08 | July | 2019 | GNC-A Blog

This is the third part of this series. Please find the other parts at the following links:

Please find below other Python example scripts to generate GOES-16 / 17 RGB’s:

Day Cloud Convection RGB (Quick Guide)

Hurricane Barbara – GOES-16 – July 3rd 2019, 17:00 UTC – Day Cloud Convection RGB created with Python

#######################################################################################################
# LICENSE
# Copyright (C) 2019 - INPE - NATIONAL INSTITUTE FOR SPACE RESEARCH
# This program is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version.
# This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details.
# You should have received a copy of the GNU General Public License along with this program. If not, see http://www.gnu.org/licenses/.
#######################################################################################################

# Python Script: Day Cloud Convection
# Quick Guide: http://rammb.cira.colostate.edu/training/visit/quick_guides/QuickGuide_DayCloudConvectionRGB_final.pdf

# Required modules
import matplotlib.pyplot as plt           # Import the Matplotlib package
import numpy as np                        # Import the Numpy package
from netCDF4 import Dataset               # Import the NetCDF Python interface
from remap import remap                   # Import the Remap function
from mpl_toolkits.basemap import Basemap  # Import the Basemap utility

# File to read
image1 = "OR_ABI-L2-CMIPF-M6C02_G16_s20191841700283_e20191841709591_c20191841710072-114300_0.nc"
# Read the file using the NetCDF library
file1 = Dataset(image1)
# Desired resolution
resolution = 2
# Desired extent
extent = [-138, 5.0, -112, 24.0]
# Remap and get the reflectances
grid = remap(image1, extent, resolution)
# Read the data as an array
data1 = grid.ReadAsArray()

# File to read
image2 = "OR_ABI-L2-CMIPF-M6C13_G16_s20191841700283_e20191841710002_c20191841710081.nc"
# Read the file using the NetCDF library
file2 = Dataset(image2)
# Desired resolution
resolution = 2
# Desired extent
extent = [-138, 5.0, -112, 24.0]
# Remap and get the brightness temperatures
grid = remap(image2, extent, resolution)
# Read the data as an array
data2 = grid.ReadAsArray()
# Convert to Celsius
data2 = data2 - 273.15

# Define the size of the saved picture=================================================================
#plt.figure(figsize=(10,6))
DPI = 150
fig = plt.figure(figsize=(data1.shape[1]/float(DPI), data1.shape[0]/float(DPI)), frameon=False, dpi=DPI)
ax = plt.Axes(fig, [0., 0., 1., 1.])
ax.set_axis_off()
fig.add_axes(ax)
ax = plt.axis('off')
# ====================================================================================================

# RGB Components
R = data1
G = data1
B = data2

# Minimuns and Maximuns
Rmin = 0.0
Rmax = 1.0

Gmin = 0.0
Gmax = 1.0

Bmin = 49.85
Bmax = -70.15

R[RRmax] = Rmax

G[GGmax] = Gmax

B[B>Bmin] = Bmin
B[B<Bmax] = Bmax

# Choose the gamma
gamma_R = 1.7
gamma_G = 1.7
gamma_B = 1

# Normalize the data
R = ((R - Rmin) / (Rmax - Rmin)) ** (1/gamma_R)
G = ((G - Gmin) / (Gmax - Gmin)) ** (1/gamma_G)
B = ((B - Bmin) / (Bmax - Bmin)) ** (1/gamma_B)

# Create the RGB
RGB = np.stack([R, G, B], axis=2)

# ====================================================================================================

# Create the basemap
bmap = Basemap(llcrnrlon=extent[0], llcrnrlat=extent[1], urcrnrlon=extent[2], urcrnrlat=extent[3], epsg=4326, resolution = 'i')

