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adv_vort_rel_500.py
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adv_vort_rel_500.py
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#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Wed Aug 24 18:20:34 2022
@author: coqueiro
"""
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Wed Aug 24 17:39:26 2022
@author: coqueiro
"""
#importando bibliotecas
import cartopy.crs as ccrs
import cartopy.feature as cfeature
import matplotlib.pyplot as plt
import metpy.calc as mpcalc
from metpy.units import units
import numpy as np
import xarray as xr
import cartopy.io.shapereader as shpreader # Import shapefiles
from datetime import datetime, timedelta # basicas datas e tipos de tempo
import cmocean
#dataset
file_0 = xr.open_dataset(
'/home/coqueiro/ufrj/fis_nuvens/dados/24_02_19_continental_multi.nc',
decode_times = False
)
file_1 = xr.open_dataset(
'/home/coqueiro/ufrj/fis_nuvens/dados/24_02_19_continental_multi.nc'
).metpy.parse_cf()
file_1 = file_1.assign_coords(dict(
longitude = (((file_1.longitude.values + 180) % 360) - 180))
).sortby('longitude')
file_2 = xr.open_dataset(
'/home/coqueiro/ufrj/fis_nuvens/dados/24_02_19_continental_single.nc'
).metpy.parse_cf()
file_2 = file_2.assign_coords(dict(
longitude = (((file_2.longitude.values + 180) % 360) - 180))
).sortby('longitude')
#
#extent
lon_slice = slice(-75., -20.)
lat_slice = slice(10., -60.)
#pega as lat/lon
lats = file_1.latitude.sel(latitude=lat_slice).values
lons = file_1.longitude.sel(longitude=lon_slice).values
#seta as variaveis
level = 500
for i in range(len(file_1.variables['time'])):
geopotencial = file_1.z.metpy.sel(
time = file_1.time[i] , vertical=level, latitude=lat_slice, longitude=lon_slice).metpy.unit_array.squeeze()
u = file_1.u.metpy.sel(
time = file_1.time[i] , vertical=level, latitude=lat_slice, longitude=lon_slice).metpy.unit_array.squeeze()
v = file_1.v.metpy.sel(
time = file_1.time[i] , vertical=level, latitude=lat_slice, longitude=lon_slice).metpy.unit_array.squeeze()
pnmm = file_2.msl.metpy.sel(
time = file_1.time[i] , latitude=lat_slice, longitude=lon_slice).metpy.unit_array.squeeze()* 0.01 * units.hPa/units.Pa
t = file_1.t.metpy.sel(
time = file_1.time[i] , vertical=level, latitude=lat_slice, longitude=lon_slice).metpy.unit_array.squeeze()
dx, dy = mpcalc.lat_lon_grid_deltas(lons, lats)
vorticidade = mpcalc.vorticity(u, v, dx=dx, dy=dy, x_dim=- 1, y_dim=- 2)*10**5
adv_vort = mpcalc.advection(vorticidade, u=u, v=v, dx=dx, dy=dy, x_dim=- 1, y_dim=- 2)*100
# escolha o tamanho do plot em polegadas (largura x altura)
plt.figure(figsize=(25,25))
# usando a projeção da coordenada cilindrica equidistante
ax = plt.axes(projection=ccrs.PlateCarree())
shapefile = list(
shpreader.Reader(
'/home/coqueiro/Downloads/br_unidades_da_federacao/BR_UF_2019.shp'
).geometries()
)
ax.add_geometries(
shapefile, ccrs.PlateCarree(),
edgecolor = 'black',
facecolor='none',
linewidth=0.5
)
# adiciona continente e bordas
ax.coastlines(resolution='50m', color='black', linewidth=1)
ax.add_feature(cfeature.BORDERS, edgecolor='black', linewidth=1)
gl = ax.gridlines(crs=ccrs.PlateCarree(),
color='gray',
alpha=1.0,
linestyle='--',
linewidth=0.5,
xlocs=np.arange(-180, 180, 5),
ylocs=np.arange(-90, 90, 5),
draw_labels=True
)
gl.top_labels = False
gl.right_labels = False
# intevalos da pnmm
intervalo_min2 = np.amin(np.array(pnmm))
intervalo_max2 = np.amax(np.array(pnmm))
interval_2 = 2 # de quanto em quanto voce quer que varie
levels_2 = np.arange(intervalo_min2, intervalo_max2, interval_2)
# intevalos da divergencia - umidade
intervalo_min3 = -1.6
intervalo_max3 = 0
interval_3 = 0.10 # de quanto em quanto voce quer que varie
levels_3 = np.arange(intervalo_min3, intervalo_max3, interval_3)
# adiciona mascara de terra
ax.add_feature(cfeature.LAND)
# plota a imagem geopotencial
sombreado = ax.contourf(lons,
lats,
adv_vort,
cmap='gist_heat',
levels = levels_3,
extend = 'both'
)
# plota a imagem pressao
contorno = ax.contour(lons,
lats,
pnmm,
colors='black',
linewidths=0.8,
levels=levels_2
)
ax.clabel(contorno,
inline = 1,
inline_spacing = 1,
fontsize=20,
fmt = '%3.0f',
colors= 'black'
)
# adiciona legenda
barra_de_cores = plt.colorbar(sombreado,
orientation = 'horizontal',
pad=0.04,
fraction=0.04
)
font_size = 20 # Adjust as appropriate.
barra_de_cores.ax.tick_params(labelsize=font_size)
# Getting the file time and date
add_seconds = int(file_0.variables['time'][i])
date = datetime(1900,1,1,0) + timedelta(hours=add_seconds)
date_formatted = date.strftime('%Y-%m-%d %H')
# Add a title
plt.title('Adveccao de vorticidade relativa (1/s²) - '+ str(level) +' hPa',
fontweight='bold',
fontsize=35,
loc='left'
)
plt.title(f'{date_formatted}',
fontsize=20,
loc='right'
)
#--------------------------------------------------------------------------
# Salva imagem
plt.savefig(f'adveccao_de_vorticidade_{date_formatted}.png', bbox_inches='tight')