Supplementary data for: Nowcasting 3D cloud fields using forward warping optical flow
Creators
- 1. Colorado State University
- 2. Cooperative Institute for Research in the Atmosphere
Description
Large- to global-domain short-term prediction of clouds (0-3 hours), or cloud nowcasting, remains relevant to civilian and military applications ranging from solar energy production to intelligence gathering. Despite the capabilities of contemporary numerical weather prediction models, nowcasting methods based on near real-time observations (i.e. satellite imagery) hold operational value due to their relative computational efficiency and accuracy for short-term applications. A commonly used nowcasting approach involves using two or more images to retrieve the apparent motions of features, or optical flow, which can be used to extrapolate the future location of those features. However, such approaches generally assume that the optical flow field remains unchanged with respect to time which is challenging to apply to piecewise cloud fields from satellite imagery. Here, we propose a method to nowcast clouds that adapts a computer vision technique for image interpolation, commonly referred to as warping, to account for temporal changes to optical flow fields derived from infrared satellite imagery. We evaluate the proposed method for 991 randomly selected regional cases from 2024 and perform a detailed analysis on three specific cases. Applying a dense (every image pixel) optical flow retrieval technique to full-disk GOES infrared imagery, we demonstrate that forward warping of the optical flow field when coupled with simple occlusion reasoning, improves skill in cloud nowcasting.
Notes
Funding provided by: Office of Naval Research
ROR ID: https://ror.org/00rk2pe57
Award Number: N00014-16-1-2040
Funding provided by: Office of Naval Research
ROR ID: https://ror.org/01awap711
Award Number: N00014-21-1-2112
Funding provided by: National Aeronautics and Space Administration
ROR ID: https://ror.org/027ka1x80
Award Number: N80NSSC21K0919
Funding provided by: Office of Naval Research
ROR ID: https://ror.org/00rk2pe57
Award Number: N0001424C2214
Methods
Explanation of code: Nowcast_Example.py provides an example of how to run the I-NOW nowcasting method on GOES-16 Band 13 data. Code downloads GOES-16 Band 13 data from AWS, runs a python version of OCTANE optical flow code to calculate the optical flow using the values contained in the JTEC article. Rather than linear extrapolation, the code uses a warping method formed from computer vision temporal interpolation methods to account for occlusions and time related changes to the optical flow field in the forward time direction rather than a time that is intermediate of two images. The code provides nowcasts of GOES-16 10.3 micron brightness temperature,CLAVR-x derived cloud top heights (CTH), and CLAVR-x derived cloud base heights (CBH). Observed brightness temperature data and CLAVR-x data are plotted alongside the nowcast results in 10-minute intervals. The code also creates animated gifs of the nowcasts to easier visualize the results through time. The code is designed to be run on a GPU with CUDA support, and the user can define the save directory for the results to be saved and data that is downloaded. CLAVR-x data is not downloadable from the internet,so the repository contains a folder with CLAVR-x data for the example date and time used in the code. The CLAVR-x files have been reduced to contain only the required CTH and CBH data instead of full CLAVR-x retrieval data to save on storage space in the repository.
Files
I-NOW.zip
Files
(1.7 GB)
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Additional details
Related works
- Is derived from
- https://github.com/AFwxking/I-NOW (URL)
- Is source of
- 10.5061/dryad.jm63xsjmd (DOI)