script_24.py

#-----------------------------------------------------------------------------------------------------------
# INPE / CPTEC - Training: Python and GOES-R Imagery: Script 24 - Satellite + NWP Plot (RGB)
# Author: Diego Souza
#-----------------------------------------------------------------------------------------------------------
# Required modules
from netCDF4 import Dataset                         # Read / Write NetCDF4 files
from osgeo import gdal                              # Python bindings for GDAL
import matplotlib.pyplot as plt                     # Plotting library
import cartopy, cartopy.crs as ccrs                 # Plot maps
import cartopy.io.shapereader as shpreader          # Import shapefiles
import os                                           # Miscellaneous operating system interfaces
import numpy as np                                  # Scientific computing with Python
from matplotlib import cm                           # Colormap handling utilities
from datetime import timedelta, date, datetime      # Basic Dates and time types
from utilities import download_CMI                  # Our function for download
from utilities import reproject                     # Our function for reproject
from utilities import loadCPT                       # Import the CPT convert function
import pygrib                                       # Provides a high-level interface to the ECWMF ECCODES C library for reading GRIB files
gdal.PushErrorHandler('CPLQuietErrorHandler')       # Ignore GDAL warnings
#-----------------------------------------------------------------------------------------------------------

def plot_maxmin_points(lon, lat, data, extrema, nsize, symbol, color='k',
                       plotValue=True, transform=None):
    """
    This function will find and plot relative maximum and minimum for a 2D grid. The function
    can be used to plot an H for maximum values (e.g., High pressure) and an L for minimum
    values (e.g., low pressue). It is best to used filetered data to obtain  a synoptic scale
    max/min value. The symbol text can be set to a string value and optionally the color of the
    symbol and any plotted value can be set with the parameter color
    lon = plotting longitude values (2D)
    lat = plotting latitude values (2D)
    data = 2D data that you wish to plot the max/min symbol placement
    extrema = Either a value of max for Maximum Values or min for Minimum Values
    nsize = Size of the grid box to filter the max and min values to plot a reasonable number
    symbol = String to be placed at location of max/min value
    color = String matplotlib colorname to plot the symbol (and numerica value, if plotted)
    plot_value = Boolean (True/False) of whether to plot the numeric value of max/min point
    The max/min symbol will be plotted on the current axes within the bounding frame
    (e.g., clip_on=True)
    """
    from scipy.ndimage.filters import maximum_filter, minimum_filter

    if (extrema == 'max'):
        data_ext = maximum_filter(data, nsize, mode='nearest')
    elif (extrema == 'min'):
        data_ext = minimum_filter(data, nsize, mode='nearest')
    else:
        raise ValueError('Value for hilo must be either max or min')

    mxy, mxx = np.where(data_ext == data)

    for i in range(len(mxy)):
        txt1 = ax.annotate(symbol, xy=(lon[mxy[i], mxx[i]], lat[mxy[i], mxx[i]]), xycoords=ccrs.PlateCarree()._as_mpl_transform(ax), color=color, size=20,
                clip_on=True, annotation_clip=True, horizontalalignment='center', verticalalignment='center',
                transform=ccrs.PlateCarree())

        txt2 = ax.annotate('\n' + str(int(data[mxy[i], mxx[i]])), xy=(lon[mxy[i], mxx[i]], lat[mxy[i], mxx[i]]), xycoords=ccrs.PlateCarree()._as_mpl_transform(ax), 
                color=color, size=10, clip_on=True, annotation_clip=True, fontweight='bold', horizontalalignment='center', verticalalignment='top',
                transform=ccrs.PlateCarree())  

#----------------------------------------------------------------------------------------------------------- 

# Select the extent [min. lon, min. lat, max. lon, max. lat]
extent = [-93.0, -60.00, -25.00, 18.00]

# Input and output directories
input = "Samples"; os.makedirs(input, exist_ok=True)
output = "Output"; os.makedirs(output, exist_ok=True)

# Datetime to process (today in this example, to match the GFS date)
#date = datetime.today().strftime('%Y%m%d')
#yyyymmddhhmn = date + '0000'
yyyymmddhhmn = '202107020000' # CHANGE THIS DATE TO THE SAME DATE OF YOUR NWP DATA

#-----------------------------------------------------------------------------------------------------------
# Download the ABI file
file_ir_8 = download_CMI(yyyymmddhhmn, 8, input)

