Applying enhancements to Geo2Grid imagery

July 3rd, 2020 |

GOES-16 ABI Band 13 (10.3 µm) Infrared Imagery from 1550 UTC on 2 July 2020, with color enhancement added, and with annotated colorbar (Click to enlarge)

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!

Actinoform clouds near Hawai’i

June 30th, 2020 |

GOES-17 “Red” Visible (0.64 µm) images [click to play animation | MP4]

GOES-17 “Red” Visible (0.64 µm) images [click to play animation | MP4]

GOES-17 (GOES-West) “Red” Visible (0.64 µm) images (above) revealed 3 cyclonically-rotating actinoform cloud structures that were moving west-southwestward toward the Hawaiian Islands on 30 June 2020 (surface analyses).

A closer look at the northernmost actinoform feature showed it moving over Buoy 51000 (located northeast of Hawai’i) around 04 UTC on 01 July — there was somewhat of an increase in 1-minute wind speeds and wind gusts as it approached, but no obvious perturbation was seen in the air pressure (it appeared to have arrived during the typical ~12-hourly drop in pressure).

GOES-17 “Red” Visible (0.64 µm) images [click to play animation | MP4]

GOES-17 “Red” Visible (0.64 µm) images [click to play animation | MP4]

A sequence of 3 hourly (at 0010, 0110 and 0210 UTC) panoramic camera views from Buoy 51000 (below) suggested that there were rain showers reaching the ocean surface beneath one of the actinoform’s radial arms at 0210 UTC (GOES-17 Visible image).

Sequence of 3 hourly (at 0010, 0110 and 0210 UTC) panoramic camera views from Buoy 51000 [click to enlarge]

Sequence of 3 hourly panoramic camera views from Buoy 51000, at 0010, 0110 and 0210 UTC [click to enlarge]

True Color Red-Green-Blue (RGB) and Infrared Window (11.45 µm) VIIRS images from NOAA-20 and Suomi NPP as visualized using RealEarth (below) provided a detailed view of 2 of the actinoform clouds. The radial arms that comprised the cloud features remained within the marine boundary layer, so they exhibited fairly warm cloud-top infrared brightness temperatures.

True Color RGB and Infrared Window (11.45 µm) images from NOAA-20 and Suomi NPP [click to enlarge]

True Color RGB and Infrared Window (11.45 µm) images from NOAA-20 and Suomi NPP [click to enlarge]

Plots of rawinsonde data from Hilo, Hawai’i (below) indicated that the marine boundary layer was strongly capped by a temperature inversion at an altitude of 1.3-1.5 km (where the air temperature was around +15ºC — which was very close to the minimum cloud-top infrared brightness temperatures exhibited by the actinoform clouds).

Plots of rawinsonde data from Hilo, Hawai'i [click to enlarge]

Plots of rawinsonde data from Hilo, Hawai’i [click to enlarge]

Other examples of actinoform clouds have been shown in May 2019, March 2008, March 2007 and June 1997.

Exploring the effects of GOES-17 parallax over Alaska

June 27th, 2020 |

GOES-17 “Red” Visible (0.64 µm) and

Topography, GOES-17 “Red” Visible (0.64 µm) and “Clean” Infrared Window (10.35 µm) images [click to play animation | MP4]

GOES-17 (GOES-West) “Red” Visible (0.64 µm) and “Clean” Infrared Window (10.35 µm) images (above) displayed the formation of an orographic rotor cloud downwind (north-northeast) of the Kigluaik and Bendeleben Mountains in the Seward Peninsula of Alaska on 27 June 2020. Even though the highest terrain in those mountain ranges was only 3700-4700 feet (1.1-1.4 km), the coldest cloud-top infrared brightness temperatures within the rotor cloud feature were around -50 to -51ºC.

A plot of rawinsonde data from Nome (below) showed the strong southwesterly winds that existed within most the troposphere on that day. The tropopause temperatures were around -51ºC at altitudes of 9.4-9.6 km — indicating that these high-altitude rotor clouds were forced by vertically-propagating waves initiated by interaction of the anomalously-strong southerly/southwesterly lower-tropospheric flow with the west-to-east oriented mountain ranges.

