Machine Learning Opportunity for Global Weather Radar

  • Published
  • By David R. Hopper
  • 55th Wing Public Affairs

This is the first of a three-part series covering the innovative work by 557th Weather Wing for ongoing development efforts to leverage machine learning for a weather radar depiction across the globe, designated the Global Synthetic Weather Radar.

“Radar data is extremely valuable environmental intelligence as it offers both operators and meteorologists insight into the state of the atmosphere,” said Chris Finnigsmier, 557th WW’s technical director. “Unfortunately, radar images are limited to areas adjacent to physical radar systems and thus unavailable across vast swaths of the planet.”

Research and development on the GSWR started when Massachusetts Institute of Technology’s Lincoln Laboratory combined weather data from existing sources and applied machine-learning techniques to create synthetic radar-like mosaics in areas just offshore of the continental United States with no radar coverage.   

Air Force leaders recognized the potential of these efforts, especially in high interest areas outside the continental U.S., and sponsored MIT/LL to produce global radar-like mosaics. The 557th WW’s high-resolution weather model, combined with satellite data from U.S. and allied sources, along with commercial global lightning data, feed the GSWR’s ability to conduct machine learning model training against actual precipitation data that has been collected by NASA. 

If proven to be operationally viable, the 28th Operational Weather Squadron, located at Shaw Air Force Base, S.C. stands to benefit greatly from this capability.  The 28th OWS is responsible for characterizing the weather environment for the Middle East theater, a part of the world where there is very little weather radar capabilities.

“The 28th OWS, not unlike our fellow overseas-focused environmental intelligence squadrons, is challenged in having enough reliable data to craft the risk-based intelligence necessary to deliver decision-makers high-confidence assessments,” said Maj. Andrew Williams, 28th OWS commander. “(The 28th OWS) directly supports a strategic theater involved in multiple National Defense Strategy priorities, often with minimal verifiable data.”

The 28th OWS relies on the experience of its forecasters to overcome reliable data shortfalls.

“The 28th’s mission capability would be enhanced by having the capability of synthetic radar to increase the confidence of our short-range assessments over areas of interest in the (U.S. Central Command) area of responsibility,” Williams said. “As the theater continues to deal with violent extremism and countering malign influence, a GWSR enhanced through machine learning can help overcome these data gaps to maximize global operations.”

The 1st Weather Group is scheduled to conduct an initial assessment of GSWR’s operational capability, with feedback anticipated to go directly to MIT/LL developers and eventually inform Air Force Materiel Command and Air Combat Command whether it’s operationally feasible and ready for an operational test and evaluation.  The Air Force Weather Systems operational test unit, 2nd Combat Weather Systems Squadron, also in the 557th WW, will lead the operational test once GSWR matures to that point.

If a 2nd CWSS-led operational test and operational utility evaluation prove this capability to be operationally feasible, this ML breakthrough for weather radar is an opportunity to fill the void across vast swaths of data-sparse regions in which U.S. air, space, land and sea forces operate. 

Observations from 1st WXG’s initial evaluation will be the subject for the second article in this series.

Public Affairs representative Paul Shirk contributed to this article.