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Tracking fog occurrence and drivers in a mountainous Costa Rican rainforest using phenological camera imagery
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|Created:||Jun 17, 2020 at 9:37 p.m.|
|Last updated:|| Aug 17, 2021 at 7:03 p.m.
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Fog patterns were determined using web camera images collected at half-hour intervals and uploaded to the PhenoCam network. These images were analyzed to determine fog presence and intensity using the K-Means iterative algorithm, as implemented in Python. Atmospheric conditions were clustered into five different categories: clear, overcast, light fog, medium fog, and heavy fog. Ecohydrological variable data was gathered from sensors placed within the forest and at a nearby weather station. The quantified fog data was then compared with the ecohydrological variables; the diurnal patterns of fog and precipitation were determined over the entire dataset and during dry and wet months. In April, rain was present 2% of the time and fog was present in 68% of the images and in September rain was present 18% of the time and fog was present in 40% of the images. Occurrence of heavy fog conditions are consistently higher in January and December but daily appeared to be highest in the early mornings. A generalized linear model was used to relate fog occurrence with temperature, relative humidity, solar radiation, and wind speed.
|The content of this resource is derived from||https://ameriflux.lbl.gov/sites/siteinfo/Cr-SoC|
|The content of this resource is derived from||https://phenocam.sr.unh.edu/webcam/sites/soltis/|
This resource was created using funding from the following sources:
|Agency Name||Award Title||Award Number|
|National Science Foundation||Collaborative Research: Continental-Scale Monitoring, Modeling and Forecasting of Phenological Responses to Climate Change||EF-1065029|
|U.S. Department of Energy, Office of Science, Biological and Environmental Research||IMPROVING LAND-SURFACE MODELING OF EVAPOTRANSPIRATION PROCESSES IN TROPICAL FORESTS||DE‐SC0010654|
|National Science Foundation||REU Site: Ecohydrology of Tropical Montane Forests -- Diversity in Science, Interdisciplinary Breadth, and Global Awareness||EAR‐1659848|
People or Organizations that contributed technically, materially, financially, or provided general support for the creation of the resource's content but are not considered authors.
|Jaeyoung Song||Texas A&M University|
How to Cite
This resource is shared under the Creative Commons Attribution CC BY.http://creativecommons.org/licenses/by/4.0/