# Plot the image
bmap.imshow(RGB, origin='upper')

# Insert a shapefile
bmap.readshapefile('estados_2010','estados_2010',linewidth=0.3,color='white')

# Configure the map
bmap.drawcoastlines(linewidth=0.6, linestyle='solid', color='gold')
bmap.drawcountries(linewidth=0.6, linestyle='solid', color='orange')
bmap.drawparallels(np.arange(-90.0, 90.0, 5.0), dashes = [4 ,4], linewidth=0.8, color='cyan', labels=[True,False,False,False], fmt='%g', labelstyle="+/-", xoffset= 0.00, yoffset= 0.00, size=7)
bmap.drawmeridians(np.arange(0.0, 360.0, 5.0), dashes = [4,4], linewidth=0.8, color='cyan', labels=[False,False,True,False], fmt='%g', labelstyle="+/-", xoffset= 0.00, yoffset= 0.00, size=7)

# Save the image
plt.savefig('RGB_Day_Cloud_Convection.png', dpi=DPI, pad_inches=0)

24-Hour Microphysics RGB (Quick Guide)

Hurricane Barbara – GOES-16 – July 3rd 2019, 17:00 UTC – 24-hour Microphysics RGB created with Python

#######################################################################################################
# LICENSE
# Copyright (C) 2019 - INPE - NATIONAL INSTITUTE FOR SPACE RESEARCH
# This program is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version.
# This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details.
# You should have received a copy of the GNU General Public License along with this program. If not, see http://www.gnu.org/licenses/.
#######################################################################################################

# Python Script: 24-hour Microphysics
# Quick Guide: http://eumetrain.org/rgb_quick_guides/quick_guides/24MicroRGB.pdf

# Required modules
import matplotlib.pyplot as plt           # Import the Matplotlib package
import numpy as np                        # Import the Numpy package
from netCDF4 import Dataset               # Import the NetCDF Python interface
from remap import remap                   # Import the Remap function
from mpl_toolkits.basemap import Basemap  # Import the Basemap utility

# File to read
image1 = "OR_ABI-L2-CMIPF-M6C15_G16_s20191841700283_e20191841709596_c20191841710080.nc"
# Read the file using the NetCDF library
file1 = Dataset(image1)
# Desired resolution
resolution = 2
# Desired extent
extent = [-138, 5.0, -112, 24.0]
# Remap and get the brightness temperatures
grid = remap(image1, extent, resolution)
# Read the data as an array
data1 = grid.ReadAsArray()
# Convert to Celsius
data1 = data1 - 273.15

# File to read
image2 = "OR_ABI-L2-CMIPF-M6C13_G16_s20191841700283_e20191841710002_c20191841710081.nc"
# Read the file using the NetCDF library
file2 = Dataset(image2)
# Desired resolution
resolution = 2
# Desired extent
extent = [-138, 5.0, -112, 24.0]
# Remap and get the brightness temperatures
grid = remap(image2, extent, resolution)
# Read the data as an array
data2 = grid.ReadAsArray()
# Convert to Celsius
data2 = data2 - 273.15

# File to read
image3 = "OR_ABI-L2-CMIPF-M6C11_G16_s20191841700283_e20191841709591_c20191841710080.nc"
# Read the file using the NetCDF library
file3 = Dataset(image3)
# Desired resolution
resolution = 2
# Desired extent
extent = [-138, 5.0, -112, 24.0]
# Remap and get the brightness temperatures
grid = remap(image3, extent, resolution)
# Read the data as an array
data3 = grid.ReadAsArray()
# Convert to Celsius
data3 = data3 - 273.15

# Define the size of the saved picture=================================================================
#plt.figure(figsize=(10,6))
DPI = 150
fig = plt.figure(figsize=(data1.shape[1]/float(DPI), data1.shape[0]/float(DPI)), frameon=False, dpi=DPI)
ax = plt.Axes(fig, [0., 0., 1., 1.])
ax.set_axis_off()
fig.add_axes(ax)
ax = plt.axis('off')
# ====================================================================================================

