Publikationsserver
Satellite-Based Fog Detection: A Dynamic Retrieval Method for Europe Based on Machine Learning
Fog has many economic as well as ecological impacts and it directly affects human life in many ways. The large number of fog influence factors shows that a comprehensive understanding of its causes and a precise mapping of the spatio-temporal distribution patterns are of great interest. Since there...
I tiakina i:
| Kaituhi matua: | |
|---|---|
| Ētahi atu kaituhi: | |
| Hōputu: | Dissertation |
| Reo: | Ingarihi |
| I whakaputaina: |
Philipps-Universität Marburg
2019
|
| Ngā marau: | |
| Urunga tuihono: | Kuputuhi katoa PDF |
| Tags: |
Tāpirihia he Tūtohu
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| Whakarāpopototanga: | Fog has many economic as well as ecological impacts and it directly affects human life in many ways. The large number of fog influence factors shows that a comprehensive understanding of its causes and a precise mapping of the spatio-temporal distribution patterns are of great interest. Since there are justifiable concerns about the general applicability of existing fog retrieval methods, this thesis investigates new techniques of satellite based fog detection and the derivation of spatio-temporal information on fog distribution in Europe. The central novelties of this study are:
- No static assumptions about microphysical properties were used during fog retrieval.
- A novel hybrid approach based on machine learning methods was developed that can be continuously applied 24 hours a day.
- The algorithm covers all fog types. Areas of different fog types could also be differentiated indirectly from the generated product due to their typical diurnal and annual frequency cycles.
- For the first time, fog frequency maps for Europe could be produced for different general weather situations separately for each fog type. |
|---|---|
| Whakaahuatanga ōkiko: | 157 Seiten |
| DOI: | 10.17192/z2019.0219 |