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Camera Images Ceiling and Visibility (CICV) database
The purpose of this database is to be able to provide a constant, real-time/up-to-date set of data points which will then be used to train a Machine Learning (ML) algorithm that can produce it's own real-time cloud ceiling data along with the ability to forecast ceiling heights.
Documentation and information is provided below; related to the collection of Camera Images and Ceiling (CIC) data and creation of a database for a ML algorithm which will be trained on the combined data from three types of data sites within a given geographical area available for the public:
- Surface CAM images (by extracting images and features from CAM imagery such as weather cameras, road cameras, etc),
- Ceilometers (by processing data and numbers from METAR, ASOS, AWOS, etc)
Public Ceiling and Visibility Datasets
Public Camera Image Sets
- ND DOT
- Unified Ceilometer Network
- Skyline Webcams
- Valley News Live Skycam Network
- Weather USA
- See.Cam
- IA DOT
Excel Sheet with Organized Data:
Creating the Database
Once downloaded, they will be documented and paired with metadata such as:
- Location
- Date
- Camera Location
- METAR/ASOS Features
This categorization and metadata pairing will allow for organized and structured access to the ceiling data within the CIC database.
Airport Coordinates: https://airportcodes.aero/
DMS/DD Coordinate Converter: https://gps-coordinates.org/coordinate-converter.php
Methods
The following link provides a step-by-step run down on how research for this project is conducted and how data is obtained.