Weather Forecasting Using METAR Inputs Through Machine Learning: Good Forecasts at Lower Computation and Data Acquisition Cost
Modern weather forecasting is a computationally intensive activity, relying on expensive data sources like satellites and Doppler radar. This presentation explores an alternative, low-cost approach using machine learning to predict local weather conditions. By leveraging historical METAR data (routine reports published by airports), we can train a deep neural network to find predictive patterns that are difficult to discern with other methods.
This talk will detail the complete project lifecycle: from acquiring and cleansing over 7.5 million METAR records to the design, training, and tuning of the neural network using Pylorch. An emphasis is placed on explaining the steps generally used to develop machine learning applications. We will cover feature engineering, model architecture, and the results, which show the model can predict temperature 30 hours out with a Mean Absolute Error of about 2.6°C. While not matching the precision of state-of-the-art global systems, this method produces a reasonably accurate forecast using minimal resources, demonstrating the power of machine learning to create effective solutions without the need for supercomputers. At the close of the talk, we will demonstrate the tool in its current form.
This presentation will count for 1 Professional Development Hour (PDH) for the PE License in Wisconsin and Michigan.
Date and Time
Location
Hosts
Registration
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- 5 N. Systems Drive
- Appleton, Wisconsin
- United States 54914
- Building: D.J. Bordini Center at FVTC
- Room Number: BC112B
- Contact Event Hosts
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Reservations should be received by Tuesday, November 4 by registering from this web page.
The Dinner fee of $20, will be collected at registration.
IEEE Student Members may attend and enjoy dinner at the reduced cost of $10. Student members should register by emailing their IEEE member number to blluchs@ieee.org or oliveira@mtu.edu.
- Starts 17 October 2025 05:00 AM UTC
- Ends 06 November 2025 05:55 AM UTC
- Admission fee ?
Speakers
Greg Hawley
Biography:
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Greg Hawley is a Staff Project Manager at Plexus Corp. in Neenah, Wisconsin. A senior member of IEEE and a member since 1988, he has spent his career at Plexus leading the development of complex products in the medical, telecommunications, and industrial sectors. He began his career at Plexus as a firmware engineer developing software for embedded control and industrial systems.
Mr. Hawley is a registered Professional Engineer in the State of Wisconsin. He earned a Bachelor of Science in Electrical Engineering and a second major in Computer Science from the University of Wisconsin-Madison.
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He has completed professional development courses in FPGA design, machine learning, spacecraft dynamics, and power electronics. He actively supports the local engineering community by serving on advisory boards for Ripon College and the Milwaukee School of Engineering. In the summertime, he is an avid road cyclist, but in the winter, he pursues interesting projects like the one we'll discuss in this talk. |
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Agenda
5:00 Featured Speaker - Greg Hawley
D.J. Bordini Center at FVTC
6:30 Adjourn to Good Company
110 N. Richmond St.
Appleton, WI
6:45 Social time, cash bar open
Order meals from the restaurant menu
7:00 Section announcements, door prize drawing
Media
| 2025-11-06_WxForcastViaMachineLearning_Presentation | Slide deck for the Nov 6, 2025 meeting presentation. | 2.14 MiB |