# Download the ABI file
file_ir_10 = download_CMI(yyyymmddhhmn, 10, input)

# Download the ABI file
file_ir_12 = download_CMI(yyyymmddhhmn, 12, input)

# Download the ABI file
file_ir_13 = download_CMI(yyyymmddhhmn, 13, input)
#-----------------------------------------------------------------------------------------------------------
# Variable
var = 'CMI'

# Open the file
img = gdal.Open(f'NETCDF:{input}/{file_ir_8}.nc:' + var)

# Read the header metadata
metadata = img.GetMetadata()
scale = float(metadata.get(var + '#scale_factor'))
offset = float(metadata.get(var + '#add_offset'))
undef = float(metadata.get(var + '#_FillValue'))
dtime = metadata.get('NC_GLOBAL#time_coverage_start')

# Load the data
ds_cmi = img.ReadAsArray(0, 0, img.RasterXSize, img.RasterYSize).astype(float)

# Apply the scale, offset and convert to celsius
ds_cmi = (ds_cmi * scale + offset) - 273.15

# Reproject the file
filename_ret = f'{output}/IR_{yyyymmddhhmn}.nc'
reproject(filename_ret, img, ds_cmi, extent, undef)

# Open the reprojected GOES-R image
file = Dataset(filename_ret)

# Get the pixel values
data_08 = file.variables['Band1'][:]
#----------------------------------------------------------------------------------------------------------- 
# Open the file
img = gdal.Open(f'NETCDF:{input}/{file_ir_10}.nc:' + var)

# Read the header metadata
metadata = img.GetMetadata()
scale = float(metadata.get(var + '#scale_factor'))
offset = float(metadata.get(var + '#add_offset'))
undef = float(metadata.get(var + '#_FillValue'))
dtime = metadata.get('NC_GLOBAL#time_coverage_start')

# Load the data
ds_cmi = img.ReadAsArray(0, 0, img.RasterXSize, img.RasterYSize).astype(float)

# Apply the scale, offset and convert to celsius
ds_cmi = (ds_cmi * scale + offset) - 273.15

# Reproject the file
filename_ret = f'{output}/IR_{yyyymmddhhmn}.nc'
reproject(filename_ret, img, ds_cmi, extent, undef)

# Open the reprojected GOES-R image
file = Dataset(filename_ret)

# Get the pixel values
data_10 = file.variables['Band1'][:]
#-----------------------------------------------------------------------------------------------------------
# Open the file
img = gdal.Open(f'NETCDF:{input}/{file_ir_12}.nc:' + var)

# Read the header metadata
metadata = img.GetMetadata()
scale = float(metadata.get(var + '#scale_factor'))
offset = float(metadata.get(var + '#add_offset'))
undef = float(metadata.get(var + '#_FillValue'))
dtime = metadata.get('NC_GLOBAL#time_coverage_start')

# Load the data
ds_cmi = img.ReadAsArray(0, 0, img.RasterXSize, img.RasterYSize).astype(float)

# Apply the scale, offset and convert to celsius
ds_cmi = (ds_cmi * scale + offset) - 273.15

# Reproject the file
filename_ret = f'{output}/IR_{yyyymmddhhmn}.nc'
reproject(filename_ret, img, ds_cmi, extent, undef)

# Open the reprojected GOES-R image
file = Dataset(filename_ret)

# Get the pixel values
data_12 = file.variables['Band1'][:]
#-----------------------------------------------------------------------------------------------------------
# Open the file
img = gdal.Open(f'NETCDF:{input}/{file_ir_13}.nc:' + var)

# Read the header metadata
metadata = img.GetMetadata()
scale = float(metadata.get(var + '#scale_factor'))
offset = float(metadata.get(var + '#add_offset'))
undef = float(metadata.get(var + '#_FillValue'))
dtime = metadata.get('NC_GLOBAL#time_coverage_start')

# Load the data
ds_cmi = img.ReadAsArray(0, 0, img.RasterXSize, img.RasterYSize).astype(float)

# Apply the scale, offset and convert to celsius
ds_cmi = (ds_cmi * scale + offset) - 273.15

# Reproject the file
filename_ret = f'{output}/IR_{yyyymmddhhmn}.nc'
reproject(filename_ret, img, ds_cmi, extent, undef)

# Open the reprojected GOES-R image
file = Dataset(filename_ret)

# Get the pixel values
data_13 = file.variables['Band1'][:]
#------------------------------------------------------------------------------------------------------
#------------------------------------------------------------------------------------------------------
# RGB Components
R = data_08 - data_10
G = data_12 - data_13
B = data_08