# RGB Components
R = data1 - data2
G = data2 - data3
B = data2

# Minimuns and Maximuns
Rmin = -4.0
Rmax = 2.0

Gmin = 0.0
Gmax = 6.0

Bmin = -25.10
Bmax = 29.8

R[RRmax] = Rmax

G[GGmax] = Gmax

B[BBmax] = Bmax

# Choose the gamma
gamma_R = 1
gamma_G = 1.2
gamma_B = 1

# Normalize the data
R = ((R - Rmin) / (Rmax - Rmin)) ** (1/gamma_R)
G = ((G - Gmin) / (Gmax - Gmin)) ** (1/gamma_G)
B = ((B - Bmin) / (Bmax - Bmin)) ** (1/gamma_B)

# Create the RGB
RGB = np.stack([R, G, B], axis=2)

# ====================================================================================================
# Create the basemap
bmap = Basemap(llcrnrlon=extent[0], llcrnrlat=extent[1], urcrnrlon=extent[2], urcrnrlat=extent[3], epsg=4326, resolution = 'i')

# Plot the image
bmap.imshow(RGB, origin='upper')

# Insert a shapefile
bmap.readshapefile('estados_2010','estados_2010',linewidth=0.6,color='white')

# Configure the map
bmap.drawcoastlines(linewidth=0.6, linestyle='solid', color='gold')
bmap.drawcountries(linewidth=0.6, linestyle='solid', color='orange')
bmap.drawparallels(np.arange(-90.0, 90.0, 5.0), dashes = [4 ,4], linewidth=0.8, color='cyan', labels=[True,False,False,False], fmt='%g', labelstyle="+/-", xoffset= 0.00, yoffset= 0.00, size=7)
bmap.drawmeridians(np.arange(0.0, 360.0, 5.0), dashes = [4,4], linewidth=0.8, color='cyan', labels=[False,False,True,False], fmt='%g', labelstyle="+/-", xoffset= 0.00, yoffset= 0.00, size=7)

# Save the image
plt.savefig('RGB_24h_Microphysics.png', dpi=DPI, pad_inches=0)

Night Microphysics RGB (Quick Guide)

Hurricane Barbara – GOES-16 – July 3rd 2019, 17:00 UTC – Night Microphysics RGB created with Python

#######################################################################################################
# LICENSE
# Copyright (C) 2019 - INPE - NATIONAL INSTITUTE FOR SPACE RESEARCH
# This program is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version.
# This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details.
# You should have received a copy of the GNU General Public License along with this program. If not, see http://www.gnu.org/licenses/.
#######################################################################################################

# Python Script: Night Microphysics
# Quick Guide: http://rammb.cira.colostate.edu/training/visit/quick_guides/QuickGuide_GOESR_NtMicroRGB_final.pdf

# Required modules
import matplotlib.pyplot as plt           # Import the Matplotlib package
import numpy as np                        # Import the Numpy package
from netCDF4 import Dataset               # Import the NetCDF Python interface
from remap import remap                   # Import the Remap function
from mpl_toolkits.basemap import Basemap  # Import the Basemap utility

# File to read
image1 = "OR_ABI-L2-CMIPF-M6C15_G16_s20191850300287_e20191850310001_c20191850310084.nc"
# Read the file using the NetCDF library
file1 = Dataset(image1)
# Desired resolution
resolution = 2
# Desired extent
extent = [-138, 5.0, -112, 24.0]
# Remap and get the brightness temperatures
grid = remap(image1, extent, resolution)
# Read the data as an array
data1 = grid.ReadAsArray()
# Convert to Celsius
data1 = data1 - 273.15

# File to read
image2 = "OR_ABI-L2-CMIPF-M6C13_G16_s20191850300287_e20191850310006_c20191850310084.nc"
# Read the file using the NetCDF library
file2 = Dataset(image2)
# Desired resolution
resolution = 2
# Desired extent
extent = [-138, 5.0, -112, 24.0]
# Remap and get the brightness temperatures
grid = remap(image2, extent, resolution)
# Read the data as an array
data2 = grid.ReadAsArray()
# Convert to Celsius
data2 = data2 - 273.15

