SIFT investigations of an EF-3 tornado that hit Boscobel WI

August 13th, 2021 |
GOES-16 ABI Band 13 (“Clean Window”) Infrared imagery (10.3 µm), 2100-2159 UTC on 7 August 2021 (Click to animate)

An EF-3 tornado moved through the southwest Wisconsin town of Boscobel, in Grant County, late in the afternoon of 7 August 2021 (Preliminary Storm Summary from WFO ARX). The tornado was on the ground from 4:29 to 4:56 PM CDT, or 2129 – 2156 UTC. How did the ABI imagery and GLM data change over this time? The Satellite Information Familiarization Tool (SIFT) can be used to investigate this. Gridded GLM data that can be imported into SIFT (a two-week rolling archive is available) is available at this website. ABI Radiance data can be acquired from NOAA CLASS or from the Amazon Cloud.

The GOES-16 ABI Clean Window animation from 2100 to 2159 UTC, bracketing the times that the tornado, linked to the image above, shows very strong upper-level difluence (consider how the cirrus shield spreads south in the hour of the animation!); one might infer cyclonic motion in the fields as well.

SIFT allows for the identification of regions that can then be investigated. The toggle below shows a polygon that has been defined. Subsequent plots will focus on this region surrounding the storm tops associated with the tornadic storm.

SIFT display of GOES-16 Clean Window (10.3 µm) at 2124 and 2157 UTC on 7 August 2021. The transparent red box defines a region being investigated.

How do the cloud-top brightness temperatures evolve in that region? One way to describe that is a simple bar-graph showing the distribution of temperatures, shown below. There are three distinct cold temperature events: around 2130 UTC, around 2138 UTC, around 2148 UTC. (Recall the tornado is on the ground fron 2129-2156) The time-scale of the changes is such that only 1-minute imagery will be able to capture it accurately.

Distribution of 10.3 µm brightness temperatures within a defined polygon as shown above; 2124-2159 UTC on 7 August 2021

How do the lightning observations evolve in the storm? SIFT will display many different GLM parameters: Average and Minimum Flash Areas, Total Energy, Group (and Flash) Extent and Centroid Densities, Group and Flash Areas. Some are displayed below, again within the confines of the polygon defined above. The first plot compares Average Flash Area (along a constant x axis) and Total Optical Energy (along a varying y axis). The distribution in the plot seems to change during the time when the tornado is on the ground.

GLM Average Flash Area v. GLM Total Energy within the defined polygon, 2124, 2127, 2134, 2140, 2149 and 2151 UTC.

SIFT also allows direct comparisons between ABI and GLM data, as shown below: Flash Extent Density is compared to Band 13 (10.3 µm) brightness temperatures at discrete times within the tornado’s lifecycle.

GLM Flash Extent Density vs. G16 ABI Band 13 (10.3 µm) Brightness Temperature within a predefined polygon, 2124, 2127, 2134, 2140, 2149, 2151 UTC

For more information on SIFT, including download instructions for linux, MacOS and Windows, refer to the SIFT website.

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:  ./ -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:  ./ -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.

../ CIRRUSRGBtest -88.3 26.6 500 -500 960 720 > $GEO2GRID_HOME/CIRRUSRGBtest.conf
../ -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-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)

Creating and Displaying gridded GLM fields using data from NOAA CLASS

March 23rd, 2021 |

GOES-16 Gridded GLM imagery of Total Optical Energy for the 1 minute ending 1501 UTC on 22 March 2021 (Click to enlarge)

This blog posts describes how to use NOAA’S CLASS (Comprehensive Large Array-data Stewardship System) system (link) that contains Level-2 GLM data, (under the GOES-R Series GLM L2+ Data Product (GRGLMPROD) tab) to create useable GLM imagery. GLM processing produces three Level 2 files each minute, and those files can be processed to produce imagery. First, choose the time range you want in CLASS, and get the global imagery.  For this blog post, I chose GOES-16 data on 22 March 2021 between 15:00 an 15:15 UTC.  On the CLASS website, I clicked the GLM L2+ Lightning Detection Data and didn’t filter by any values (CLASS allows you to filter by minimum/maximum flash, event and group counts, if you want).  This request returned 47 different files, but that is only about 10 Mbytes.  Some of the file names — two minutes’ worth — are shown below: LCFA files from julian Day 081 (that is, 3/22/2021) starting at 15:00:00:00, 15:00:00:20, 15:00:00:40, … etc.

