Multispectral Imaging Information
This blog post will explore an experimental RGB “sandwich” product that has shown application in monitoring convection, particularly during the early stages of the convective lifecycle (aka convective initiation). The intended purpose of this revolves around identifying, interrogating, and monitoring these features of interest:
- Moisture gradients as a source of focused ascent.
- Early stages of convective lifecycle (i.e. convective initiation).
- Cloud top structure and movement to glean additional info like stability, shear, and overall convective behavior.
An experimental RGB dubbed “Moisture Gradient Convective RGB” was developed using bands within the Advanced Baseline Imagery (ABI) onboard the current operational GOES satellites.
The Split Window Difference product (10.35 – 12.3 um) has shown utility in identifying low level moisture gradients, and has been shared numerous times on this blog (Just a few examples: here, here, and here). The 1.37 um Cirrus Band as well as the 1.61 um Snow/Ice Band can be useful in monitoring the early stages of the convective lifecycle . And when looking to interrogate cloud top structure and movement, good ole trusty 0.64 um Visible Band is a clear winner.
The Moisture Gradient Convective RGB recipe* is as follows:
- R: 1.37 um Cirrus Band
- Min – Max: 0 – 10
- Gamma: 1.90
- G: Split Window Difference product (10.3-12.3 um)
- Min – Max: 0 – (-6)
- Gamma: 1.00
- B: 1.61 um Snow/Ice Band
- Min – Max: 0 – 70
- Gamma: 0.50
*Note on the RGB recipe: this recipe has not been tested for optimization across different regions and environments. Ranges and gamma values will likely need to be adjusted depending on the moisture and thermal environment.
Here is an example of this RGB:
But wait, where’s the 0.64 um Visible Band to help in interrogating cloud top structure and movement??
This is where the “Sandwich” approach is handy. We simply overlay a semi-transparent 0.64 um Visible Band on top of the RGB to add such desired information:
The RGB Sandwich requires daylight for this application, and is most useful with limited to no higher clouds masking information on moisture gradients and growing, infant convection.
In areas absent of masking cloud cover, greener colors represent relatively greater low level moisture content compared to areas that are bluer. Within clouds, red coloring represents clouds that have grown vertically high enough to extend into a dry air mass aloft while exhibiting glaciation (could either be optically thick cirrus and/or glaciated tops of robust convection), whereas purple-blue clouds represent shallower liquid phased clouds. Clouds that are more yellow-orange represent optically thin cirrus clouds.
Application Example: Southern Plains Severe Convection March 2, 2023
Robust convection was anticipated within central Texas March 2, 2023, initiated along a merging dryline/Pacific front. Identifying these initiating boundaries and how/when convection develops along these boundaries would prove useful in operational forecasting and decision support services.
Near the southern High Plains and Trans Pecos region of Texas, congestus cumulus started to develop between 1700 – 1730 UTC along one of these boundaries: a dryline, particularly near the Midland and Big Springs area. The useful Day Cloud Phase Distinction RGB has a proven track record in monitoring for convective initiation (as seen above), and can show the infant cumulus just starting to sprout.
Supplementing this imagery with automated surface observations and objective analysis can be useful in identifying boundaries suspect of initiating convection. However, there still is some form of interpolation needed when these boundaries don’t have attached clouds to help identify them. This is where adding information on moisture gradients can come in handy.
Comparing the Day Cloud Phase Distinction RGB to the Moisture Gradient Convective RGB Sandwich, can the exact location of the initiating boundary/ies location be identified in between areas of growing cumulus?
The location of the dryline is apparent at this time (orange annotated line), including other boundaries where low level moisture is converging (black annotated lines).
Moving forward with time, we can see cumulus agitate and grow right along this boundaries, particularly near Big Springs where the dryline “bulged” forward and where moisture convergent boundaries intersected. Additionally, one can notice a collection of mesoscale, convergent moisture bands just ahead of the dryline, acting as additional local sources of initiation and moisture pooling.
While the Moisture Gradient Convective RGB Sandwich can be very applicable in dryline situations, it can be useful in any other convective scenarios where moisture gradients are suspected to be a source of initiation so long as there aren’t any masking clouds to cover moisture information given by the Split Window Difference product. Of course all limitations of any of the RGB components must be considered when utilizing this multispectral imaging.
Important caveats to keep in mind when using the Split Window Difference (SWD) to analyze low-level moisture features include:1) must have clear sky conditions, 2) the SWD values depend on low level moisture content AND low-level lapse rates (see basic graphic below). For example, in the presence of moisture, a steeper positive ll lapse rate (temperature decreasing with height) will result in greater SWD values compared to weaker positive ll lapse rates with the same amount of moisture. A neutral or negative lapse rate (temperature increasing with height) in the presence of low-level moisture will result in SWD values lower or opposite of those with a positive lapse rate. In short, interpretation of this RGB as outlined in this blog post requires a positive low-level lapse rate.
Keep in mind this is an experimental multispectral imaging technique. At the time of this post’s creation, it hasn’t undergone extensive research and application to prove its usefulness and find common pitfalls. Additionally, it may not be optimized for different regions, nor at different times of the year which may have implications on overall moisture content. For example, the SWD range may need to be adjusted during the peak Spring/Summer convective season to account for greater (negative) SWD values.
If you happen to find any interesting or notable uses of the Moisture Gradient Convective RGB Sandwich, as well as any pitfalls, please comment or reach out to either of the authors.
Carl Jones (NWS Grand Forks), with input from Bill Line (NESDIS/STAR)