University of Twente Student Theses


Satellite nowcasting of cloud coverage via machine learning

Lazorenko, D. (2023) Satellite nowcasting of cloud coverage via machine learning.

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Abstract:Satellites orbiting the earth make it possible to provide increasingly accurate weather forecasts. Through observations and numerical weather predictions, meteorologists can make near-future weather forecasts which have a major impact on everyday decisions in different societal sectors. However, unexpected cloud appearances often lead to inaccuracies in nowcasting weather or radiation. We propose the usage of machine learning via a Convolutional LSTM Neural Network. In particular, we validate the performance of learning spatial and temporal patterns within a sequence of satellite images using a Convolutional Long Short-Term Memory model architecture for cloud coverage prediction. The accuracy of the machine learning model was found to be 90 percent upon training on a dataset spanning a period of two years. However, additional efforts are deemed necessary to address and remove biases present within the model.
Item Type:Essay (Bachelor)
Faculty:EEMCS: Electrical Engineering, Mathematics and Computer Science
Subject:54 computer science
Programme:Business & IT BSc (56066)
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