Polar2Grid allows users to create true-color imagery from VIIRS (Visible Infrared Imaging Radiometer Suite) data from Suomi-NPP or NOAA-20. This tutorial will take you through the needed steps. Step one is to decide when you want the data; the ways to determine when a Polar Orbiter overflies a particular point are outlined in this blog post, that points to this website. For this blog post I’ve chosen Missouri. The image above shows a True-Color image over Missouri at about 19:42 UTC on 9 April 2019.

To create true-color imagery, Polar2Grid requires VIIRS M-Bands 3, 4 and 5 (Blue (0.48 µm), Green (0.55 µm) and Red (0.67 µm), respectively, all with 750-m resolution); click here for a list of all VIIRS bands). If the VIIRS I-Band 1 (at 0.64 µm) is present in the directory, then that image is used to sharpen the resultant image. Polar2Grid CREFL software also performs a simple atmospheric Rayleigh scattering removal; smoke and haze will still be apparent in the imagery, however.

To create the imagery above, first order the data from NOAA Class. (Steps to follow are shown here). Download the data into a unique directory. We are going to remap these data onto a map centered on Missouri, and for that to happen, Polar2Grid needs mapping parameters. These can be generated automatically with the p2_grid_helper.sh script that comes with Polar2Grid software. From the bin directory, I entered this command to put the grid parameters in a file .

/p2g_grid_helper.sh missouri -93.0 38.0 500 -500 2000 2000 > my_grids.txt

The line of data entered into that file is this:

missouri, proj4, +proj=lcc +datum=WGS84 +ellps=WGS84 +lat_0=38.000 +lat_1=38.000 +lon_0=-93.000 +units=m +no_defs, 2000, 2000, 500.000, -500.000, -99.055deg, 42.352deg

Now I’m ready to generate a true-color image (corrected ceflectance — crefl — imagery) with Polar2Grid, using this command:

./polar2grid.sh crefl gtiff –grid-configs /home/scottl/Polar2Grid/polar2grid_v_2_2_1/bin/my_grids.txt -g missouri -f /data-hdd/storage/Polar2GridData/09April/

The flags “–grid-configs <path to directory where file created by p2g_grid_help sits” and “-g map <name of map inside that file>” instruct to the Polar2Grid software to pull the mapping data for the defined grid out of the file. Otherwise, the data are in satellite projection. This polar2grid.sh invokation created a file named ‘j01_viirs_true_color_20190409_194226_missouri.tif’; I want to put a map on it so it is easier to georeference, and that is done using this shell in the Polar2Grid bin directory:

./add_coastlines.sh –add-borders –borders-resolution=f –borders-level=2 –borders-outline=’black’ j01_viirs_true_color_20190409_194226_missouri.tif

This adds a map to the image, then converts it to the png file (j01_viirs_true_color_20190409_194226_missouri.png) that is shown above.

After doing the same steps for a series of clear days in the midwest (09 March 2019, 15 March 2019, 21 March 2019, 26 March 2019, 31 March 2019), and annotating and concatenating the images in an animation, the greening up of Spring is apparent. See below.

Special shout-out to Dave Hoese, SSEC/CIMSS, for crafting software that is so easy to use to produce excellent satellite imagery.

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Strong winds — gusting as high as 77 mph in New Mexico and 88 mph in Texas — associated with a rapidly-intensifying midlatitude cyclone generated large plumes of blowing dust (originating from southeastern Arizona,southern New Mexico, northern Mexico and western Texas) on 10 April 2019. GOES-16 (GOES-East) Split Window (10.3-12.3 µm) images (above) helped to highlight the areas of blowing dust, which initially developed along and behind a cold front after 15 UTC.

GOES-16 Split Window images with hourly plots of surface wind barbs and gusts (above) showed the distribution of strong winds across the region, while plots of the surface visibility (below) showed decreases to 1/4 mile at Deming, New Mexico, 1/2 mile at Lubbock, Texas and 4 miles at Altus, Oklahoma.

