Using CSPP Quicklooks software to show multiple NUCAPS passes

August 31st, 2022 |
Suomi-NPP NUCAPS estimates of 300-mb temperature, 15-20 October 2018 (click to enlarge)

CSPP Quicklooks (click to view documentation) is a software package (available here) developed at CIMSS to (as the name might suggest!) create images from Polar-orbiting data, as shown in this blog post that shows imagery created from Direct Broadcast data (for example. using NUCAPS EDRs (Environmental Data Records) files as available at the CIMSS Direct Broadcast site). NUCAPS EDR files can also be downloaded from the NOAA CLASS site — by choosing ‘JPSS Sounder Products’ in the ‘Please select a product to search’ drop-down menu, and then choosing ‘NUCAPS Environmental Data Records’ — that is, EDRs.

Follow the instructions in this blog post to download and set up the Quicklooks software (free registration at the CSPP website may be required). Imagery at the previous blog post used default domains and colorbars. In the example above, multiple images are captured over a specified domain, and are scaled identically using keywords as shown in the calls below:

sh ./ "$file18" NUCAPS --dset temp --pressure 300 --plotMin 223.0 --plotMax 258.0 --lat_0 15.0 --lon_0 -75.0

Note that ‘file18’ identifies all files within a directory that contain an EDR from around 1700 UTC on 19 October 2018. The wildcard includes >20 different granules that are composited into an image for that time, as shown below. The –plotMin and –plotMax keywords define the color scaling used, and the data are centered at 15o N, 75o W on a Lambert Conformal grid. Similarly, an image that uses all data from 20181919* can be created, as shown below.

Suomi-NPP NUCAPS profiles temperatures at 300 mb, ca. 1700 UTC on 19 October 2018 (Click to enlarge)
Suomi-NPP NUCAPS profiles temperatures at 300 mb, ca. 1900 UTC on 19 October 2018 (Click to enlarge)

How are both images combined so that data from the afternoon/evening passes are in one image (as shown in the animation above?) This is done by making parts of the images above transparent, and by overlaying the transparent image over the bottom image (I did this using ImageMagick). Both white (“#ffffff”) and grey (“#d9d9d9”) values were made transparent, and a combined image (here) is created. This is done for all morning images, and afternoon/evening images, and the animation is then created.

Note that if this is done when both Suomi-NPP and NOAA-20 passes are available, the gaps apparent in the imagey above will not be present. October 2018 was before NOAA-20 NUCAPS were operational however.

The Day Convection RGB and Cloud Particle Size

April 28th, 2022 |
Day Cloud Convection RGB over the Amazon River delta, 1640-1720 UTC on 27 April 2022 (Click to enlarge)

Conventional Meteorological Canon is that the Day Convection RGB, shown above in full-disk imagery from AWIPS (and because it’s Full Disk, the resolution is degraded to 6 km in both latitudinal and longitudinal directions), shows yellow in regions where cloud (ice) particle sizes are very small at the top of strong updrafts, and because they are small, there is much more reflectance of 3.9 µm solar radiation, so that the Day Fog Difference (3.9 µm – 10.3 µm), the green component of the RGB, acquires a very strong positive value. One of the Level 2 products derived from the Advanced Baseline Imager (ABI) is Cloud Particle Size Distribution (PSD). What does the PSD look like over that developing convection. This can be explored using the Satellite Information Familiarization Tool (SIFT).

The toggle below shows the Day Convection RGB and the GOES-16 Level 2 Cloud Particle Size at 1700 UTC on 27 April 2022, with growing convection over the large island (Marajó) in the Amazon Delta. The Cloud Particle Size individually with a color bar is shown below as well.

Day Convection RGB and Cloud Particle Size, 1700 UTC on 27 April 2022 (click to enlarge)
Cloud Particle Size, 1700 UTC on 27 April 2022 (Click to enlarge)

Cloud Particle size values over the growing convection do appear to be smaller than in regions surrounding the growing convection. SIFT includes functionality to compare values within regions. The toggle below shows a small polygon — shaded in magenta — over the growing convection. The scatterplot beneath compares the Day Fog Difference and Cloud Particle Size within the polygon. Note that the Cloud Particle Size Distribution product is computed in daytime with ABI Bands 2 (0.64 µm) and 6 (2.25 µm) (ATBD).

