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g16_rgb_cloud_phase_cartopy.py
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g16_rgb_cloud_phase_cartopy.py
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#######################################################################################################
# LICENSE
# Copyright (C) 2019 - INPE - NATIONAL INSTITUTE FOR SPACE RESEARCH - BRAZIL
# 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
#------------------------------------------------------------------------------------------------------
#------------------------------------------------------------------------------------------------------
# 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
import cartopy, cartopy.crs as ccrs # Plot maps
import cartopy.io.shapereader as shpreader # Import shapefiles
import math # Import math
from pyproj import Proj # Python interface to PROJ (cartographic projections and coordinate transformations library).
from datetime import datetime, timedelta # Library to convert julian day to dd-mm-yyyy
#------------------------------------------------------------------------------------------------------
#------------------------------------------------------------------------------------------------------
# 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 = 8
# Read the resolution
band_resolution_km = getattr(file1, 'spatial_resolution')
band_resolution_km = float(band_resolution_km[:band_resolution_km.find("km")])
# Division factor to reduce image size
f = math.ceil(float(resolution / band_resolution_km))
# Read the central longitude
longitude = file1.variables['goes_imager_projection'].longitude_of_projection_origin
# Calculate the image extent
h = file1.variables['goes_imager_projection'].perspective_point_height
x = file1.variables['x_image_bounds'] * h
y = file1.variables['y_image_bounds'] * h
# Reading the file time and date
add_seconds = int(file1.variables['time_bounds'][0])
date = datetime(2000,1,1,12) + timedelta(seconds=add_seconds)
date_formated = date.strftime('%Y-%m-%d %H:%M UTC')
date_file = date.strftime('%Y%m%d%H%M')
year = date.strftime('%Y')
month = date.strftime('%m')
day = date.strftime('%d')
hour = date.strftime('%H')
minutes = date.strftime('%M')
# Convert to Celsius
data1 = file1.variables['CMI'][:,:][::f ,::f]
#------------------------------------------------------------------------------------------------------
#------------------------------------------------------------------------------------------------------
# File to read
image2 = "OR_ABI-L2-CMIPF-M6C06_G16_s20191841700283_e20191841709596_c20191841710062.nc"
# Read the file using the NetCDF library
file2 = Dataset(image2)
# Read the resolution
band_resolution_km = getattr(file2, 'spatial_resolution')
band_resolution_km = float(band_resolution_km[:band_resolution_km.find("km")])
# Division factor to reduce image size
f = math.ceil(float(resolution / band_resolution_km))
# Convert to Celsius
data2 = file2.variables['CMI'][:,:][::f ,::f]
#------------------------------------------------------------------------------------------------------
#------------------------------------------------------------------------------------------------------
# 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)
# Read the resolution
band_resolution_km = getattr(file3, 'spatial_resolution')
band_resolution_km = float(band_resolution_km[:band_resolution_km.find("km")])
# Division factor to reduce image size
f = math.ceil(float(resolution / band_resolution_km))
# Convert to Celsius
data3 = file3.variables['CMI'][:,:][::f ,::f]
#------------------------------------------------------------------------------------------------------
#------------------------------------------------------------------------------------------------------
# Calculates the solar zenith angle
from pyorbital import astronomy
from datetime import datetime
print("Calculating the lons and lats...")