GOES-16 True Color Red-Green-Blue (RGB) images (below; courtesy of Rick Kohrs, SSEC) depicted the blowing dust as shades of tan to light brown. Willcox Playa was the source of the dust plume coming from southeastern Arizona. Note that the dust plume emanating from White Sands, New Mexico was lighter in appearance compared to the other tan/brown-colored areas of blowing dust — this is due to the white gypsum sand that comprises the surface of White Sands National Monument.

250-meter resolution MODIS True Color RGB images from the MODIS Today site (below) provided a more detailed view of the plume streaming northeastward from its White Sands source. On the later Aqua image, dense tan-colored areas of blowing dust had developed below the thin higher-altitude veil of brighter gypsum aerosols that had earlier been lofted from White Sands.

A NOAA-20 True Color RGB image viewed using RealEarth is shown below. 19 UTC surface observations at 3 sites near White Sands included Las Cruces KLRU (visibility 3 miles, wind gusting to 46 knots), Alamogordo KALM (visibility 3 miles, wind gusting to 43 knots) and Ruidoso KSRR (visibility 5 miles, wind gusting to 55 knots). The strong winds and dense areas of blowing dust reducing surface visibility not only impacted ground transportation but also posed a hazard to aviation.

Dust and sandstorms create conditions of reduced visibility near the surface and like volcanic ash can create a hazard when ingested into engine intakes. In this case, the dust storm was widespread so the @NWSAWC issued a SIGMET for IFR conditions as a warning to pilots. pic.twitter.com/QOpe1pIs9b

— Scott Dennstaedt (@AvWxWorkshops) April 11, 2019

===== 11 April Update =====

In a larger-scale view of GOES-16 Split Window images (below), the yellow dust signature could be followed during the subsequent overnight hours and into the following day on 11 April, as the aerosols were being transported northeastward across the Upper Midwest. There were widespread reports and photos of dust residue on vehicles and tan/brown-colored snow in parts of Nebraska, Iowa, Minnesota and Wisconsin.

IDEA forward trajectories (below) — initialized from a cluster of elevated Aura OMI Aerosol Index points over Mexico, New Mexico and Texas — passed directly over areas of model-derived precipitation across the Upper Midwest, providing further support of precipitation scavenging of dust aerosols. Interestingly, a similar event of long range dust transport occurred on 10-11 April 2008.

HYSPLIT model 24-hour forward trajectories initialized at 3 locations — El Paso, Lubbock and Amarillo in Texas — showed a few of the likely dust transport pathways toward the Upper Midwest at 3 different levels (below).

GOES-16 True Color RGB images from the AOS site (below) showed that some clouds across the Upper Midwest exhibited a subtle light brown hue at times.

===== 12 April Update =====

GOES-16 Split Window (10.3-12.3 µm) images (above) showed that the yellow signature of dust aerosols aloft had wrapped all the way around the southern and eastern sectors of the occluded low on 12 April.

Ground-based lidar at the University of Wisconsin – Madison confirmed the presence of elevated levels of aerosol loading between the surface and 6 km.

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GOES-17 (GOES-West) Split Window (10.3-12.3 µm), Split Cloud Top Phase (11.2-8.4 µm) and “Red” Visible (0.64 µm) images (above) displayed a plume of blowing dust — whose source region was a dry lake bed along the California-Nevada border — which developed in advance of an approaching cold front (surface analyses) and moved northeastward across far southern Nevada on 09 April 2019. Wind gusts of 50-65 mph were reported across the region.

This dust plume was also apparent over far southern Nevada in GOES-17 True Color Red-Green-Blue (RGB) images from the AOS site (below).

There are 5 airports located in the Las Vegas Valley, and GOES-17 images showed that the dust plume passed directly over Henderson (KHND) — time series plots of surface data from these sites (below) indicated that visibility was reduced to 3 miles at Henderson, with visibilities dropping to 8-9 miles at McCarran International Airport (KLAS) and Nellis Air Force Base (KLSV). The visibility was not impacted at the North Las Vegas Airport (KVGT), with its more northwest location being farther from the dust plume.

