Starting the day of June 16, 2021, a plume of elevated smoke originating from the Robertson Draw fire in Montana began moving over the Dakotas. Day shift forecasters at the Grand Forks, ND, NWS office were briefed of this smoke moving towards their CWA, possibly impacting daytime temperatures and in turn, potential convection later in the afternoon along a cold fropa. With the expectation of smoke hindering the amount of insolation during peak heating hours, forecasters set out to track this smoke using GOES imagery, namely visible imagery from ABI’s channels 1 and 2, CIRA’s Geocolor RGB, as well as the GOES Aerosol Optical Depth product.

While plume guidance was available from the HRRR-smoke model, close observation of the elevated smoke using satellite imagery was performed to truly define the spatial bounds in an effort to set an observed trajectory. Given the expected absence of large changes within the mid level environment, a persistence forecast using the smoke’s observed trajectory was performed using the Distance Speed and Time of Arrival tools within AWIPS. Using both persistence trajectory forecast and model guidance, an assessment could be made to highlight the geographical areas that would most likely be under veil of elevated smoke during peak heating hours (roughly thought to be between 15 – 00 UTC during this time of year), thus indicating areas that have best chance of seeing some loss of insolation.

Armed with the knowledge of Channel 1’s superior aerosol detection, forecasters looked at this imagery to start the process of the smoke’s aerial extent and tracking. However, default colormap ranges within AWIPS made boundary definition of the smoke somewhat difficult during the low light hours of the morning, with little contrast between smoke and clear sky. Luckily, some minor colormap manipulation helped draw out the feature of interest, i.e. the area of smoke. The default minimum/maximum colormap ranges of 0-130 were edited to 10-30. Figure 1 shows the comparison between the default AWIPS range versus the constrained colormap range. While the edited range overexposed clouds within the area of smoke as well as underexposed land features, this was tolerable as it better revealed the spatial bounds of the smoke, from which trajectory analysis could made.

Figure 2 compares Channel 1 (0.47 um ‘blue’ visible band) to Channel 2 (0.64 um ‘red’ visible band), both of which have their colormap ranges edited to 10-30 and 5-30, respectively. Notice Channel 1’s superior ability to reveal the smoke compared to Channel 2. This is due to Channel 1’s sensitivity near the 0.47 um wavelength which can detect scattered solar radiation by atmospheric constituents better than Channel 2’s near 0.64 um wavelength, especially by larger aerosol particulates like dust, smoke, and pollutants. And perhaps the muted detail from land surface features (again, from Channel 1’s increased sensitivity to scattered radiation) as well as lower spatial resolution within Channel 1 compared to Channel 2 helps draw out the overall feature of interest, i.e. smoke.

The area of smoke was just within viewing range of GOES-East’s Aerosol Optical Depth product (AOD), too. This product helped increase confidence of the smoke’s extent near the leading and trailing edges, especially further into the day as the visual enhancement of the smoke due to additional scattering lessened with a decreasing solar zenith angle. Figure 3 shows the Aerosol Optical Depth product overlaid on top of range edited Channel 1. Confidence was placed in the presence of smoke for values of AOD generally larger than ~0.2-0.4 (blue-cyan colored). Note the masked data within AOD as flagged by things like clouds and GOES-East’s local zenith angle greater than 60 degrees, as well as others. Even portions of the smoke itself was masked by the product algorithm’s quality flags, however it was assumed smoke was still present between the edges of higher AOD as confirmed by Channel 1 imagery.

While colormap range manipulation during times of low solar light has shown operational relevance, it may be worth mentioning how changing the colormap’s grayscale may affect appearance of the smoke within visible imagery. The “ease” of feature identification can depend upon the observer, and is where subjective analysis of imagery can vary from person to person. Figure 4 displays a four panel animation of Channel 1 with four different colormap configurations: left panels = square root grayscale, right panels = linear grayscale, top panels = AWIPS default 0-130 ranges, bottom panels = range edited 10-30. Which colormap configuration can you most easily track the smoke? Does time of day influence your ability to observe the spatial bounds of smoke between the different colormaps? How about the ranges?

The CIRA Geocolor RGB also helped reveal the area of smoke in addition to differentiating smoke from clouds, as shown in Figure 5.

Ultimately, smoke and clouds did effect temperatures within portions of the Red River Valley into northwest Minnesota by limiting the amount of heating during this day, although it is hard to tell which may have had more of an impact, further compounded upon forecast accuracy of a changing lower level thermodynamic environment associated with thermal ridging and moisture advection ahead of a cold front. Nevertheless, here is an example showing Fargo only topping out at 88 F, whereas the interquartile and deterministic NBM forecasts were all higher. This also likely played a role in lessening convective potential along a cold fropa that afternoon making for a more effective capping inversion. The lack of insolation had a role in the removal of a marginal risk within an update to the Day 1 convective outlook collaborated between FGF and SPC.

Carl Jones (NWS Grand Forks) and Bill Line (NESDIS and CIRA)