# Minimuns and Maximuns
Rmin = -26.2
Rmax = 0.6

Gmin = -43.2
Gmax = 6.7

Bmin = -29.25
Bmax = -64.65

R[R<Rmin] = Rmin
R[R>Rmax] = Rmax

G[G<Gmin] = Gmin
G[G>Gmax] = Gmax

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

# Choose the gamma
gamma = 1

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

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

# Open the GRIB file
grib = pygrib.open("gfs.t00z.pgrb2full.0p50.f000")

# Select the variable
prmls = grib.select(name='Pressure reduced to MSL')[0]

# Get information from the file    
init  = str(prmls.analDate)      # Init date / time
run   = str(prmls.hour).zfill(2) # Run
ftime = str(prmls.forecastTime)  # Forecast hour
valid = str(prmls.validDate)     # Valid date / time 
print('Init: ' + init + ' UTC')
print('Run: ' + run + 'Z')
print('Forecast: +' + ftime)
print('Valid: ' + valid + ' UTC')

# Read the data for a specific region
prmls, lats, lons = prmls.data(lat1=extent[1],lat2=extent[3],lon1=extent[0]+360,lon2=extent[2]+360)

# Convert to hPa
prmls = prmls / 100
#-----------------------------------------------------------------------------------------------------------
# Choose the plot size (width x height, in inches)
plt.figure(figsize=(8,8))

# Use the Geostationary projection in cartopy
ax = plt.axes(projection=ccrs.PlateCarree())

# Define the image extent
img_extent = [extent[0], extent[2], extent[1], extent[3]]

   
# Plot the image
img1 = ax.imshow(RGB, origin='upper', extent=img_extent, alpha=1.0)

# Define de contour interval
data_min = 990
data_max = 1050
interval = 2
levels = np.arange(data_min,data_max,interval)

# Plot the contours 
img2 = ax.contour(lons, lats, prmls, colors='cyan', linewidths=0.7, levels=levels)
ax.clabel(img2, inline=1, inline_spacing=0, fontsize='10',fmt = '%1.0f', colors= 'cyan')

# Add a shapefile
# https://geoftp.ibge.gov.br/organizacao_do_territorio/malhas_territoriais/malhas_municipais/municipio_2019/Brasil/BR/br_unidades_da_federacao.zip
shapefile = list(shpreader.Reader('BR_UF_2019.shp').geometries())
ax.add_geometries(shapefile, ccrs.PlateCarree(), edgecolor='white',facecolor='none', linewidth=0.3)

# Add coastlines, borders and gridlines
ax.coastlines(resolution='10m', color='white', linewidth=0.8)
ax.add_feature(cartopy.feature.BORDERS, edgecolor='white', linewidth=0.5)
gl = ax.gridlines(crs=ccrs.PlateCarree(), color='white', alpha=1.0, linestyle='--', linewidth=0.25, xlocs=np.arange(-180, 180, 5), ylocs=np.arange(-90, 90, 5), draw_labels=True)
gl.top_labels = False
gl.right_labels = False

# Use definition to plot H/L symbols
plot_maxmin_points(lons, lats, prmls, 'max', 50, symbol='H', color='b',  transform=ccrs.PlateCarree())
plot_maxmin_points(lons, lats, prmls, 'min', 25, symbol='L', color='r', transform=ccrs.PlateCarree())

# Extract date
date = (datetime.strptime(dtime, '%Y-%m-%dT%H:%M:%S.%fZ'))

# Add a title
plt.title('GOES-16 Airmass RGB ' + date.strftime('%Y-%m-%d %H:%M') + ' UTC' + ' + GFS PSML (hPa)', fontweight='bold', fontsize=6, loc='left')
plt.title('Reg.: ' + str(extent) , fontsize=6, loc='right')
#-----------------------------------------------------------------------------------------------------------
# Save the image
plt.savefig(f'{output}/image_24.png', bbox_inches='tight', pad_inches=0, dpi=300)

# Show the image
plt.show()