GOES-16 Day Convection (1700 UTC on 27 April 2022) with a small defined region used in the scatterplot below (Click to enlarge)
GOES-16 Day Fog Brightness Temperature Difference (x-axis) vs. Level 2 Particle Size Distribution, 1700 UTC on 27 April 2022 in the small region shown above (Click to enlarge)

The scatterplot above does suggest that Night Fog Brightness Temperature Difference fields — when large (which means where the Convection RGB will acquire a yellowish hue) — are related to Cloud Particle Size over the developing convection: smaller cloud particles are indicated where the Day Convection RGB is most yellow. There are other regions of yellow in the scene — and other regions of small particle size. Consider the region to the east, for example, as shown in the toggle below. Similar to the scene above, where the Brightness Temperature Difference is large, the Cloud Particle Sizes are small. A region-wide comparison (i.e., for the whole scene), is here.

GOES-16 Day Convection (1700 UTC on 27 April 2022) with a small defined region used in the scatterplot below (Click to enlarge)
GOES-16 Day Fog Brightness Temperature Difference (x-axis) vs. Level 2 Particle Size Distribution, 1700 UTC on 27 April 2022 in the small region shown above (Click to enlarge)

Coming Soon: a Geo2Grid update!

March 11th, 2022 |
Himawari-8 airmass RGB, 0000 UTC 24 February – 0000 UTC 28 February 2022

CIMSS scientists have been working on an update (and improvement) on Geo2Grid software (v 1.1) that can be expected within a couple months. (Beta-testing is ongoing at CIMSS) The animation above shows the Airmass RGB from Himawari-8 (data courtesy JMA) and the Fujiwhara interaction (also discussed on the CIMSS blog here) between southern Indian Ocean tropical cyclone Vernon and invest area 93S. The animation stretches from 0000 UTC on 24 March to 0000 UTC on 28 March.

Because the animation includes data near the limb, the remapping was changed from the default. That is, the geo2grid invocation includes the following specification:

-g Fujiwhara --grid-configs $GEO2GRID_HOME/Fujiwhara.yaml --method nearest --radius-of-influence 40000

Remapping is done using a nearest-neighbor approach (the default in geo2grid when a non-native projection is used), and points within 40 km (40000 m) are considered. Note also that the full-disk AHI imagery is reprojected onto a grid named ‘Fujiwhara’; the components required by geo2grid are within the file Fujiwhara.yaml (as created by a call to the script):

./ Fujiwhara 88.0 -12.0 2000 2000 1080 640 > $GEO2GRID_HOME/Fujiwhara.yaml 

What was the series of unix-based commands that produced the imagery above? Those are outlined below in a shell script. This uses the beta version of geo2grid v1.1; similar commands will work in v1.0. Before this shell script can be invoked, the compressed package must be expanded, creating a directory with many sub-directories, such as /bin, /share, /colormaps, /etc, and so on. This directory is the GEOGRID_HOME directory, and you can specify it, as shown below.

cd /directory/where/geo2gridpkg/sits/
cd bin
# The command below will create a grid (named 'Fujiwhara') 
#   centered at 88 E, 12 S (geo2grid assumes east/north positive)
# The grid has 2 km resolution in the x- and y- directions, and
#   its size is 1080x640 (so it's wider than it is high)
# Place the multi-line output from in the file
#   named $GEO2GRID_HOME/Fujiwhara.yaml
../ Fujiwhara 88.0 -12.0 2000 2000 1080 640   > $GEO2GRID_HOME/Fujiwhara.yaml
# Now, call geo2grid, reading (the -r flag) AHI HSD format files
#   Write out (the -w flag) geotiff files of (the -p flag) the product
#   airmass RGB.  Regrid (the -g flag) the data to the 'Fujiwhara' 
#   grid defined above, and tell geo2grid where the grid definitions sit
#   (the --grid-configs filename flag).  Regridding uses nearest-
#   neighbor (--method nearest) and it looks within 40000 m of each grid
#   point (--radius-of-influence 40000)  (Note that geo2grid v1.0 did
#   not include --radius-of-influence!)
../ -r ahi_hsd -w geotiff -p airmass  -g Fujiwhara --grid-configs $GEO2GRID_HOME/Fujiwhara.yaml --method nearest --radius-of-influence 40000 -f /directory/holding/Himawari-8/HSDFILES/for/ONE/time/only/*FLDK*.DAT
#  The command above will create a geotiff file with a name like this:
#  HIMAWARI-8_AHI_airmass_20220223_0000??_Fujiwhara.tif
#  The command below adds coastlines (in blue) and lat/lon line 
#  (--add-grid) colored in the default (cyan).  The output is a 
#  .png
../ --add-coastlines --coastlines-resolution h --coastlines-level=5 --coastlines-outline='blue' --add-grid --grid-text-size 8 --grid-D 5.0 5.0 --grid-d 5.0 5.0    HIMAWARI-8_AHI_airmass_20220223_0000??_Fujiwhara.tif
#  I use ImageMagick commands to annotate the imagery, as shown below
convert HIMAWARI-8_AHI_airmass_20220223_0000??_Fujiwhara.png   -gravity Southwest -fill yellow -pointsize 16 -annotate +12+16 "HIMAWARI-8 AHI airmass RGB 0000 UTC 23 February 2022"  HIMAWARI-8_AHI_airmass_20220223_0000_Fujiwhara.png
#  Gotta add a logo too!
convert HIMAWARI-8_AHI_airmass_20220223_0000_Fujiwhara.png ~scottl/smalllogo.png   -gravity northwest -geometry +12+8 -composite HIMAWARI-8_AHI_airmass_20220223_0000_FujiwharaL.png

