AI in Weather Radars

This in-person short course will familiarize participants with fundamentals of AI and deep learning for weather radar applications. 

Sunday, January 23, 2022 (8:45 AM - 3:45 PM)

This course will be converted to a virtual format (more details forthcoming).

Duration: Full Day
Participant Cap: Not Announced

Course Description:

Modern ground based weather radars are mostly dual-polarized and they are rich in information content in multiple dimensions and are ideal candidates for effective artificial intelligence (AI) applications. There are a large number of dual-polarization radars around the world. In addition, space borne weather radars such as Tropical Rainfall Measuring Mission (TRMM) Precipitation Radar (PR) and Global Precipitation Measurement (GPM) mission Dual-frequency Precipitation Radar (DPR) have also produced rich observations that are great to be analyzed using AI. AI has already been used with weather radar information long before it became popular in mainstream, such as use of neural networks for ingesting vertical profiles and using neuro-fuzzy systems for hydrometeor classification.

The primary goal of this short course is to familiarize participants with fundamentals of AI and deep learning, for weather radar applications. The course will introduce basic principles of modern weather radars covering both ground and space borne systems. The course will then immerse students into three different weather radar applications, namely, precipitation/storm classification, quantitative precipitation estimation, and nowcasting, each covering different aspects of data sciences. The overarching goal is to improve hydrometeorological forecasting and warnings through the lens of AI.

View Preliminary Agenda

Each participant must bring a laptop capable of wireless internet to access the Google Colab software for hands-on exercises.


headshot of V. Chandrasekar
V. Chandrasekar

Colorado State University

headshot of Haonan Chen
Haonan Chen

Colorado State University

For more information, please contact Haonan Chen at