Skip to Main Content
Predicting the temporal and spatial probability of orographic cloud cover in the Luquillo Experimental Forest in Puerto Rico using generalized linear (mixed) models.Author(s): Wei Wu; Charlesb Hall; Lianjun Zhang
Source: Ecological Modelling 192 :473–498
Publication Series: Miscellaneous Publication
PDF: View PDF (2.1 B)
DescriptionWe predicted the spatial pattern of hourly probability of cloud cover in the Luquillo Experimental Forest (LEF) in North-Eastern Puerto Rico using four different models. The probability of cloud cover (defined as “the percentage of the area covered by clouds in each pixel on the map” in this paper) at any hour and any place is a function of three topographic variables: aspect, slope and the difference between elevation and lifting condensation level.We chose the best models based on multiple statistics including the Akaike Information Criterion (AIC), scaled deviance and extra-dispersion scale. As a result, the generalized linear model (GLM) and one generalized linear mixed model (GLMM) with exponential spatial structure were the best candidate models. The probabilities of cloud cover in both our simulations and the observations increased with elevation, and were higher at night. They decreased in the morning after the sun rose until early afternoon, and then increased again for the rest of the day until night, apparently in response to the movement of the lifting condensation level. Two types of satellite images were available to calibrate our models: the higher spatial resolution, but expensive and infrequent Landsat-7 Enhanced Thermal Mapper plus (ETM+) images and the frequent, free, but low spatial resolution Moderate Resolution Imaging Spectroradiometer (MODIS) images. The derived probabilities of cloud cover when calibrated to the two types of remote sensing images were very similar, which justifies our using the free MODIS images instead of the Landsat images to calibrate the models.We applied the model to all months and the results indicated in agreement with the data that the probability of cloud cover is less during the dry season,higher in wet season and moderate for the rest of the year. We found our models could usually predict the probability of cloud cover for each 100m in elevation level at a certain time with an index of agreement (IoA) of 0.560–0.919 and at a certain location over a day with an IoA of 0.940–0.994, indicating a medium to good model simulation at that particular time or location.
- We recommend that you also print this page and attach it to the printout of the article, to retain the full citation information.
- This article was written and prepared by U.S. Government employees on official time, and is therefore in the public domain.
CitationWu, Wei; Hall, Charlesb; Zhang, Lianjun. 2006. Predicting the temporal and spatial probability of orographic cloud cover in the Luquillo Experimental Forest in Puerto Rico using generalized linear (mixed) models. Ecological Modelling 192 :473–498
KeywordsGeneralized linear model (GLM), Generalized linear mixed model (GLMM), Spatial autocorrelation, probability of cloud cover, MODIS, Landsat-7 ETM+
- Spatial modelling of evapotranspiration in the Luquillo experimental forest of Puerto Rico using remotely-sensed data.
- Fitting the multitemporal curve: a fourier series approach to the missing data problem in remote sensing analysis
- Creating cloud-free Landsat ETM+ data sets in tropical landscapes: cloud and cloud-shadow removal
XML: View XML