# Satellite height
sat_h = file1.variables['goes_imager_projection'].perspective_point_height
# Satellite longitude
sat_lon = file1.variables['goes_imager_projection'].longitude_of_projection_origin
# Satellite sweep
sat_sweep = file1.variables['goes_imager_projection'].sweep_angle_axis
# Read the resolution
band_resolution_km = getattr(file1, 'spatial_resolution')
band_resolution_km = float(band_resolution_km[:band_resolution_km.find("km")])
# Division factor to reduce image size
f = math.ceil(float(resolution / band_resolution_km))
# The projection x and y coordinates equals
# the scanning angle (in radians) multiplied by the satellite height (http://proj4.org/projections/geos.html)
X = file1.variables['x'][:][::f] * sat_h
Y = file1.variables['y'][:][::f] * sat_h
# map object with pyproj
p = Proj(proj='geos', h=sat_h, lon_0=sat_lon, sweep=sat_sweep, a=6378137.0)
# Convert map points to latitude and longitude with the magic provided by Pyproj
XX, YY = np.meshgrid(X, Y)
lons, lats = p(XX, YY, inverse=True)
# Get the year month day hour and minute to apply the zenith correction
utc_time = datetime(int(year), int(month), int(day), int(hour), int(minutes))
sun_zenith = np.zeros((data1.shape[0], data1.shape[1]))
sun_zenith = astronomy.sun_zenith_angle(utc_time, lons, lats)
# 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)))
#------------------------------------------------------------------------------------------------------
#------------------------------------------------------------------------------------------------------
# RGB Components
R = data1
G = data2
B = data3
# Minimuns and Maximuns
Rmin = 0
Rmax = 0.5
Gmin = 0
Gmax = 0.5
Bmin = 0
Bmax = 1
R[R<Rmin] = Rmin
R[R>Rmax] = Rmax
G[G<Gmin] = Gmin
G[G>Gmax] = Gmax
B[B<Bmin] = Bmin
B[B>Bmax] = 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)
# Eliminate values outside the globe
mask = (RGB == [R[0,0],G[0,0],B[0,0]]).all(axis=2)
RGB[mask] = np.nan
#------------------------------------------------------------------------------------------------------
#------------------------------------------------------------------------------------------------------
# Plot configuration
plot_config = {
"resolution": band_resolution_km,
"dpi": 150,
"states_color": 'grey', "states_width": data1.shape[0] * 0.00006,
"countries_color": 'white', "countries_width": data1.shape[0] * 0.00012,
"continents_color": 'white', "continents_width": data1.shape[0] * 0.00025,
"grid_color": 'white', "grid_width": data1.shape[0] * 0.00025, "grid_interval": 10.0,
"title_text": "GOES-16 CLOUD PHASE RGB", "title_size": int(data1.shape[1] * 0.005), "title_x_offset": int(data1.shape[1] * 0.01), "title_y_offset": data1.shape[0] - int(data1.shape[0] * 0.016),
"file_name_id_1": "G16", "file_name_id_2": "CPHRGB"
}
#------------------------------------------------------------------------------------------------------
#------------------------------------------------------------------------------------------------------
# Choose the plot size (width x height, in inches)
fig = plt.figure(figsize=(data1.shape[1]/float(plot_config["dpi"]), data1.shape[0]/float(plot_config["dpi"])), dpi=plot_config["dpi"])
# Define the projection
proj = ccrs.Geostationary(central_longitude=longitude, satellite_height=h)
img_extent = (x.min(), x.max(), y.min(), y.max())
# Use the Geostationary projection in cartopy
ax = plt.axes([0, 0, 1, 1], projection=proj)
# Plot the image
img = ax.imshow(RGB, origin='upper', extent=img_extent, zorder=3)
# Add countries
shapefile = list(shpreader.Reader('ne_50m_admin_0_countries.shp').geometries())
ax.add_geometries(shapefile, ccrs.PlateCarree(), edgecolor=plot_config["countries_color"],facecolor='none', linewidth=plot_config["countries_width"], zorder=4)
# Add continents
shapefile = list(shpreader.Reader('ne_10m_coastline.shp').geometries())
ax.add_geometries(shapefile, ccrs.PlateCarree(), edgecolor=plot_config["continents_color"],facecolor='none', linewidth=plot_config["continents_width"], zorder=5)
# Add coastlines, borders and gridlines
ax.gridlines(color=plot_config["grid_color"], alpha=0.5, linestyle='--', linewidth=plot_config["grid_width"], xlocs=np.arange(-180, 180, plot_config["grid_interval"]), ylocs=np.arange(-180, 180, plot_config["grid_interval"]), draw_labels=False, zorder=6)
# Remove the outline border
ax.outline_patch.set_visible(False)
# Add a title
plt.annotate(plot_config["title_text"] + " " + date_formated , xy=(plot_config["title_x_offset"], plot_config["title_y_offset"]), xycoords='figure pixels', fontsize=plot_config["title_size"], fontweight='bold', color='white', bbox=dict(boxstyle="round",fc=(0.0, 0.0, 0.0), ec=(1., 1., 1.)), zorder=7)
# Save the image
plt.savefig(plot_config["file_name_id_1"] + "_" + plot_config["file_name_id_2"] + "_" + date_file + '.png', bbox_inches='tight', pad_inches=0, facecolor='black')
#------------------------------------------------------------------------------------------------------
#------------------------------------------------------------------------------------------------------