Deep Learning for Aviation Turbulence Forecasting with Numerical Weather Prediction Models

The Hong Kong University of Science and Technology
Department of Computer Science and Engineering


MPhil Thesis Defence


Title: "Deep Learning for Aviation Turbulence Forecasting with Numerical 
Weather Prediction Models"

By

Mr. Nai Chit FUNG


Abstract:

In this thesis, we explore the use of deep learning models to enhance numerical 
weather prediction (NWP) forecasts for aviation turbulence. NWP models forecast 
atmospheric state by numerically solving mathematical equations of known 
physical laws. Currently, it is the primary way to forecast weather including 
aviation turbulence. Using the NWP forecasts as input, we utilize deep neural 
network to generate refined prediction with forecast time range from 0 to 60 
hours. The model output is the predicted turbulence intensity level: (i) nil or 
light, (ii) moderate, and (iii) severe. Experiments are performed on a dataset 
of 53,380 turbulence reports collected from year 2018 to 2022. Results show 
that the proposed model outperforms commonly-used turbulence forecasting 
baselines, including the significant weather (SIGWX) charts issued by World 
Area Forecast Center (WAFC) and diagnostic indices generated by NWP models.


Date:                   Monday, 7 October 2024

Time:                   2:00pm - 4:00pm

Venue:                  Room 4472
                        Lifts 25/26

Chairman:               Dr. Dan XU

Committee Members:      Prof. James KWOK (Supervisor)
                        Prof. Pedro SANDER