near real-time operational capability for all new sensors and products.

GeoIPS®, a Python 3 data processing platform developed by the U.S. Naval Research Laboratory Marine Meteorology Division (NRL-MMD), is beginning to provide a collaborative, easy-to-use processing system that can support development efforts creating unique products from many different data sources, and facilitate streamlined operational transitions of these new products.

GeoIPS® will deliver a much needed capability for efficient environmental data processing, benefiting users across the operational and research communities. The collaborative nature of GeoIPS® development will lead to increased efficiency and functionality of the final product.

This talk will provide an overview on the processing workflow structure, which includes modularized plugins for data readers, interpolation routines, algorithms, and output formats. We will also highlight some of the existing capabilities in GeoIPS®, and current collaboration efforts with NRL-MMD.