[Added, 14 December: geo2grid v 1.1 is now available at this link!] The true color animation toggle above, over Taiwan, shows True-Color imagery over Taiwan shortly after 0000 UTC on 8 December using FY4A and Himawari-9 data; readers for data from those satellites are included in version 1.1 of geo2grid. Data from Himawari-9 (HSD level 1b files) are supplied courtesy of JMA, the Japan Meteorological Agency. FY4A data from CMA are available at this link (free registration is required to download the data). At that site (shown below), you can choose AGRI data from FY4A, and the download link is available on a separate page that becomes available after choosing the day and time. (In the screen capture, the 500-m resolution checked is only Channel 2, whereas the file used for this blog post is the topmost one — with a size of 355 Mbytes; this file took some time to download).

First, you can user –list-products to determine what can be created from the hdf file:

$GEO2GRID_HOME/bin/geo2grid.sh -r agri_fy4a_l1 -w geotiff --list-products -f /data-hdd/AGRI/FY4A-_AGRI--_N_DISK_1047E_L1-_FDI-_MULT_NOM_20221208001500_20221208002959_1000M_V0001.HDF

The output showed the following possibilities: C01, C02, C03, true_color. This WMO website ( https://space.oscar.wmo.int/instruments/view/agri ) shows that C01-C03 on the AGRI instrument correspond to wavelengths of 0.47 µm, 0.65 µm and 0.83 µm. Two commands are run as shown below; the first one creates a small mapped region (a similar restriction to how much data to process could be achieved using the –ll-box keyword in geo2grid), the second creates the image over that domain:

$GEO2GRID_HOME/bin//p2g_grid_helper.sh Taiwan 121.0 24.0 1000 -1000 960 720 > $GEO2GRID_HOME/Taiwan.yaml

$GEO2GRID_HOME/bin/geo2grid.sh -r agri_fy4a_l1 -w geotiff -p true_color --grids Taiwan --grid-configs $GEO2GRID_HOME/Taiwan.yaml -f /data-hdd/AGRI/FY4A-_AGRI--_N_DISK_1047E_L1-_FDI-_MULT_NOM_20221208001500_20221208002959_1000M_V0001.HDF

The geo2grid.sh invocation here does not have access to all the information that is needed, and the output notes that solar zenith angle correction in the true color will not occur.

INFO     : Sorting and reading input files...
INFO     : Loading product metadata from files...
WARNING  : Required file type 'agri_l1_4000m_geo' not found or loaded for 'satellite_azimuth_angle'
WARNING  : Required file type 'agri_l1_4000m_geo' not found or loaded for 'solar_zenith_angle'
WARNING  : Required file type 'agri_l1_4000m_geo' not found or loaded for 'solar_azimuth_angle'
WARNING  : Required file type 'agri_l1_4000m_geo' not found or loaded for 'satellite_zenith_angle'
INFO     : Checking products for sufficient output grid coverage (grid: 'Taiwan')...
INFO     : Resampling to 'Taiwan' using 'nearest' resampling...
INFO     : Computing products and saving data to writers...
INFO     : SUCCESS

The invocation of geo2grid to read the Himawari data (that does include information for the solar zenith angle correction) and the output from that call is shown below.

$GEO2GRID_HOME/bin/geo2grid.sh -r ahi_hsd -w geotiff -p true_color --grids Taiwan --grid-configs $GEO2GRID_HOME/Taiwan.yaml -f /path/to/data/himawari09/2022/2022_12_08_342/0020/*FLDK*.DAT
INFO     : Sorting and reading input files...
INFO     : Loading product metadata from files...
INFO     : Checking products for sufficient output grid coverage (grid: 'Taiwan')...
INFO     : Resampling to 'Taiwan' using 'nearest' resampling...
INFO     : Computing products and saving data to writers...
INFO     : SUCCESS

You will note a slight shift in the imagery in the toggle above, suggesting different navigations for the two satellites.

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GOES-16 Air Mass RGB images, with ship reports plotted in yellow [click to play animated GIF | MP4]

GOES-16 (GOES-East) Air Mass RGB images (above) showed the signature of dry air (brighter shades of orange-red, which also indicate the presence of a lower tropopause with higher levels of stratospheric ozone within the atmospheric column) wrapping into the circulation of an anomalously-deep Hurricane Force low pressure system — which had recently rapidly intensified over the North Atlantic Ocean — during the 09 December – 10 December 2022 period (surface analyses).

A closer view of GOES-16 Air Mass RGB images (below) includes plots of land-based surface reports — and as the system slowly weakened to a Storm Force low about 200 miles NW of the Azores at 12 UTC on 10 December, a wind gust to 55 knots (63 mph) was recorded at Flores Airport in the far NW part of that island chain (RGB image | plot of surface data).

