Forecasting lightning

July 15th, 2021 |

Lightning safety is important for aircraft, mariners, and many outdoor activities. CIMSS is working to evaluate a model that nowcasts lightning. This model was trained using GOES-16 ABI visible, near-infrared, and long-wave infrared channels, as well as GOES-16 Geostationary Lightning Mapper (GLM) observations. It predicts the probability of lightning (IC or CG, as observed by GLM) in the next 60 minutes at any given point. The model routinely provides lead-time to lightning initiation of 20 minutes or more. We’re hopeful that one day such a model will help forecasters provide guidance for aviators, mariners, and decision support services (DSS) for things like sporting events, festivals, and theme parks. Near-real-time model output can be viewed using SSEC’s RealEarth.

Below are a few examples, with the forecast lightning probability contoured over the daytime cloud phase RGB and GOES-16 GLM flash-extent density.

So this summer, whether you’re going to the South Carolina beach,

or sailing in the Gulf of Maine,

or hiking in the Rocky Mountains,

or catching the first MLB game in Iowa,

be on the lookout for lightning!

Pyrocumulonimbus cloud produced by the Bootleg Fire in Oregon

July 14th, 2021 |

GOES-17 "Red" Visible (0.64 µm, top left), Shortwave Infrared (3.9 µm, top right), "Clean" Infrared Window (10.35 µm, bottom left) and Fire Temperature RGB (bottom right) [click to play animation | MP4]

GOES-17 “Red” Visible (0.64 µm, top left), Shortwave Infrared (3.9 µm, top right), “Clean” Infrared Window (10.35 µm, bottom left) and Fire Temperature RGB (bottom right) [click to play animation | MP4]

1-minute Mesoscale Domain Sector GOES-17 (GOES-West) “Red” Visible (0.64 µm), Shortwave Infrared (3.9 µm), “Clean” Infrared Window (10.35 µm) and Fire Temperature RGB images (above) revealed that the Bootleg Fire in far southern Oregon produced a pyrocumulonimbus (pyroCb) cloud — denoted by cloud-top 10.35 µm infrared brightness temperatures of -40ºC and colder (darker blue pixels) — late in the day on 14 July 2021. The maximum surface 3.9 µm brightness temperature sensed with this fire was 138.7ºC — which is the saturation temperature for the ABI Band 7 detectors.

A plot of 00 UTC rawinsonde data from nearby Medford, Oregon (below) indicated that the -40ºC temperature closely corresponded to the height of the tropopause and the Most Unstable (MU) air parcel Equilibrium Level (EL).

Plot of 00 UTC rawinsonde data from Medford, Oregon [click to enlarge]

Plot of 00 UTC rawinsonde data from Medford, Oregon [click to enlarge]

On the following morning, signatures of the upper-tropospheric/lower-stratospheric smoke that was forced aloft by the pyroCb cloud could be seen arcing east-southeastward over parts of Idaho, Montana and Wyoming on GOES-17 Visible and Near-Infrared “Cirrus” (1.37 µm) images (below). The smoke signature in 1.37 µm images was not due to the plume being composed of ice particles; rather, the Cirrus imagery is also able to highlight the presence of particles that are highly effective at scattering sunlight (which includes ice crystals, dust, volcanic ash, smoke) — and the smoke signature was also further highlighted by a favorable forward scattering angle.

GOES-17 "Red" Visible (0.64 µm, top) and Near-Infrared "Cirrus" (1.37 µm, bottom) images [click to play animation | MP4]

GOES-17 “Red” Visible (0.64 µm, top) and Near-Infrared “Cirrus” (1.37 µm, bottom) images [click to play animation | MP4]

Thunderstorms over the Chukchi Sea and Beaufort Sea north of Alaska

July 12th, 2021 |

Suomi NPP VIIRS Infrared Window (11.45 µm) and Visible (0.64 µm) images [click to play animation | MP4]

Suomi NPP VIIRS Infrared Window (11.45 µm) and Visible (0.64 µm) images [click to play animation | MP4]

A sequence of Suomi NPP VIIRS Infrared Window (11.45 µm) and Visible (0.64 µm) images (above) showed snapshots of thunderstorms over parts of the Chukchi Sea and the Beaufort Sea off the northern coast of Alaska on 12 July 2021. The coldest convective cloud-top infrared brightness temperatures were in the -30 to -40ºC range. Unusual aspects of these thunderstorms included their high latitude location over ice-covered waters — as far north as 75ºN latitude — and the large amount of cloud-to-surface lightning strikes that they produced.



These thunderstorms were not surface-based — instead, they were forced by an approaching cold front (surface analyses) which helped to release elevated instability within the 500-300 hPa layer (below).

Suomi NPP VIIRS Infrared Window (11.45 µm) images, with contours of NAM40 lapse rate within the 500-300 hPa layer [click to enlarge]

Suomi NPP VIIRS Infrared Window (11.45 µm) images, with contours of NAM40 lapse rate within the 500-300 hPa layer [click to enlarge]

Rawnsonde data from Utqiagvik (PABR) were not available (due to ongoing equipment malfunction at that site) — but a NUCAPS profile near the southernmost cluster of convection around 15 UTC (below) showed the layer of instability aloft.

