GOES-16 (GOES-East) “Red” Visible (0.64 µm) images (above) include plots of GOES-16 Derived Motion Winds (DMW) within the 775-900 hPa and the 900 hPa-Surface layers — which displayed a rapidly-intensifying area of low pressure (surface analyses) over the West Atlantic Ocean (south of Nova Scotia, Canada) on 12 December 2022. Note the area of haziness just east and southeast of the lobe of deep convection in the center of the satellite scene — this milky/hazy appearance was due to the enhanced diffuse reflection of light off very rough seas (likely accompanied by abundant sea spray) resulting from a burst of strong surface winds across that particular area. Several nearby DMW vectors within the 900 hPa-Surface layer exhibited speeds of 50 knots or higher, including 62 knots at 1446 UTC and 59 knots at 1746 UTC. In addition, GCOM-W1 AMSR2 surface winds (source) in the vicinity of the diffuse reflection signature were around 60 knots at 1813 UTC.

This region of enhanced diffuse reflection was further highlighted in GOES-16 True Color RGB images from the CSPP GeoSphere site (below).

The corresponding GOES-16 Low-level Water Vapor (7.3 µm) images (below) showed an area of orange enhancement that likely represented rapidly-descending (and hence warming/drying, via adiabatic compression) air within the lower troposphere, which was rotating around the southeastern and eastern edge of the lobe of deep convection.

A sequence of Suomi-NPP VIIRS Visible (0.64 µm), Near-Infrared “Vegetation” (0.87 µm), Near-Infrared “Snow/Ice” (1.61 µm), Shortwave Infrared (3.74 µm), Infrared Window (11.45 µm), True Color RGB and False Color RGB images — along with the corresponding GOES-16 Derived Motion Winds near that time (below) provided a more detailed view of the area of enhanced diffuse reflection. Also apparent at that time was the hook-like shape along the southeastern edge of the lobe of deep convection, somewhat resembling a “scorpion tail” that is frequently seen in cases of a sting jet (Monthly Weather Review | Wikipedia).

The aforementioned satellite signatures in this case resemble those seen with another rapidly-intensifying low off the coast of North Carolina in April 2019, which also featured a sting jet.

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[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 (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 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). 

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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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