Toward a Climatology of Fog Frequency in the Atacama Desert via Multispectral Satellite Data and Machine Learning Techniques
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| Title: | Main Title: Toward a Climatology of Fog Frequency in the Atacama Desert via Multispectral Satellite Data and Machine Learning Techniques |
| Description: | Abstract: In many hyperarid ecosystems, such as the Atacama Desert, fog is the most important freshwater source. To study biological and geological processes in such water-limited regions, knowledge about the spatiotemporal distribution and variability of fog presence is necessary. In this study, in situ measurements provided by a network of climate stations equipped, inter alia, with leaf wetness sensors are utilized to create a reference fog dataset that enables the validation of satellite-based fog retrieval methods. Further, a new satellite-based fog-detection approach is introduced that uses brightness temperatures measured by the Moderate Resolution Imaging Spectroradiometer (MODIS) as input for a neural network. Such a machine learning technique can exploit all spectral information of the satellite data and represent potential nonlinear relationships. Relative to a second fog-detection approach based on MODIS cloud-top height retrievals, the neural network reaches a higher detection skill (Heidke skill score of 0.56 as compared with 0.49). A suitable representation of temporal variability on subseasonal time scales is provided with correlations mostly greater than 0.7 between fog occurrence time series derived from the neural network and the reference data for individual climate stations, respectively. Furthermore, a suitable spatial representativity of the neural-network approach to expand the application to the whole region is indicated. Three-year averages of fog frequencies reveal similar spatial patterns for the austral winter season for both approaches. However, differences are found for the summer and potential reasons are discussed. |
| Identifiers: | 10.1175/JAMC-D-20-0208.1 (DOI) https://doi.org/10.1175/JAMC-D-20-0208.1 (URL) |
| Citation Advice: | Böhm, C., Schween, J. H., Reyers, M., Maier, B., Löhnert, U., & Crewell, S. (2021). Toward a Climatology of Fog Frequency in the Atacama Desert via Multispectral Satellite Data and Machine Learning Techniques, Journal of Applied Meteorology and Climatology, 60(8), 1149-1169. |
Responsible Party
| Creators: | Christoph Böhm (Author), Jan Schween (Author), Mark Reyers (Author), Benedikt Maier (Author), Ulrich Löhnert (Author), Susanne Crewell (Author) |
| Funding Reference: | Deutsche Forschungsgemeinschaft (DFG): CRC 1211: Earth - Evolution at the Dry Limit |
| Publisher: | American Meteorological Society |
| Publication Year: | 2021 |
Topic
| CRC1211 Topic: | Climate |
| Related Subprojects: | A1, A3 |
| Subjects: | Keywords: Fog, Meteorology, Satellite remote sensing |
| Geogr. Information Topic: | Climatology/Meteorology/Atmosphere |
File Details
| Filename: | Boehm_et_al_2021_JAMC_Toward_a_Climatology_of_Fog_Frequency_in_the_Atacama_Desert_via_Multispectral_Satellite_Data_and_Machine_Learning_Techniques.pdf |
| Data Type: | Text - Article |
| File Size: | 3.4 MB |
| Dates: | Accepted: 07.06.2021 Available: 01.08.2021 |
| Mime Type: | application/pdf |
| Data Format: | |
| Language: | English |
| Status: | Completed |
Constraints
| Download Permission: | Only Project Members |
| General Access and Use Conditions: | According to the CRC1211DB data policy agreement. |
| Access Limitations: | According to the CRC1211DB data policy agreement. |
| Licence: | [Creative Commons] Attribution-NonCommercial-NoDerivs 4.0 Unported (CC BY-NC-ND 4.0) |
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Specific Information - Publication
| Publication Status: | Published |
| Review Status: | Peer reviewed |
| Publication Type: | Article |
| Article Type: | Journal |
| Source: | Journal of Applied Meteorology and Climatology |
| Source Website: | https://doi.org/10.1175/JAMC-D-20-0208.1 |
| Issue: | 8 |
| Volume: | 60 |
| Number of Pages: | 21 (1149 - 1169) |
Metadata Details
| Metadata Creator: | Christoph Böhm |
| Metadata Created: | 14.01.2022 |
| Metadata Last Updated: | 30.11.2023 |
| Subproject: | A1 |
| Funding Phase: | 2 |
| Metadata Language: | English |
| Metadata Version: | V50 |
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| Page Visits: | 151 |
| Metadata Downloads: | 0 |
| Dataset Downloads: | 0 |
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