A notable exception was the Boulder City Municipal Airport (KBVU), which was downwind of a smaller local point source of blowing dust (Mursha Reservoir, another dry lake bed to the southwest) — the visibility at KBVU was restricted to 2 miles at times. With the 2-km spatial resolution (at satellite nadir) of the GOES-17 Infrared spectral bands, there was not a signature of this smaller-scale Boulder City dust plume in the 10.3-12.3 µm and 11.2-8.4 µm Brightness Temperature Difference products — however, this hazy plume was evident in the 0.5-km resolution (at satellite nadir) Visible imagery.

A comparison of 1-km resolution NOAA-19 AVHRR Visible (0.63 µm), Shortwave Infrared (3.8 µm) and Split Window (10.8-12.0 µm) images (below) provided a detailed view of the primary dust plume — and also exhibited a subtle signature of the smaller plume that reduced visibility at Boulder City KBVU. The small dust aerosols act as efficient reflectors of incoming solar radiation, therefore appearing warmer (darker) on the Shortwave Infrared image.

The GOES-17 and NOAA-19 images also showed that the larger dust plume moved across a section of Interstate 15 between Sloan and Jean; traffic cameras showed significant reductions in visibility along I-15 near Primm (along the California/Nevada border).

Dust plume visible on @nevadadot cam blowing across I-15 near the NV/CA border! Be prepared for reduced visibility! #NVwx #CAwx pic.twitter.com/hkwlgp9IzJ

— NWS Las Vegas (@NWSVegas) April 9, 2019

#VegasWeather – it’s pretty bad out there in some areas. https://t.co/mdzeGNnMSL

— Clark County Nevada (@ClarkCountyNV) April 9, 2019

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Polar2Grid is Python-based software created at CIMSS/SSEC to process data from Polar Orbiting satellites into useful imagery (or into files that can be imported directly into the National Weather Service Advanced Weather Information Processing System (AWIPS) for display). (You can download it for a linux machine at this site.) This tutorial will describe where to find downloadable NOAA-20 (or Suomi-NPP) data, and how to create imagery. Thus, this blog post is similar to earlier blogs posts (here, and here) that described how to use Geo2Grid for Geostationary Satellites. Earlier blog posts showing Polar2Grid output are here and here.

There are data repositories associated with many direct broadcast antennas — but they are not always publically available. NOAA CLASS data is a reliable source for NOAA-20 and Suomi-NPP data. Typically data shows up there within six hours of its being sampled by the satellite. For a post-event analysis, this is a good data source. For near-real time investigation, it’s better to acquire data from a direct broadcast site. (Data from the Direct Broadcast antenna at CIMSS are available here.)

The first thing required at the NOAA CLASS site is to request the data that you want to access. There is a long drop-down menu (‘Please select a product to search’ is its original title) on the CLASS front page; use the drop down to select ‘JPSS Visible Infrared Imaging Radiometer Suite Sensor Data Record (VIIRS_SDR)’. This tells CLASS that you want VIIRS data from either Suomi NPP or NOAA-20. Click on the ‘Go’ button to the right of the menu bar. That takes you to a selection page.

It is necessary to log into the CLASS system. When you order data, an email is sent acknowledging the order, and a follow-on email once the data are available specifies how you can download it. The log-in tells CLASS where to send the emails.

Polar2Grid expects the data to be ‘de-aggregated’, so you must set User Preferences in NOAA CLASS to do this. Click on the ‘User Preferences’ link in the table on the left-hand side of the main NOAA CLASS Page (after you have logged in). The location is shown below in the toggle.

Once you have entered the User Preference page, make certain that the ‘Package Geolocation with JPSS Data Products’ button is toggled to ‘No’. (Shown here) You also do want to De-aggregate JPSS Data Products. (That is, separate files for each band). In practice, this blogger finds it easier (that is, Polar2Grid complains less) if I make separate data requests at CLASS for each VIIRS band I want to view. (Update: ‘JPSS Companion Files to deliver with data’ should also be blank, as as shown at the link. This allows for one data request.)