# File to read
image3 = "OR_ABI-L2-CMIPF-M6C07_G16_s20191850300287_e20191850310006_c20191850310078.nc"
# Read the file using the NetCDF library
file3 = Dataset(image3)
# Desired resolution
resolution = 2
# Desired extent
extent = [-138, 5.0, -112, 24.0]
# Remap and get the brightness temperatures
grid = remap(image3, extent, resolution)
# Read the data as an array
data3 = grid.ReadAsArray()
# Convert to Celsius
data3 = data3 - 273.15

# Define the size of the saved picture=================================================================
#plt.figure(figsize=(10,6))
DPI = 150
fig = plt.figure(figsize=(data1.shape[1]/float(DPI), data1.shape[0]/float(DPI)), frameon=False, dpi=DPI)
ax = plt.Axes(fig, [0., 0., 1., 1.])
ax.set_axis_off()
fig.add_axes(ax)
ax = plt.axis('off')
# ====================================================================================================

# RGB Components
R = data1 - data2
G = data2 - data3
B = data2

# Minimuns and Maximuns
Rmin = -6.7
Rmax = 2.6

Gmin = -3.1
Gmax = 5.2

Bmin = -29.60
Bmax = 19.5

R[RRmax] = Rmax

G[GGmax] = Gmax

B[BBmax] = Bmax

# Choose the gamma
gamma_R = 1
gamma_G = 1
gamma_B = 1

# Normalize the data
R = ((R - Rmin) / (Rmax - Rmin)) ** (1/gamma_R)
G = ((G - Gmin) / (Gmax - Gmin)) ** (1/gamma_G)
B = ((B - Bmin) / (Bmax - Bmin)) ** (1/gamma_B)

# Create the RGB
RGB = np.stack([R, G, B], axis=2)

# ====================================================================================================

# Create the basemap
bmap = Basemap(llcrnrlon=extent[0], llcrnrlat=extent[1], urcrnrlon=extent[2], urcrnrlat=extent[3], epsg=4326, resolution = 'i')

# Plot the image
bmap.imshow(RGB, origin='upper')

# Insert a shapefile
bmap.readshapefile('estados_2010','estados_2010',linewidth=0.6,color='white')

# Configure the map
bmap.drawcoastlines(linewidth=0.6, linestyle='solid', color='gold')
bmap.drawcountries(linewidth=0.6, linestyle='solid', color='orange')
bmap.drawparallels(np.arange(-90.0, 90.0, 5.0), dashes = [4 ,4], linewidth=0.8, color='cyan', labels=[True,False,False,False], fmt='%g', labelstyle="+/-", xoffset= 0.00, yoffset= 0.00, size=7)
bmap.drawmeridians(np.arange(0.0, 360.0, 5.0), dashes = [4,4], linewidth=0.8, color='cyan', labels=[False,False,True,False], fmt='%g', labelstyle="+/-", xoffset= 0.00, yoffset= 0.00, size=7)

# Save the image
plt.savefig('RGB_Night_Microphysics.png', dpi=DPI, pad_inches=0)

Cloud Phase RGB (Quick Guide)

Hurricane Barbara – GOES-16 – July 3rd 2019, 17:00 UTC – Cloud Phase RGB created with Python

#######################################################################################################
# LICENSE
# Copyright (C) 2019 - INPE - NATIONAL INSTITUTE FOR SPACE RESEARCH
# This program is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version.
# This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details.
# You should have received a copy of the GNU General Public License along with this program. If not, see http://www.gnu.org/licenses/.
#######################################################################################################

# Python Script: Day Land Cloud RGB
# Quick Guide: http://eumetrain.org/rgb_quick_guides/quick_guides/CloudPhaseRGB.pdf

# File to read
image1 = "OR_ABI-L2-CMIPF-M6C05_G16_s20191841700283_e20191841709591_c20191841710071.nc"
# Read the file using the NetCDF library
file1 = Dataset(image1)
# Desired resolution
resolution = 2
# Desired extent
extent = [-138, 5.0, -112, 24.0]
# Remap and get the reflectances
grid = remap(image1, extent, resolution)
# Read the data as an array
data1 = grid.ReadAsArray()

