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 Baseline Imager (ABI) 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 glob

Starting to create satpy scenes¶

sat_files = glob("input/G16_ABI-L1b-RadC/*")
sat_files

['input/G16_ABI-L1b-RadC/OR_ABI-L1b-RadC-M6C06_G16_s20232561536173_e20232561538551_c20232561539011.nc',
 'input/G16_ABI-L1b-RadC/OR_ABI-L1b-RadC-M6C13_G16_s20232561536173_e20232561538557_c20232561539004.nc',
 'input/G16_ABI-L1b-RadC/OR_ABI-L1b-RadC-M6C03_G16_s20232561536173_e20232561538546_c20232561538589.nc',
 'input/G16_ABI-L1b-RadC/OR_ABI-L1b-RadC-M6C01_G16_s20232561536173_e20232561538548_c20232561538585.nc',
 'input/G16_ABI-L1b-RadC/OR_ABI-L1b-RadC-M6C10_G16_s20232561536173_e20232561538557_c20232561539020.nc',
 'input/G16_ABI-L1b-RadC/OR_ABI-L1b-RadC-M6C05_G16_s20232561536173_e20232561538546_c20232561539016.nc',
 'input/G16_ABI-L1b-RadC/OR_ABI-L1b-RadC-M6C09_G16_s20232561536173_e20232561538551_c20232561538580.nc',
 'input/G16_ABI-L1b-RadC/OR_ABI-L1b-RadC-M6C07_G16_s20232561536173_e20232561538557_c20232561539026.nc',
 'input/G16_ABI-L1b-RadC/OR_ABI-L1b-RadC-M6C04_G16_s20232561536173_e20232561538546_c20232561539001.nc',
 'input/G16_ABI-L1b-RadC/OR_ABI-L1b-RadC-M6C15_G16_s20232561536173_e20232561538551_c20232561539007.nc',
 'input/G16_ABI-L1b-RadC/OR_ABI-L1b-RadC-M6C16_G16_s20232561536173_e20232561538557_c20232561539036.nc',
 'input/G16_ABI-L1b-RadC/OR_ABI-L1b-RadC-M6C02_G16_s20232561536173_e20232561538546_c20232561538578.nc',
 'input/G16_ABI-L1b-RadC/OR_ABI-L1b-RadC-M6C08_G16_s20232561536173_e20232561538546_c20232561538598.nc',
 'input/G16_ABI-L1b-RadC/OR_ABI-L1b-RadC-M6C14_G16_s20232561536173_e20232561538546_c20232561538592.nc',
 'input/G16_ABI-L1b-RadC/OR_ABI-L1b-RadC-M6C11_G16_s20232561536173_e20232561538546_c20232561539034.nc',
 'input/G16_ABI-L1b-RadC/OR_ABI-L1b-RadC-M6C12_G16_s20232561536173_e20232561538552_c20232561539031.nc']

scn = Scene(filenames = sat_files, reader='abi_l1b')

dataset_names = scn.all_dataset_names()

print(dataset_names)

['C01', 'C02', 'C03', 'C04', 'C05', 'C06', 'C07', 'C08', 'C09', 'C10', 'C11', 'C12', 'C13', 'C14', 'C15', 'C16']

scn.load([f'C{x:02d}' for x in range(1, 17)])

print(scn.available_composite_names())

['24h_microphysics', 'airmass', 'ash', 'blowing_snow', 'cimss_cloud_type', 'cimss_cloud_type_raw', 'cimss_green', 'cimss_green_sunz', 'cimss_green_sunz_rayleigh', 'cimss_true_color', 'cimss_true_color_sunz', 'cimss_true_color_sunz_rayleigh', 'cira_day_convection', 'cira_fire_temperature', 'cloud_phase', 'cloud_phase_distinction', 'cloud_phase_distinction_raw', 'cloud_phase_raw', 'cloudtop', 'color_infrared', 'colorized_ir_clouds', 'convection', 'day_cloud_type', 'day_microphysics', 'day_microphysics_abi', 'day_microphysics_eum', 'day_severe_storms', 'day_severe_storms_tropical', 'dust', 'fire_temperature_awips', 'fog', 'geo_color', 'geo_color_background_with_low_clouds', 'geo_color_high_clouds', 'geo_color_low_clouds', 'geo_color_night', 'green', 'green_crefl', 'green_nocorr', 'green_raw', 'green_snow', 'highlight_C14', 'ir108_3d', 'ir_cloud_day', 'land_cloud', 'land_cloud_fire', 'natural_color', 'natural_color_nocorr', 'natural_color_raw', 'natural_color_raw_with_night_ir', 'night_fog', 'night_ir_alpha', 'night_ir_with_background', 'night_ir_with_background_hires', 'night_microphysics', 'night_microphysics_eum', 'night_microphysics_tropical', 'overshooting_tops', 'overview', 'overview_raw', 'rocket_plume_day', 'rocket_plume_night', 'snow', 'snow_fog', 'so2', 'tropical_airmass', 'true_color', 'true_color_crefl', 'true_color_nocorr', 'true_color_raw', 'true_color_reproduction', 'true_color_reproduction_corr', 'true_color_reproduction_uncorr', 'true_color_with_night_fires', 'true_color_with_night_fires_nocorr', '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]

#result

keys = scn.keys()

#keys

area_info = scn["C13"].area

#area_info

area_info = scn["C01"].area

#area_info

area_info = scn["C02"].area

#area_info

scn.load(["natural_color"])

The following datasets were not created and may require resampling to be generated: DataID(name='natural_color')

rs = scn["C13"].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')

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Introduction

Advanced Baseline Imager (ABI) data with Satpy

Introduction

Advanced Baseline Imager (ABI) data with Satpy