Abstract: Weather radar interpolation is the process of estimating and predicting rainfall data in areas that are not directly observed by radar. This technique is commonly used in weather forecasting, flood prediction, and agricultural planning. The main goal of weather radar interpolation is to produce accurate and reliable precipitation maps in areas with limited radar coverage or where radar data is incomplete. The interpolation methods can be categorized into two main groups: deterministic and stochastic. Deterministic methods use mathematical equations and physical models to estimate the rainfall, while stochastic methods rely on statistical algorithms to analyze the correlations between the radar measurements and ground observations. In recent years, machine learning algorithms have also been applied to weather radar interpolation, showing promising results in terms of accuracy and robustness. In this paper, we mainly propose a radar image interpolation method based on spatio-temporal convolutional networks. The experiments are mainly compared and analyzed
for different combinations of networks, connection methods, and different loss functions.

Adviser: Venkatachalam Chandrasekar
Co-Adviser: Haonan Chen
Non-ECE Member: Thomas Siller, CE
Member 3: Steven Gooch, ECE
Addional Members: Steven Gooch, ECE

Publications:
None

Program of Study:
ECE411
ECE461
ECE462
ECE545
ECE512
ECE520
ECE699
MECH564