# File to read
image2 = "OR_ABI-L2-CMIPF-M6C06_G16_s20191841700283_e20191841709596_c20191841710062.nc"
# Read the file using the NetCDF library
file2 = Dataset(image2)
# Desired resolution
resolution = 2
# Desired extent
extent = [-138, 5.0, -112, 24.0]
# Remap and get the reflectances
grid = remap(image2, extent, resolution)
# Read the data as an array
data2 = grid.ReadAsArray()

# File to read
image3 = "OR_ABI-L2-CMIPF-M6C02_G16_s20191841700283_e20191841709591_c20191841710072-114300_0.nc"
# Read the file using the NetCDF library
file3 = Dataset(image3)
# Desired resolution
resolution = 2
# Desired extent
extent = [-138, 5.0, -112, 24.0]
# Remap and get the reflectances
grid = remap(image3, extent, resolution)
# Read the data as an array
data3 = grid.ReadAsArray()

# Calculates the solar zenith angle
from pyorbital import astronomy
from datetime import datetime

lat = np.linspace(extent[3], extent[1], data1.shape[0])
lon = np.linspace(extent[0], extent[2], data1.shape[1])

xx,yy = np.meshgrid(lon,lat)
lats = xx.reshape(data1.shape[0], data1.shape[1])
lons = yy.reshape(data1.shape[0], data1.shape[1])

# Get the year month day hour and minute to apply the zenith correction
utc_time = datetime(2019, 7, 3, 17, 00)
sun_zenith = np.zeros((data1.shape[0], data1.shape[1]))
sun_zenith = astronomy.sun_zenith_angle(utc_time, lats, lons)

# Apply the sun zenith correction
data1 = (data1)/(np.cos(np.deg2rad(sun_zenith)))
data2 = (data2)/(np.cos(np.deg2rad(sun_zenith)))
data3 = (data3)/(np.cos(np.deg2rad(sun_zenith)))

# Define the size of the saved picture=================================================================
#plt.figure(figsize=(10,6))
DPI = 150
fig = plt.figure(figsize=(data1.shape[1]/float(DPI), data1.shape[0]/float(DPI)), frameon=False, dpi=DPI)
ax = plt.Axes(fig, [0., 0., 1., 1.])
ax.set_axis_off()
fig.add_axes(ax)
ax = plt.axis('off')
# ====================================================================================================

# RGB Components
R = data1
G = data2
B = data3

# Minimuns and Maximuns
Rmin = 0
Rmax = 1

Gmin = 0
Gmax = 1

Bmin = 0
Bmax = 1

R[RRmax] = Rmax

G[GGmax] = Gmax

B[BBmax] = Bmax

# Choose the gamma
gamma_R = 1
gamma_G = 1
gamma_B = 1

# Normalize the data
R = ((R - Rmin) / (Rmax - Rmin)) ** (1/gamma_R)
G = ((G - Gmin) / (Gmax - Gmin)) ** (1/gamma_G)
B = ((B - Bmin) / (Bmax - Bmin)) ** (1/gamma_B)

# Create the RGB
RGB = np.stack([R, G, B], axis=2)

# ====================================================================================================
# Create the basemap
bmap = Basemap(llcrnrlon=extent[0], llcrnrlat=extent[1], urcrnrlon=extent[2], urcrnrlat=extent[3], epsg=4326, resolution = 'i')