Data requests require you to know something about the orbits of the satellites — so you can narrow down the amount of data you want. This handy website shows where all polar orbiters have been flying. Follow the links to the specific satellite and location; for example, if we’re interested in NOAA-20 orbits over Hawai’i on April 8 2019, this map shows them. The map suggests requesting NOAA-20 from 23:20 to 23:30 on 8 April 2019 will give a good view of the islands.

The data we will request are I01, I03 and I05 (0.64 µm, 1.61 µm and 11.45 µm, respectively). In addition to requesting the Sensor Data Records, it is also necessary to request, near the bottom of the page, the ‘VIIRS Image Bands SDR Ellipsoid Terrain Corrected Geolocation (GITCO) (public)’. These geolocation files are used by Polar2Grid to navigate the imagery.

After submitting the data request, and waiting a period of time (sometimes as short as two or three minutes), you will receive an email telling you exactly where the data sits, and how to access it. It will look something like this. Download the data into a directory, and you’re ready to have Polar2Grid create imagery for you. The directory for I05 (I will put data for each band in a separate directory) should include files named like this:

GIMGO_j01_d20190408_t2320306_e2321551_b07188_c20190409223158545413_noac_ops.h5
GITCO_j01_d20190408_t2320306_e2321551_b07188_c20190409223155222712_noac_ops.h5
SVI03_j01_d20190408_t2320306_e2321551_b07188_c20190409223158545413_noac_ops.h5

‘GIMGO’ and ‘GITCO’ refer to geolocation data; even though you only requested GITCO fields, CLASS will give both. That’s okay. SVI01, SVI03 and SVI05 hold data from bands I01, I03 and I05, respectively. The ‘j01’ tag signifies data from NOAA-20, or JPSS-1. For the 20-minute request I made for each band, I received 8 of each type of file.

The Polar2Grid command that creates imagery is this one:

polar2grid.sh viirs_sdr gtiff -p i03 -f /home/scottl/Polar2Grid/data/viirs/I03/

The polar2grid.sh shell script is within the bin directory of the Polar2Grid software that emerges after you g-unzip and untar the file from the Polar2Grid download site. The command results in a .tif file with highest-resolution grey-scaled imagery. The image below (converted from .tif to .png) shows the scene. It’s quite large: 6876×7616 pixels!

It’s useful to add maps to the imagery, and that is done in Polar2Grid after the .tif file has been created by using ‘add_coastlines.sh’, also in the bin directory. The command can add coastlines (–add-coastlines), borders (–add-borders), rivers (–add-rivers) and lat/lon lines (–add-grid). Lat/Lon lines can also be labeled. When maps are added to the .tif file, the image is also saved as a .png file so the original scene is not overwritten. You can use the -o flag to rename the new file. The command I used is shown below.

add_coastlines.sh –add-coastlines j01_viirs_i03_20190408_231906_wgs84_fit.tif –coastlines-resolution=f –coastlines-level=6 –coastlines-outline=’magenta’ -o j01_I03_20190408_231906.png

After adding maps to all three files created, I used ImageMagick to crop the large fields to something more manageable in size (convert -crop 1520×900+2112+2409 +repage -quality 100 j01_I05_20190408_231906.png j01_I05_20190408_231906.gif, for example), and added annotation to the imagery:

convert -font helvetica -fill yellow -pointsize 36 -draw “text 70,70 ‘NOAA-20 VIIRS Image Band I01 (0.64 µm) 23:25 UTC 8 April 2019′” j01_I01_20190408_231906.gif j01_I01_20190408_231906.annot.gif

The animation at the top of this blog post shows all three images (I01, I03 and I05) sequentially.

Final note: Infrared imagery by default is not color-enhanced, and built-in enhancements are not (yet) available. Polar2Grid does support creation of colortables, however. That will be the subject of a future blog post.

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