Plot of rawinsonde data from Nome, Alaska [click to enlarge]

Plot of rawinsonde data from Nome, Alaska [click to enlarge]

Comparisons of topography and Visible/Infrared images from Suomi NPP and GOES-17 around 1320 UTC and 2140 UTC are shown below. Since there is generally very little parallax offset associated with imagery from polar-orbiting satellites (such as Suomi NPP), the rotor cloud appeared closer to the topography that helped to force development of that cloud feature.

Topography, Suomi NPP VIIRS Visible (0.64 µm) and GOES-17 "Red" Visible (0.64 µm) images around 1320 UTC [click to enlarge]

Topography, Suomi NPP VIIRS Visible (0.64 µm) and GOES-17 “Red” Visible (0.64 µm) images around 1320 UTC [click to enlarge]

Topography, Suomi NPP VIIRS Infrared Window (11.45 µm) and GOES-17 "Clean" Infrared Window (10.35 µm) images around 1320 UTC [click to enlarge]

Topography, Suomi NPP VIIRS Infrared Window (11.45 µm) and GOES-17 “Clean” Infrared Window (10.35 µm) images around 1320 UTC [click to enlarge]

Topography, Suomi NPP VIIRS Visible (0.64 µm) and GOES-17 "Red" Visible (0.64 µm) images around 2140 UTC [click to enlarge]

Topography, Suomi NPP VIIRS Visible (0.64 µm) and GOES-17 “Red” Visible (0.64 µm) images around 2140 UTC [click to enlarge]

Topography, Suomi NPP VIIRS Infrared Window (11.45 µm) and GOES-17 "Clean" Infrared Window (10.35 µm) images around 2140 UTC [click to enlarge]

Topography, Suomi NPP VIIRS Infrared Window (11.45 µm) and GOES-17 “Clean” Infrared Window (10.35 µm) images around 2140 UTC [click to enlarge]

Plots of GOES-17 parallax correction vectors and displacements (in km) for a 30,00-feet (9.1 km) cloud feature at select points over the Alaska region (from this site) are shown below. For such a cloud feature over the Seward Peninsula, the parallax offset would be about 40 km (25 miles) — which closely corresponded to the offset seen between the GOES-17 and Suomi NPP images shown above.

Plots of GOES-17 parallax correction vectors and displacements (in km) for a 30,000-foot (9.1 km) cloud feature at select points over the Alaska region [click to enlarge]

Plots of GOES-17 parallax correction vectors and displacements (in km) for a 30,000-foot (9.1 km) cloud feature at select points over the Alaska region [click to enlarge]

Thunderstorms affecting American Samoa

June 20th, 2020 |

VIIRS Infrared Window (11.45 µm) images from NOAA-20 and Suomi NPP [click to enlarge]

VIIRS Infrared Window (11.45 µm) images from NOAA-20 and Suomi NPP [click to enlarge]

VIIRS Infrared Window (11.45 µm) images from NOAA-20 and Suomi NPP as viewed using RealEarth (above) showed thunderstorms beginning to increase in areal coverage north of and in the vicinity of American Samoa on 20 June 2020, as a surface trough north of the islands began to move southward.

To monitor the flash flooding potential of these thunderstorms, a GOES-17 (GOES-West) Mesoscale Domain Sector was positioned over the American Samoa region — which provided “Clean” Infrared Window (10.35 µm) images at 1-minute intervals (below). Some of these storms exhibited minimum cloud-top infrared brightness temperatures around -80ºC (black enhancement), producing heavy rainfall (over 6 inches in 9 hours) and strong winds (gusting to 60 mph) according to the NWS Pago Pago compilation of local storm reports.

GOES-17 "Clean" Infrared Window (10.35 µm) images [click to play animation | MP4]

GOES-17 “Clean” Infrared Window (10.35 µm) images [click to play animation | MP4]