Code to convert these files (that contain raw-ish group, event and flash fields) to gridded GLM fields (that can be displayed with, for example, Geo2Grid, or AWIPS) is within the CSPP Gridded GLM software package that can be downloaded here (free registration may be required; the Gridded GLM tarball to download includes a short and useful README). To create a data file that is properly configured for Geo2Grid (or AWIPS), with software that uses the open-source glmtools software developed by Dr. Eric Bruning at Texas Tech, use this command: ../../data/OR_GLM-L2-LCFA_G16_s20210811501*

That will create a file with a name like this:;

Geo2Grid can then be used to create imagery from the newly-created netCDF file. The Geo2Grid code used is below.

../ TestGridded -75.0 8. 1000 -1000 2000 750 > $GEO2GRID_HOME/TestGridded.conf
../ -r glm_l2 -w geotiff -p total_energy -g TestGridded --grid-configs $GEO2GRID_HOME/TestGridded.conf --method nearest -f /home/scottl/CSPPGeo/GGLM/cspp-geo-gridded-glm-1.0b1/bin/
../ ../../../enhancements/TotalEnergy.txt GOES-16_GLM_total_energy_20210322_150100_TestGridded.tif
../ --add-coastlines --coastlines-resolution=h --coastlines-outline='black' --add-grid --grid-text-size 12 --grid-d 1.0 1.0 --grid-D 1.0 1.0 --add-colorbar --colorbar-tick-marks 250.0 --colorbar-text-size 1 --colorbar-no-ticks --colorbar-align bottom GOES-16_GLM_total_energy_20210322_150100_TestGridded.tif
convert GOES-16_GLM_total_energy_20210322_150100_TestGridded.png -gravity Southwest -fill white -pointsize 24 -annotate +8+30 "1501 UTC 22 March 2021 Total Energy" GOES-16_GLM_total_energy_20210322_1501_Labeled.png

The Geo2Grid package commands above (1) created the grid (‘TestGridded’) onto which the data were interpolated; (2) created the imagery from the netCDF file output from the Gridded GLM package; (3) Added a pre-defined colormap (within ‘TotalEnergy.txt’); (4) Added coastlines, a lat/lon grid, and a colorbar and (5) annotated the image. This last command used ImageMagick.

Note that the GLM image created, shown at top, is mostly transparent. Three areas of GLM observations are apparent, two over South America, one over the Pacific Ocean south of Panama. The transparency is handy if you want to overlay GLM data on top of ABI imagery!

Using NUCAPS lapse rates to evaluate atmospheric stability

February 26th, 2021 |

GOES-17 Visible Imagery (2300 UTC), NOAA-20 NUCAPS-derived lapse rate (925 – 700 mb, 23:03 UTC) and NUCAPS sounding points (2249 UTC) on 25 February 2021 (Click to enlarge)

NUCAPS profiles derived from CrIS and ATMS data on NOAA-20 provide model-independent estimates of atmospheric thermodynamics globally, including, for this case over the central Pacific Ocean, in regions otherwise bereft of data.  NUCAPS lapse rates show a minimum in stability in low-levels in between two cloud features; the region includes mostly ‘green’ NUCAPS retrieval points:  where infrared and microwave retrievals have both converged.  It is difficult in the case above to relate differences in cloud features to differences in the diagnosed stability.

Four minutes later (shown below), NOAA-20 was closer to the Pole on this ascending pass and the diagnosed stability does relate well to differences in cloud structures.  In particular, the change from lapse rates around 5 C/km northeast of Hawai’i to lapse rate closer to 2 or 3 C/km even farther northeast aligns with a boundary between cloud types.

GOES-17 Visible Imagery (2310 UTC), NOAA-20 NUCAPS-derived lapse rate (925 – 700 mb, 23:07 UTC) and NUCAPS sounding points (2249 UTC) on 25 February 2021 (Click to enlarge)

The subsequent NOAA-20 pass was west of the main Hawai’ian Island chain.  Again, differences in lapse rates are related to cloud features in the visible imagery.  Stable air — with lapse rates between 3 and 4 C/km — overlies a region of very little cumuliform development.  A region of larger lapse rates over the eastern 1/3rd of the pass, just to the west of the Hawai’ian Islands is accompanied by cumulus development.  NUCAPS thermodynamic fields, even though they have limited resolution in the vertical (at most 10 layers in the enter tropopause), can give useful information on stability over the ocean that can help in the real-time diagnosis of the atmosphere.