While GOES animation code will not run on older Internet Explorer browsers,
they work in the newest versions of Microsoft Edge. If you are using
Internet Explorer, please try a different browser: Chrome, Firefox, Safari, or
MS Edge are all supported.
Air Mass - RGB based on data from IR & water vapor - 21 Nov 2024 - 0816 UTC
Air Mass - RGB based on data from IR & water vapor - 21 Nov 2024 - 0821 UTC
Air Mass - RGB based on data from IR & water vapor - 21 Nov 2024 - 0826 UTC
Air Mass - RGB based on data from IR & water vapor - 21 Nov 2024 - 0831 UTC
Air Mass - RGB based on data from IR & water vapor - 21 Nov 2024 - 0836 UTC
Air Mass - RGB based on data from IR & water vapor - 21 Nov 2024 - 0841 UTC
Air Mass - RGB based on data from IR & water vapor - 21 Nov 2024 - 0846 UTC
Air Mass - RGB based on data from IR & water vapor - 21 Nov 2024 - 0851 UTC
Air Mass - RGB based on data from IR & water vapor - 21 Nov 2024 - 0856 UTC
Air Mass - RGB based on data from IR & water vapor - 21 Nov 2024 - 0901 UTC
Air Mass - RGB based on data from IR & water vapor - 21 Nov 2024 - 0906 UTC
Air Mass - RGB based on data from IR & water vapor - 21 Nov 2024 - 0911 UTC
Key for AirMass RGB:
1 - Jet stream / potential vorticity (PV) / deformation zones / dry upper level (dark red / orange)
2 - Cold air mass (dark blue/purple)
3 - Warm air mass (green)
4 - Warm air mass, less moisture (olive/dark orange)
5 - High thick cloud (white)
6 - Mid level cloud (tan/salmon)
7 - Low level cloud (green, dark blue)
8 - Limb effects (purple/blue)
Air Mass RGB is used to diagnose the environment surrounding synoptic systems by enhancing temperature and moisture characteristics of airmasses. Cyclogenesis can be inferred by the identification of warm, dry, ozone-rich descending stratospheric air associated with jet streams and potential vorticity (PV) anomalies. The RGB can be used to validate the location of PV anomalies in model data. Additionally, this RGB can distinguish between polar and tropical airmasses, especially along upper-level frontal boundaries and identify high-, mid-, and low-level clouds.