This notebook is developed during the pythia cook-off at NCAR Mesa-Lab Boulder Colorado, June 12-14, 2024
Participants in the workshop event have the chance to practice collaborative problem-solving and hands-on learning in the field of Python programming.
This notebook is part of the Breakout Topic: Geostationary on AWS, lead by Jorge Humberto Bravo Mendez jbravo2@stevens.edu, from Stevens Institute of Technology
Advanced Himawari Imager (AHI) data with Satpy¶
Using Satpy to read and Advanced Baseline Imager (ABI) data from GOES-R satellites. Here’s a step-by-step guide:
Imports¶
import warnings
warnings.filterwarnings('ignore')
warnings.simplefilter('ignore', SyntaxWarning)
from satpy.scene import Scene
from satpy.utils import debug_on
from datetime import datetime
from glob import globStarting to create satpy scenes¶
sat_files = glob("input/HI9_AHI_L1b_JP/*")
sat_files['input/HI9_AHI_L1b_JP/HS_H09_20230815_0430_B05_JP02_R20_S0101.DAT.bz2',
'input/HI9_AHI_L1b_JP/HS_H09_20230815_0430_B11_JP02_R20_S0101.DAT.bz2',
'input/HI9_AHI_L1b_JP/HS_H09_20230815_0430_B07_JP02_R20_S0101.DAT.bz2',
'input/HI9_AHI_L1b_JP/HS_H09_20230815_0430_B01_JP02_R10_S0101.DAT.bz2',
'input/HI9_AHI_L1b_JP/HS_H09_20230815_0430_B04_JP02_R10_S0101.DAT.bz2',
'input/HI9_AHI_L1b_JP/HS_H09_20230815_0430_B02_JP02_R10_S0101.DAT.bz2',
'input/HI9_AHI_L1b_JP/HS_H09_20230815_0430_B14_JP02_R20_S0101.DAT.bz2',
'input/HI9_AHI_L1b_JP/HS_H09_20230815_0430_B09_JP02_R20_S0101.DAT.bz2',
'input/HI9_AHI_L1b_JP/HS_H09_20230815_0430_B10_JP02_R20_S0101.DAT.bz2',
'input/HI9_AHI_L1b_JP/HS_H09_20230815_0430_B16_JP02_R20_S0101.DAT.bz2',
'input/HI9_AHI_L1b_JP/HS_H09_20230815_0430_B08_JP02_R20_S0101.DAT.bz2',
'input/HI9_AHI_L1b_JP/HS_H09_20230815_0430_B03_JP02_R05_S0101.DAT.bz2',
'input/HI9_AHI_L1b_JP/HS_H09_20230815_0430_B13_JP02_R20_S0101.DAT.bz2',
'input/HI9_AHI_L1b_JP/HS_H09_20230815_0430_B15_JP02_R20_S0101.DAT.bz2',
'input/HI9_AHI_L1b_JP/HS_H09_20230815_0430_B12_JP02_R20_S0101.DAT.bz2',
'input/HI9_AHI_L1b_JP/HS_H09_20230815_0430_B06_JP02_R20_S0101.DAT.bz2']scn = Scene(filenames = sat_files, reader='ahi_hsd')
dataset_names = scn.all_dataset_names()
print(dataset_names)['B01', 'B02', 'B03', 'B04', 'B05', 'B06', 'B07', 'B08', 'B09', 'B10', 'B11', 'B12', 'B13', 'B14', 'B15', 'B16']
scn.load(dataset_names)print(scn.available_composite_names())['airmass', 'ash', 'cloud_phase_distinction', 'cloud_phase_distinction_raw', 'cloudtop', 'colorized_ir_clouds', 'convection', 'day_microphysics_ahi', 'day_microphysics_eum', 'day_severe_storms', 'dust', 'fire_temperature', 'fire_temperature_39refl', 'fire_temperature_awips', 'fire_temperature_eumetsat', 'fog', 'geo_color', 'geo_color_background_with_low_clouds', 'geo_color_high_clouds', 'geo_color_low_clouds', 'geo_color_night', 'hybrid_green', 'hybrid_green_nocorr', 'ir_cloud_day', 'mid_vapor', 'natural_color', 'natural_color_nocorr', 'natural_color_raw', 'natural_color_raw_with_night_ir', 'ndvi_hybrid_green', 'night_ir_alpha', 'night_ir_with_background', 'night_ir_with_background_hires', 'night_microphysics', 'night_microphysics_tropical', 'overview', 'overview_raw', 'reproduced_green', 'reproduced_green_uncorr', 'rocket_plume_night', 'true_color', 'true_color_ndvi_green', 'true_color_nocorr', 'true_color_raw', 'true_color_reproduction', 'true_color_reproduction_corr', 'true_color_reproduction_night_ir', 'true_color_reproduction_uncorr', 'true_color_with_night_ir', 'true_color_with_night_ir_hires', 'water_vapors1', 'water_vapors2']
rgb_im = 'airmass'
scn.load([rgb_im])### Uncomment to show it
#scn.show(rgb_im)result = scn[rgb_im]
resultLoading...
