University of Twente Student Theses
Satellite nowcasting of cloud coverage via machine learning
Lazorenko, D. (2023) Satellite nowcasting of cloud coverage via machine learning.
|
PDF
6MB |
| 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) |
| Clients: | Infoplaza |
| Faculty: | EEMCS: Electrical Engineering, Mathematics and Computer Science |
| Subject: | 54 computer science |
| Programme: | Business & IT BSc (56066) |
| Link to this item: | https://purl.utwente.nl/essays/94383 |
| Export this item as: | BibTeX EndNote HTML Citation Reference Manager |
Show download statistics for this publication
Show download statistics for this publicationRepository Staff Only: item control page