How does a change in –radius-of-influence affect the created image? If you’re near the limb, and the radius of influence is too small, data gaps can appear. The image below compares an image with radius of influence of 2km (the native resolution at the sub-satellite point for infrared imagery) with an image with a radius of influence of 40 km.

A Full-disk image at this time is here so you can judge how close to the edge this event it.

Himawari-8 airmass RGB at 0000 UTC on 25 February 2022 created with regridding using a radius of influence of 2 km (left) vs. 40 km (right) (Click to enlarge)

A non-satellite of the Fujiwhara interaction is shown below.

Creating a VIIRS brightness temperature difference field from direct broadcast data using McIDAS-V

March 4th, 2022 |

This blog post contains Suomi-NPP VIIRS imagery that was derived (using CSPP) from data downloaded at the Direct Broadcast site at CIMSS. That blog post suggests the need of a brightness temperature difference field from the I04 and I05 data that can be found in this ( direct broadcast directory (direct link, available for about 6 days). The Sensor Data Record directory includes I04 and I05 hdf5 granules that McIDAS-V can read: SVI04_npp_d20220303_t0641201_e0642443_b53613_c20220303071038095682_cspp_dev.h5 and SVI05_npp_d20220303_t0641201_e0642443_b53613_c20220303071039605225_cspp_dev.h5 ; determining exactly which granule you want — there are 10 different granules in this particular directory — is partly trial and error and partly viewing the orbit path (here) and choosing wisely. Save those files into a directory; also save the ‘GITCO’ files (that is: GITCO_npp_d20220303_t0641201_e0642443_b53613_c20220303070838040363_cspp_dev.h5) that contain georeferencing for the Imager (‘I’) bands (similarly, GMTCO files contain georeferencing for the Moderate-resolution ‘M’ bands).

McIDAS-V Data Source load window

After starting up McIDAS-V, you want to load the data. Note above that I’ve clicked on JPSS Imagery, and navigated to the directory containing the downloaded data (that directory also includes the GITCO files for those granules, but you don’t see them here). I’ve chosen both I04 and I05 data files from one granule, observed from 06:41:20.1 to 06:42:44.3. Click on ‘Add Source’ in the lower right corner of the window. If you then expand ‘IMAGE’ under ‘Fields’, you’ll see both I04 and I05 Brightness Temperatures.

McIDAS-V Field Selector window

Next, under the ‘Data Sources:’ tab, click on ‘Formulas’. You will see a ‘Miscellaneous’ tab, and under that tab, a ‘Simple Difference a-b’ choice. Choose that and click ‘Create Display’ — this will pop up a window in which you can choose the a (in this case, I05 Brightness Temperature) and b (for this case, I04 Brightness Temperature). Subsect the portion of the granule that you want to display using shift-left click and drag — and — after clicking ‘create image’ — you end up with the image below (zoomed in).

Created I05 – I04 field over portions of central Florida (Click to enlarge)

There is some fine-tuning yet to do. Under the ‘Legend’ in the image above, right-click on ‘VIIRS 2022-03-03…’ to bring up the Control Window shown below. Slide the ‘Texture Quality’ from ‘Medium’ to ‘High’ (if you have a large image — much larger than this one! — that will test your machine’s RAM!)

Layer Controls Window in McIDAS-V

Similarly, if you right-click (again!) under ‘Legend’ and ‘VIIRS 2022-03-03…’ to ‘Edit->Properties’, you can change the Layer Label to include more information, which I did, as shown in the image below. Finally, I edited the color table to highlight positive values (that is, where I05 – I04 Brightness Temperature Difference is between 0 and 2o C) that might show where stratiform clouds are present. That result is shown below. Yellow in the enhancement shows no difference between the fields, blue is a brightness temperature difference of 2o C. Are you obtaining a good signal of fog in the region of the very dense fog over southern Volusia County? I’d only ask what a ‘good signal’ is!

McIDAS-V display, I05-I04, 0641 UTC on 3 March 2022 (click to enlarge)

Imagery in this post was created using v1.8 of McIDAS-V (downloadable here). You can find further documentation on this here.