# Plot the image
bmap.imshow(RGB, origin='upper')

# Insert a shapefile
bmap.readshapefile('estados_2010','estados_2010',linewidth=0.3,color='white')

# Configure the map
bmap.drawcoastlines(linewidth=0.6, linestyle='solid', color='gold')
bmap.drawcountries(linewidth=0.6, linestyle='solid', color='orange')
bmap.drawparallels(np.arange(-90.0, 90.0, 5.0), dashes = [4 ,4], linewidth=0.8, color='cyan', labels=[True,False,False,False], fmt='%g', labelstyle="+/-", xoffset= 0.00, yoffset= 0.00, size=7)
bmap.drawmeridians(np.arange(0.0, 360.0, 5.0), dashes = [4,4], linewidth=0.8, color='cyan', labels=[False,False,True,False], fmt='%g', labelstyle="+/-", xoffset= 0.00, yoffset= 0.00, size=7)

# Save the image
plt.savefig('RGB_Cloud_Phase.png', dpi=DPI, pad_inches=0)

SO2 RGB (Quick Guide)

Hurricane Barbara – GOES-16 – July 3rd 2019, 17:00 UTC – SO2 RGB created with Python

#######################################################################################################
# LICENSE
# Copyright (C) 2019 - INPE - NATIONAL INSTITUTE FOR SPACE RESEARCH
# This program is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version.
# This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details.
# You should have received a copy of the GNU General Public License along with this program. If not, see http://www.gnu.org/licenses/.
#######################################################################################################

# Python Script: SO2
# Quick Guide: http://rammb.cira.colostate.edu/training/visit/quick_guides/Quick_Guide_SO2_RGB.pdf

# Required modules
import matplotlib.pyplot as plt           # Import the Matplotlib package
import numpy as np                        # Import the Numpy package
from netCDF4 import Dataset               # Import the NetCDF Python interface
from remap import remap                   # Import the Remap function
from mpl_toolkits.basemap import Basemap  # Import the Basemap utility

# File to read
image1 = "OR_ABI-L2-CMIPF-M6C09_G16_s20191841700283_e20191841709596_c20191841710081.nc"
# Read the file using the NetCDF library
file1 = Dataset(image1)
# Desired resolution
resolution = 2
# Desired extent
extent = [-138, 5.0, -112, 24.0]
# Remap and get the brightness temperatures
grid = remap(image1, extent, resolution)
# Read the data as an array
data1 = grid.ReadAsArray()
# Convert to Celsius
data1 = data1 - 273.15

# File to read
image2 = "OR_ABI-L2-CMIPF-M6C10_G16_s20191841700283_e20191841710002_c20191841710078.nc"
# Read the file using the NetCDF library
file2 = Dataset(image2)
# Desired resolution
resolution = 2
# Desired extent
extent = [-138, 5.0, -112, 24.0]
# Remap and get the brightness temperatures
grid = remap(image2, extent, resolution)
# Read the data as an array
data2 = grid.ReadAsArray()
# Convert to Celsius
data2 = data2 - 273.15

# File to read
image3 = "OR_ABI-L2-CMIPF-M6C13_G16_s20191841700283_e20191841710002_c20191841710081.nc"
# Read the file using the NetCDF library
file3 = Dataset(image3)
# Desired resolution
resolution = 2
# Desired extent
extent = [-138, 5.0, -112, 24.0]
# Remap and get the brightness temperatures
grid = remap(image3, extent, resolution)
# Read the data as an array
data3 = grid.ReadAsArray()
# Convert to Celsius
data3 = data3 - 273.15

# File to read
image4 = "OR_ABI-L2-CMIPF-M6C11_G16_s20191841700283_e20191841709591_c20191841710080.nc"
# Read the file using the NetCDF library
file4 = Dataset(image3)
# Desired resolution
resolution = 2
# Desired extent
extent = [-138, 5.0, -112, 24.0]
# Remap and get the brightness temperatures
grid = remap(image3, extent, resolution)
# Read the data as an array
data4 = grid.ReadAsArray()
# Convert to Celsius
data4 = data4 - 273.15