keys = scn.keys()
keys[DataID(name='B01', wavelength=WavelengthRange(min=0.45, central=0.47, max=0.49, unit='µm'), resolution=1000, calibration=<1>, modifiers=()),
DataID(name='B02', wavelength=WavelengthRange(min=0.49, central=0.51, max=0.53, unit='µm'), resolution=1000, calibration=<1>, modifiers=()),
DataID(name='B03', wavelength=WavelengthRange(min=0.62, central=0.64, max=0.66, unit='µm'), resolution=500, calibration=<1>, modifiers=()),
DataID(name='B04', wavelength=WavelengthRange(min=0.85, central=0.86, max=0.87, unit='µm'), resolution=1000, calibration=<1>, modifiers=()),
DataID(name='B05', wavelength=WavelengthRange(min=1.5, central=1.6, max=1.7, unit='µm'), resolution=2000, calibration=<1>, modifiers=()),
DataID(name='B06', wavelength=WavelengthRange(min=2.2, central=2.3, max=2.4, unit='µm'), resolution=2000, calibration=<1>, modifiers=()),
DataID(name='B07', wavelength=WavelengthRange(min=3.7, central=3.9, max=4.1, unit='µm'), resolution=2000, calibration=<2>, modifiers=()),
DataID(name='B08', wavelength=WavelengthRange(min=6.0, central=6.2, max=6.4, unit='µm'), resolution=2000, calibration=<2>, modifiers=()),
DataID(name='B09', wavelength=WavelengthRange(min=6.7, central=6.9, max=7.1, unit='µm'), resolution=2000, calibration=<2>, modifiers=()),
DataID(name='B10', wavelength=WavelengthRange(min=7.1, central=7.3, max=7.5, unit='µm'), resolution=2000, calibration=<2>, modifiers=()),
DataID(name='B11', wavelength=WavelengthRange(min=8.4, central=8.6, max=8.8, unit='µm'), resolution=2000, calibration=<2>, modifiers=()),
DataID(name='B12', wavelength=WavelengthRange(min=9.4, central=9.6, max=9.8, unit='µm'), resolution=2000, calibration=<2>, modifiers=()),
DataID(name='B13', wavelength=WavelengthRange(min=10.2, central=10.4, max=10.6, unit='µm'), resolution=2000, calibration=<2>, modifiers=()),
DataID(name='B14', wavelength=WavelengthRange(min=11.0, central=11.2, max=11.4, unit='µm'), resolution=2000, calibration=<2>, modifiers=()),
DataID(name='B15', wavelength=WavelengthRange(min=12.2, central=12.4, max=12.6, unit='µm'), resolution=2000, calibration=<2>, modifiers=()),
DataID(name='B16', wavelength=WavelengthRange(min=13.1, central=13.3, max=13.5, unit='µm'), resolution=2000, calibration=<2>, modifiers=()),
DataID(name='airmass', resolution=2000)]area_info = scn["B13"].area
area_infoLoading...
scn.load(["natural_color"])The following datasets were not created and may require resampling to be generated: DataID(name='natural_color')
rs = scn["B13"].area
lscn = scn.resample(rs)lscn.load(["natural_color"])
### Uncomment to show it
#lscn.show("natural_color")lscn.load(['true_color'])
### Uncomment to show it
#lscn.show('true_color')No rsr file /home/runner/.local/share/pyspectral/rsr_ahi_Himawari-9.h5 on disk
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