five

Cloud index fields, cloud motion fields, and supplementary material.

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Mendeley Data2024-03-27 更新2024-06-27 收录
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https://zenodo.org/record/2574203
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This contains the data used in the paper: "Intra-hour cloud index forecasting with data assimilation." It also includes supplementary material referenced in the above paper. The CI_CMV_data.tar file contains data.nc and data_opt_flow.nc files that are in netCDF4 format. The data.nc files contain cloud index fields derived from GOES-15 geostationary satellite images and wind fields from a Weather Research and Forecasting (WRF) model run. The WRF_run_attributes.txt file contains the parameters of the WRF model that generated the wind fields contained in the data.nc files. The data_opt_flow.nc files contain dense optical flow cloud motion data that were derived using the method described in [1]. The data.nc and data_opt_flow.nc files are in a directory of the form: /yyyy/mm/dd/ corresponding to the year, month and day of the data. The six videos are made from images from the GOES-15 geostationary satellite in the GOES-WEST position. The images are centered around Tucson, AZ. The domain of the images are from a latitude of approximately 27.22° N to 37.22° N and from a longitude of approximately 115.97° W to 105.97° W. All satellite images include a rectangle with dotted edges forming a region over Tucson, AZ that is 40 km from west to east and 56 km from south to north. Some satellite images contain a rectangle with solid edges. This rectangle surrounds a computational domain used in cloud index forecasting. Three of the six videos (sat_images_2014_04, sat_images_2014_05, and sat_images_2014_06) contain the images for April, May and June of 2014. The other three videos (sat_images_2014_04_15, sat_images_2014_04_26, and sat_images_2014_05_29) are for 4/15/2014, 4/26/2014, and 5/29/2014. There are also 500 mb height maps for these three days from NCEP. [1] Sun, D., Roth, S., Black, M.J., 2010. Secrets of optical flow estimation and their principles, in: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 2432–2439. doi:10.1109/CVPR.2010.5539939.
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2023-06-28
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