GOES-16 (GOES-East) True Color Red-Green-Blue (RGB) images created using Geo2Grid (above) showed plumes of blowing dust originating from dry river beds along portions of the Río Grande O Guapay and Río Parapetí in southern Bolivia on 04 July 2020. Strong northerly winds developed across that region, just east of the axis of a deepening trough of low pressure.

VIIRS True Color RGB images from Suomi NPP and NOAA-20 as visualized using RealEarth (below) provided a more detailed view at the blowing dust plumes.

A plot of surface report data from Viro Viru International Airport, Santa Cruz de la Sierra — located not far to the north of the blowing dust plumes — showed northerly winds gusting as high as 36 knots (41 mph) at 20 UTC (below).

Thanks to Santiago Gassó for pointing out these dust features.

Dust activity yesterday at sunset in Bolivia pic.twitter.com/WmgaRRuVX4

— Santiago Gassó (@SanGasso) July 5, 2020

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The animation above shows GOES-16 visible imagery (Band 2, 0.64 µm) and stability indices. Initially an obvious gradient in stability is present where isolated convection is developing over southwestern Wisconsin. As the gradient relaxes, the convection dissipates even though instability is, in general increasing. A radar animation (clipped from RealEarth) is shown below.  The showers have largely dissipated over southwestern Wisconsin by 1700 UTC.

The absence of gradients in the derived stability product can often mean that convection will not initiate.  In this case, the relaxation of the gradient went hand-in-hand with the dissipation of convection.

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geo2grid (see this recent blog post) is a handy software package that allows anyone to create useful satellite imagery. Recent blog posts that document how to use geo2grid have shown visible or red-green-blue (RGB) composites. Individual infrared bands can also provide useful information, and this blog posts describes how you can create infrared imagery to which a useful color enhancement has been applied.

Step 1, as always, is to find data. NOAA CLASS has archived Level-1b Radiance files; these are the data that geo2grid expects, and the naming convention of the files is also what geo2grid expects. Amazon Cloud Services can also offer correctly-named data. (This blogger is aware of some users who have retrieved data from Google, and those files did not follow the expected naming convention).

The animation above compares an infrared image from 1550 UTC on 2 July over the northern part of the United States. The grid is described in this blog post and was used for convenience. (Of some importance: The grid created has dimensions of 1500×800). The original grey-scaled image is shown (with coastlines overlain), then a color enhancement is applied, and finally a script is run to annotate the colorbar.

The geo2grid command used to start the image creation was:

./geo2grid.sh -r abi_l1b -w geotiff -p C13 -g PADERECHO --grid-configs $GEO2GRID_HOME/PADERECHO.conf --method nearest -f /arcdata/goes_restricted/grb/goes16/2020/2020_07_02_184/abi/L1b/RadF/OR_ABI-L1b-RadF-M6C13_G16_s20201841550211_e20201841559531_c20201841600013.nc

This creates a Band 13 (the -p C13 flag) grey-scaled geotiff (-w geotiff) from data at the archive site specified ( -f /arcdata/goes_restricted/..../OR_ABI-L1b-RadF-M6C13_G16_s20201841550211_e20201841559531_c20201841600013.nc ), reading Advanced Baseline Imagery Level-1b imagery (the -r abi_l1b flag), and using nearest-neighbor interpolation to a pre-defined grid (“PADERECHO”) to transform the projection from the default satellite projection (That’s this part of the command: -g PADERECHO --grid-configs $GEO2GRID_HOME/PADERECHO.conf --method nearest). Note that by default, the geotiff created is 8-bit (in contrast to the 11-bit imagery; some information is lost.) You can create geotiffs with full bit depth. For this example an 8-bit image is being created.

The add_colorbar shell applies a color enhancement to the grey-scaled geotiff image that geo2grid creates. For the image in this blog post, I used this invocation:

./add_colormap.sh ../../enhancements/IR4AVHRR100_colortable.txt GOES-16_ABI_RadF_C13_20200702_155021_PADERECHO.tif

This colorbar must be pre-defined by the user. The format of the colortable (“IR4AVHRR100_colortable.txt”) is a comma-separated list of greyscale values (0-255 for the 8-bit geotiff that is created) followed by Red, Green, Blue and Transparency values. (Documentation is here. The file used for this example is here — courtesy of Jim Nelson, CIMSS) The colortable is applied to the geotiff image and a separate image (.png in this case) is created. You will note that this bi-linear enhancement is various shades of grey for bit values 0-152, then different colors for values 153-255.

How do you associate the color with a temperature? For a bi-linear enhancement as used for Band 13, this is not simple. With knowledge of the calibration used, or with access to a system (such as McIDAS-X or McIDAS-V) that includes the calibration, it is possible to associate the greyscale values with temperature values and then use annotation software such as ImageMagick to write in values, with a command such as:

convert INPUTFIELD_with_Coastline_Colorbar_Enhanced.png -fill white -pointsize 24 -annotate +997+28 "-30" InputField_with_CoastLine_Colorbar_enhanced_-30Label.png

This must be done for each label you want to add to the image, but a shell script does this easily, and once done, you have something that you can use for any image that is the same size: 1500 pixels wide. The spacing between the -30ºC, -40ºC, -50ºC… labels on the color bar is constant in this example. Thus, once two values are known and placed, it’s simple to compute the offsets for the other temperature values.

Polar2Grid colorbar functionality is a bit more comprehensive than Geo2Grid. When a colorbar is created in Polar2Grid, metadata on the minimum and maximum values are inserted into the geotiff. This allows automatic generation of colorbar labels (for a linear scaling only) as shown in this excellent example of documentation!

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Advanced Clear-Sky Processing of Oceans (ACSPO) Sea Surface Temperatures (SSTs or, in this case, Lake Surface Temperatures, LSTs) are available via an LDM feed from CIMSS for insertion into AWIPS.  Clear skies over the lower Great Lakes (Erie and Ontario) for the morning overpasses of Suomi NPP (orbit paths, from this site) and NOAA-20 (orbit paths, from this site), toggled above, allow for an accurate depiction of lake surface temperatures.  Warmest Lake Erie temperatures are in the western part of the lake (mid-70s), with low 70s along the southern shore of the lake.  Surface temperatures are in the upper 60s on the Ontario side of the lake, with the exception of warm temperatures in Long Point Bay.

In Lake Ontario, temperatures are warmest over the southwest part of the Lake (almost 70!) and coolest along the north shore (low/mid 60s).  Note that the region that is warmest does have a different signal in the True Color imagery, below, cropped from an experimental CIMSS website (VIIRS Today).

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