# Define the size of the saved picture=================================================================
#plt.figure(figsize=(10,6))
DPI = 150
fig = plt.figure(figsize=(data1.shape[1]/float(DPI), data1.shape[0]/float(DPI)), frameon=False, dpi=DPI)
ax = plt.Axes(fig, [0., 0., 1., 1.])
ax.set_axis_off()
fig.add_axes(ax)
ax = plt.axis('off')
# ====================================================================================================

# RGB Components
R = data1 - data2
G = data3 - data4
B = data3

# Minimuns and Maximuns
Rmin = -4.0
Rmax = 2.0

Gmin = -4.0
Gmax = 5.0

Bmin = -30.1
Bmax = 29.8

R[RRmax] = Rmax

G[GGmax] = Gmax

B[BBmax] = Bmax

# Choose the gamma
gamma_R = 1
gamma_G = 1.2
gamma_B = 1

# Normalize the data
R = ((R - Rmin) / (Rmax - Rmin)) ** (1/gamma_R)
G = ((G - Gmin) / (Gmax - Gmin)) ** (1/gamma_G)
B = ((B - Bmin) / (Bmax - Bmin)) ** (1/gamma_B)

# Create the RGB
RGB = np.stack([R, G, B], axis=2)

# ====================================================================================================

# Create the basemap
bmap = Basemap(llcrnrlon=extent[0], llcrnrlat=extent[1], urcrnrlon=extent[2], urcrnrlat=extent[3], epsg=4326, resolution = 'i')

# Plot the image
bmap.imshow(RGB, origin='upper')

# Insert a shapefile
bmap.readshapefile('estados_2010','estados_2010',linewidth=0.6,color='white')

# Configure the map
bmap.drawcoastlines(linewidth=0.6, linestyle='solid', color='gold')
bmap.drawcountries(linewidth=0.6, linestyle='solid', color='orange')
bmap.drawparallels(np.arange(-90.0, 90.0, 5.0), dashes = [4 ,4], linewidth=0.8, color='cyan', labels=[True,False,False,False], fmt='%g', labelstyle="+/-", xoffset= 0.00, yoffset= 0.00, size=7)
bmap.drawmeridians(np.arange(0.0, 360.0, 5.0), dashes = [4,4], linewidth=0.8, color='cyan', labels=[False,False,True,False], fmt='%g', labelstyle="+/-", xoffset= 0.00, yoffset= 0.00, size=7)

# Save the image
plt.savefig('RGB_SO2.png', dpi=DPI, pad_inches=0)

Python Script Examples to Generate GOES-16 / 17 RGB’s: Part IIIhttps://t.co/z6DFImPao1 pic.twitter.com/5Ve7AV3OMF

— Diego Souza 📡🛰🌎 (@DrodSouza) July 8, 2019

	Diego Souza on Converting L1b Radiances to BT…
	Gloria Lucia Arango… on Converting L1b Radiances to BT…
	typhoonvery801b96786… on The Journey to Create a Nice I…
	typhoonvery801b96786… on Python Script Examples to Gene…
	Diego Souza on Python Script Examples to Gene…
	typhoonvery801b96786… on Python Script Examples to Gene…
	Diego Souza on Python Script Examples to Gene…
	enrique on Python Script Examples to Gene…
	Catiaadao on Using Conda Environments With…
	Joyce Casanova on GEONETCast + Python Training F…
	Diego Souza on GEONETCast + Python Training F…
	Joyce Casanova on GEONETCast + Python Training F…
	John Joseph Cornicel… on First Images of MTG-I1
	Diego Souza on AdBlock Extension
	Will Abarca on AdBlock Extension

	Diego Souza on Converting L1b Radiances to BT…
	Gloria Lucia Arango… on Converting L1b Radiances to BT…
	typhoonvery801b96786… on The Journey to Create a Nice I…
	typhoonvery801b96786… on Python Script Examples to Gene…
	Diego Souza on Python Script Examples to Gene…

GNC-A Blog

All about GEONETCast-Americas

Daily Archives: 2019-07-08

Python Script Examples to Generate GOES-16 / 17 RGB’s: Part III