Talk: Machine Learning for Earth System Prediction and Predictability, 11/13
11-12pm Wed., Nov. 13, in-person in Physics 401 at UMBC
Professor Maria Janeth Molina from the University of Maryland talk on Machine learning for Earth System Prediction and Predictability, 11-12pm on Wednesday, November 13 in Physics 401 at UMBC.
Machine learning can be used for Earth system prediction, or to study the uppermost limit of prediction skill theoretically achievable given the system's initial state or other factors, otherwise known as predictability. In traditional numerical weather prediction frameworks, we solve the governing partial differential equations starting from an initial state. This initialized prediction framework usually involves three stages: 1) generating the initial conditions of the Earth system, 2) running the mathematical representation of the system on a computer forward in time, and 3) analyzing the output and converting it into a format that is useful for end users.
UMBC Center for AI
Machine learning can be used for Earth system prediction, or to study the uppermost limit of prediction skill theoretically achievable given the system's initial state or other factors, otherwise known as predictability. In traditional numerical weather prediction frameworks, we solve the governing partial differential equations starting from an initial state. This initialized prediction framework usually involves three stages: 1) generating the initial conditions of the Earth system, 2) running the mathematical representation of the system on a computer forward in time, and 3) analyzing the output and converting it into a format that is useful for end users.
Machine learning can be used to improve each of these individual stages or to circumvent the three-stage framework altogether, and examples of each will be given in this seminar. More time during the seminar will be dedicated to the challenges surrounding subseasonal prediction, which focuses on lead times of three to four weeks, and how we can use machine learning to both uncover potential biases in our initialized prediction systems and how we can bias-correct them.
UMBC Center for AI
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Posted: November 10, 2024, 7:32 PM