GOES-16 Air Mass RGB images, with ship reports plotted in yellow and land-based surface reports plotted in cyan [click to play animated GIF | MP4]

GOES-16 Mid-level Water Vapor (6.9 µm) images (below) exhibited a similarly-striking appearance, with the ribbon of dry air wrapping into the center of the storm’s circulation having 6.9 µm infrared brightness temperatures as warm as +4 to +5ºC (darker shades of orange). 

GOES-16 Mid-level Water Vapor (6.9 µm) images, with ship reports plotted in yellow and land-based surface reports plotted in cyan [click to play animated GIF | MP4]

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Previous blog posts have documented how to display imagery using geo2grid, and how to create new RGB products. (Note: geo2grid v1.1 is now available at this link) This blog post will detail the steps needed to create a brightness temperature difference field. For that to occur, information must be entered into these two files within the geo2grid file structure: $GEO2GRID_HOME/libexec/python_runtime/etc/polar2grid/enhancements/abi.yaml and $GEO2GRID_HOME/libexec/python_runtime/etc/polar2grid/composites/abi.yaml. For this example, I created a Water Vapor Brightness temperature difference field (named ‘wvdif’), as used with the airmass RGB (Here’s a Quick Guide for that RGB). The information added to the $GEO2GRID_HOME/libexec/python_runtime/etc/polar2grid/composites directory in the abi.yaml file (defining the bands used to create the product) is shown below. Indentations are important here; the ‘wvdif’ line is indented two spaces! In plain language, Band 10 (low level water vapor, 7.3 µm) is subtracted from Band 8 (upper level water vapor (6.19 µm).

  wvdif:
    compositor: !!python/name:satpy.composites.DifferenceCompositor
    prerequisites:
      - name: C08
      - name: C10
    standard_name: wvdif

Information added to abi.yaml file in the $GEO2GRID_HOME/libexec/python_runtime/etc/polar2grid/enhancements directory (indentations are important here; the ‘wvdif’ line is indented two spaces) is shown below. This gives the range of values that are of interest; in the RGB, values of the Water Vapor Brightness Temperature difference range from -26.2^oC to 0.6^oC. Note that ‘max_stretch’ and ‘min_stretch’ correspond to values with the greatest and smallest, respectively, amounts of red in the RGB.

  wvdif:
    standard_name: wvdif
    operations:
      - name: stretch
        method: !!python/name:satpy.enhancements.stretch
        kwargs:
          stretch: crude
          min_stretch: -26.2
          max_stretch: 0.6

The geo2grid software calls that creates the temperature difference field, and color-enhances it, and applies coastlines and a latitude/longitude grid are shown below (this geo2grid call also makes the airmass RGB, the RGB that uses the water vapor difference field as the red component of the RGB). Note that because a color bar is not added by the add_coastlines.sh script (as discussed in this blog post), multiple .tif files can be referenced.

$GEO2GRID_HOME/bin/geo2grid.sh -r abi_l1b -w geotiff -p airmass wvdif -f /path/to/ABIdata/L1b/RadC/*s20223430001*

$GEO2GRID_HOME/bin/add_colormap.sh /path/to/enhancements/Red1.txt   GOES-16_ABI_RadC_C13_20221209_000117_GOES-East.tif

$GEO2GRID_HOME/bin/add_coastlines.sh --add-coastlines --coastlines-resolution f --coastlines-level 5 --add-grid --grid-D 10.0 10.0 --grid-d 10. 10.  --grid-text-size 14 *1209_0001*.tif

The animation at the top shows the airmass RGB, the greyscaled water vapor difference field, and the water vapor difference field enhanced to show how much red will be in the airmass RGB.

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The VIIRS (Visible/Infrared Imaging Radiometer Suite) instrument on-board NOAA-21 (launched in November 2022) is now sending data to Earth. (Here is the NOAA Satellite Tweet announcing this landmark occasion!). Direct Broadcast users can also view NOAA-21 data now; the image above was downloaded and processed using the latest Community Satellite Processing Package (CSPP) at CIMSS. (Users must also update their antenna demodulator to allow receipt of NOAA-21 data!).

The VIIRS instrument has great resolution, as shown in the subsected views below of Long Island (partially hidden by thin cirrus, Florida, and ice-covered Lake Winnipeg. NOAA-21 will be undergoing testing and evaluation over the next months prior to being declared operational.

Direct Broadcast users will note that NOAA-21 and Suomi NPP conflict; that is, users with a single antenna must choose to download data from one or the other.

The image below, courtesy Kathy Strabala and Liam Gumley, CIMSS, shows an image similar to the NOAA Satellite Tweet. The image below was created with SDR data from NOAA CLASS, and those data were processed with CSPP’s Polar2Grid (v 2.3) software (downloadable here).

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