Registration rates:
$16 for student members
$32 for members
$102 for non-members
AI in Weather Radars
This virtual short course will familiarize participants with fundamentals of AI and deep learning for weather radar applications.
March 28, 2022 at 10:00 AM - 2:00 PM Eastern Time (Virtual)
| Registration close date: | Monday, March 21, 2022 at 11:59 PM Eastern Time |
| Participant cap: | 75 |
Registration policy:
AMS requires a valid payment to be made within 5 days of the start of a course or sooner if registration has reached capacity. You will be contacted by AMS staff if payment is required. Refunds will not be issued to attendees within 7 days of the start of a course. Registrations are not transferable or exchangeable.
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.
Participants will need access to Zoom through either the web or desktop application.
Instructors:
V. Chandrasekar
Colorado State University
Haonan Chen
Colorado State University