NUCAPS profile near thunderstorms off the northern coast of Alaska [click to enlarge]

NUCAPS profile near thunderstorms off the northern coast of Alaska [click to enlarge]

Creating RGB imagery using SIFT and Geo2Grid

July 8th, 2021 |

The use of routine multispectral geostationary satellite imagery over the United States has increased the routine use of Red/Green/Blue composite imagery to describe and evaluate surface and atmospheric conditions. This blog post will detail how to create new (or old) RGB composites using two UW-Madison/CIMSS/SSEC-developed tools: The Satellite Information and Familiarization Tool (SIFT; Journal article link) and Geo2Grid (Previous blog posts showing Geo2Grid examples are here). The scene to be highlighted is shown above in the GOES-16 Cirrus Band; it was chosen because of the interesting parallel bands in the Cirrus, features that can identify regions of turbulence. A larger-scale view of the data (created using CSPP Geosphere) is here (for the 1.37 µm Cirrus band) or here (for True Color).

SIFT has a very useful (and easy!) RGB generator.  For this case involving cirrus, I decided to create an RGB using the Split Window Difference (10.3 µm – 12.3 µm, Band 13 – Band 15) (shown here) that has been used to identify cirrus for quite a while (link to journal article), the cirrus band 4, and also the Snow/Ice channel Band 5 (1.61 µm).  After downloading SIFT and importing the data (and creating the split window difference field — here’s a blog post that describes how to do that), a SIFT user can create an RGB and tinker with the bounds.  Changing the bounds and the gamma causes a simultaneous change in the RGB in the SIFT display window, so it’s not difficult to iterate to a satisfactory solution.  As shown below, the RGB created has the Split Window Difference as the red component, with values from 0 (no red) to 12.0 (saturated red) and a Gamma value of 2;  the cirrus channel (C04) is the green component with values from 0.27 (no green) to 0 (saturated green) and a Gamma value of 2;  the snow/ice channel (C05) is the blue component with values from 0.0 (no blue) to 0.40 (saturated blue) and a Gamma value of 1.

SIFT RGB Creation window

The RGB created in SIFT using these values is shown below.  Maybe using maximum green — a color one’s eyes are usually particularly adept at viewing — for no signal in the cirrus channel was not the best choice.  But there is nice contrast between the background and the thin cirrus, and an obvious difference between the parallel lines of cirrus in the middle of the image and other clouds, such as the cirrus at the western edge of the image!

“Cirrus” RGB at 1411 UTC on 8 July 2021 (click to enlarge)


How do you create something similar using Geo2Grid?  Step 1, of course, is always to download and install the software package.  To see what products can be created with geo2grid, enter this command:  ./geo2grid.sh -r abi_l1b -w geotiff --list-products -f /path/to/the/directory/holding/GOESR/Radiance/Files/*syyyydddhhmm*.nc .  Let’s assume all 16 channels from ABI are available.  Important caveat: Geo2Grid will only work on one data time at a time, so specify your year/julian day/hour/minute with sufficient stringency.

RGB product definitions are found in yaml files within the Geo2Grid directory. Ones for abi in particular are found in $GEO2GRID_HOME/etc/satpy/composites/abi.yaml in which file you would enter something what is shown below for a product called ‘cirrustest’;  note that it has three channels:  the first is a difference between C13 and C15 (that is, the Split Window Difference);  the second is C04 (cirrus channel) and the third is C05 (snow/ice channel). This is the same as in the SIFT definitions.

Within $GEO2GRID_HOME/etc/satpy/enhancements/abi.yaml there is a further definition of this RGB.  The crude stretch defines the bounds of the RGB:  Red includes values from 0 – 12;  Green from 27 — that is, a reflectance of 0.27, or 27% — to 0 (note that it is inverted);  Blue from 0 to 40.  In addition, Gamma values are specified:  0.5, 0.5 and 1.

Two important things to note:  Gamma in SIFT follows National Weather Service and JMA conventions.  Gamma in Geo2Grid follows EUMETSAT conventions. Thus, one is the reciprocal of the other.  Also, note the _abi suffix in the abi.yaml file name in enhancements, i.e., cirrustest_abi, to specify the satellite.

After making these changes to the two abi.yaml files, and rerunning this command:  ./geo2grid.sh -r abi_l1b -w geotiff --list-products -f /path/to/the/directory/holding/GOESR/Radiance/Files/*syyyydddhhmm*.nc, you should see a new possibility: cirrustest (or whatever you have named your new RGB). Then you run Geo2Grid commands to create the cirrustest RGB (with the -p cirrustest flag.  The commands below sequentially create the grid for the analysis, create the tiff file, georeference it with coastlines (none, in this case over the Gulf) and latitude/longitude lines, and annotate it.

../p2g_grid_helper.sh CIRRUSRGBtest -88.3 26.6 500 -500 960 720 > $GEO2GRID_HOME/CIRRUSRGBtest.conf
#
../geo2grid.sh -r abi_l1b -w geotiff -p cirrustest C04 -g CIRRUSRGBtest --grid-configs $GEO2GRID_HOME/CIRRUSRGBtest.conf --method nearest -f /arcdata/goes_restricted/grb/goes16/2021/2021_07_08_189/abi/L1b/RadC/*s20211891411*.nc
../add_coastlines.sh --add-borders --borders-outline='blue' --borders-resolution=f --add-grid --grid-text-size 20 --grid-d 5.0 5.0 --grid-D 5.0 5.0 GOES-16_ABI_RadC_cirrustest_20210708_1411??_CIRRUSRGBtest.tif
convert GOES-16_ABI_RadC_cirrustest_20210708_1411??_CIRRUSRGBtest.png -gravity Southwest -fill yellow -pointsize 14 -annotate +8+24 "1411 UTC 8 July 2021 Cirrus RGB" GOES-16_ABI_RadC_cirrustest_20210708_1411_CIRRUSRGBtest_annot_2.png

The final image from Geo2Grid is shown below. Its geographic coverage is slightly different than in SIFT, above, but the two RGBs have similar looks.

‘Cirrustest’ RGB at 1411 UTC on 8 July 2021 (Click to enlarge)