Ozone and the airmass RGB

December 13th, 2021 |
GOES_17 airmass RGB, 2200 UTC on 12 December 2021 (Click to enlarge)

A GOES-17 airmass RGB, above, shows a strong feature in the Gulf of Alaska. It’s common to associate the orange and purple regions within that polar feature (that is accompanied by cloud features consistent with very cold air aloft) with enhanced ozone. What products are available online to gauge the amount of ozone?

The OMPS instrument on board NOAA-20 (and on Suomi-NPP) senses in the ultraviolet (from 250-310 nm) to compute ozone concentration. (For more information on OMPS, refer to this document) The figure below, taken from this Finnish website, shows ozone concentration for the 24 hours ending at 0110 UTC on 13 December. A distinct maximum is apparent over the Gulf of Alaska. Note the northern terminus of the observations that are related to the time of year: there is little Sun north of 60 N. The data for this were downloaded from the Direct Broadcast site at GINA at the University of Alaska-Fairbanks. OMPS data are also available (from Suomi-NPP) at NASA Worldview.

To determine the time of the data in the image below, consult the NOAA-20 orbital paths here. This image (from that site) shows a NOAA-20 ascending overpass between 2235 and 2245 UTC over the Gulf of Alaska.

Daily Composite of Ozone concentration for the 24 hours ending 0111 UTC on 13 December 2021 (click to enlarge)

NOAA-20 also carries the Cross-track Infrared Sounder (CrIS) and Advanced Technology Microwave Sounder (ATMS) instruments that are used to create NUCAPS vertical profiles; one of the trace gases retrieved in this way is ozone. The distribution of ozone (with values in regions where it was dark) from NUCAPS is shown below (from this website maintained by SPoRT), and it corresponds roughly with the OMPS estimates shown above.

Gridded NUCAPS estimates of ozone, 2217 UTC on 12 December 2021 (Click to enlarge)

Conclusion: The assumption that upper-tropospheric ozone values are large in regions where the airmass RGB is tinted red or purple is a good assumption, especially if other structures in the RGB — such as cumulus cloud development in the cold air — reinforce the idea that an intrusion of stratospheric air is occurring. The strong storm that this lowered tropopause is supporting is accompanied by a moist feed of air moving into central California, as shown below by MIMIC total precipitable water fields.

Total Precipitable Water, 2200 UTC on 12 December 2021 (Click to enlarge)

Gridded NUCAPS fields are being tested within RealEarth, as shown below. They should be generally available soon.

RealEarth Gridded NUCAPS estimates of ozone, 2217 UTC on 12 December 2021 (Click to enlarge)

ASCAT winds versus SAR winds

November 22nd, 2021 |
ASCAT winds from Metop-B, 0616 and 1826 on 22 November 2021 (Click to enlarge)

From an email came this question: RADARSAT vs ASCAT winds, what are the differences between the two methods?

This comparison is not easy to make directly, as the orbits of Metop-B and Metop-C, the two satellites that carry the ASCAT instrument (now that Metop-A, which satellite also carried ASCAT, has been decomissioned), don’t sample the ocean at the same time/location as RADARSAT. The toggle above shows ASCAT winds from Metop-B (Metop-B orbits on 22 November 2021 are here, from this website) at 0631 and 1826 UTC on 22 November (from this source) in the region around Haida Gwaii (once known as the Queen Charlotte Islands). An obvious frontal passage occurred between those two times; this is also shown in the animation of surface charts (every 3 hours from 0600 through 1500 UTC shown here).

Imagery below shows SAR winds from RCM3 and RCM1 (RCM = RADARSAT Constellation Mission) at 02:23 (top) and around 15:03 (bottom) UTC on 22 November. The 15:03:52 image that follows the two images at bottom is here.

RCM3 RADARSAT SAR winds at 02:23 UTC on 22 November 2021 (Click to enlarge)
RCM1 RADARSAT SAR winds, 15:02:50 – 15:03:13 on 22 November 2021 (Click to enlarge)

How does scatterometery measure winds? If wind speeds over the ocean (or a lake) are very light, the water surface will be smooth. Microwave energy from a side-looking radar (ASCAT and SAR are both active radars; that is, they emit a ping and listen for a response) will reflect off it, and not scatter back to the instrument. As winds increase, small ripples develop and backscatter increases. Backscattered energy is greatest if the radar look and the wind direction are aligned; also, the backscatter is greater if the wind is blowing towards (vs. away from) the satellite. This is a source of ambiguity in direction. The backscatter distribution sensed has a name: Normalized Radar Cross-Section (NRCS); many different wind speed/direction combinations can produce the same NRCS. How can you mitigate these ambiguities?

For ASCAT instruments, the ambiguity is reduced through multiple measurements of the same surface — this gives NRCS values with different aspect and incident angles. Multiple measurements are achieved via the multiple antennas that are part of the ASCAT instrument (similarly, rotating beams on instruments such as AMSR-2 give multiple observations). Multiple observations allow for an accurate estimate of wind direction given the observations.

SAR processing mitigates the ambiguities by using numerical model output that suggests the correct wind direction. A challenge is that numerical model data has a far coarser resolution than SAR data. (Model data might also include errors!) As a result, artifacts can be introduced, and a good example is shown in the 02:23 UTC image above at 54.6 N, 131.8 W. In that region, where the windspeeds have an hourglass shape, the model wind direction is unlikely to be consistent with the observations. Keep that in mind when observing SAR winds.

One other aspect of the ASCAT v. SAR wind comparison bears notice: ASCAT winds have a typical upper bound, at around 45-50 knots. At stronger wind speeds, the backscatter to the ASCAT instrument is affected by foam on the sea surface that typically accompanies such strong winds. Special SAR wind processing (as discussed here) allows for observations of much stronger winds, as shown for 2020’s Hurricane Laura, where Seninel-1 SAR observations peaked at 150 knots! These computations use cross-polarization observations from SAR. Both SAR and ASCAT use co-polarization observations. Future ASCAT missions will support cross-polarization observations.

Some of the information above came from this link (specifically, here). If there are errors in the description, they’re this blogger’s fault however.

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:  ./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)