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figura_paper_esfuerzo_rotor_medio.py
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figura_paper_esfuerzo_rotor_medio.py
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"""
En esta rutina hago la Figura 2 del paper,
con los rotores medios del stress del viento de
NCEP, CFSR, ERA y CCMPv2
Dani Risaro
Noviembre 2019
"""
import warnings
warnings.filterwarnings('ignore')
import cmocean as cm
import numpy as np
import pandas as pd
import xarray as xr
import cartopy.crs as ccrs
import matplotlib.pyplot as plt
import cartopy.feature as cfeature
from cartopy.mpl.ticker import LongitudeFormatter, LatitudeFormatter
from scipy.ndimage.filters import gaussian_filter
# curl and wind data
directory = '/home/daniu/Documentos/datos_rotor_reanalisis_CCMPv2_mensuales/'
datasets = ['CCMPv2','ERAI','CFSR','NCEPR1']
windfiles = '.u-v.mean.gaussiangrid.nc'
curlfiles = '.dv.vor.gaussiangrid.nc'
# area for the plot
lonlatbox_list = [250, 350, -60, -10]
lon_w, lon_e, lat_s, lat_n = lonlatbox_list
# load topo data
data_bati = xr.open_dataset('/home/daniu/Documentos/batimetria/ETOPO1_Bed_g_gmt4.grd')
data_bati = data_bati.sel(x=slice(-(360-lon_w), -(360-lon_e)), \
y=slice(lat_s, lat_n))
blon = data_bati.x.values
blat = data_bati.y.values
data_bati = data_bati.z.values
# load SAF data
saf = np.loadtxt('/home/daniu/Documentos/frentes/saf_orsi.csv', delimiter=',')
saf_lon = saf[:,0]
saf_lat = saf[:,1]
ind_lon = np.where(((saf_lon >= lon_w) & (saf_lon <= lon_e)))
ind_lat = np.where(((saf_lat >= lat_s) & (saf_lat <= lat_n)))
ind = np.intersect1d(ind_lon, ind_lat)
saf_x = saf_lon[ind]
saf_y = saf_lat[ind]
# figure settings
figsize = (6,4)
splt_pos = [ # the figure has 4 subplots (2x2). Here we set the position of each of them
[0.05, 0.50, 0.38, 0.38],
[0.50, 0.50, 0.38, 0.38],
[0.05, 0.05, 0.38, 0.38],
[0.50, 0.05, 0.38, 0.38],
]
cmap = plt.get_cmap(cm.cm.curl)
lon_ticks = np.arange(-110, 0, 20)
lat_ticks = np.arange(-60, 0, 10)
title = ['a) CCMPv2','b) ERA-Interim','c) CFSR','d) NCEPR1']
fontsize = 6
clevs_curl = np.linspace(-.6, .6, 13)
clevs_curl_label = [-1, -.8, -.6, -.4, -.2, 0, .2 ,.4 ,.6, .8, 1]
nodes = [20, 20, 10, 3]
# figure
plt.close('all')
fig = plt.figure(figsize=figsize)
plt.clf()
for i in range(4):
# load curl data
data = xr.open_dataset(directory + datasets[i] + curlfiles).sel(lon=slice(lon_w,lon_e),lat=slice(lat_n,lat_s))
data_curl = data.svo.mean('time').values
lon_curl = data.lon.values
lat_curl = data.lat.values
sigma = 1.25 # this depends on how noisy your data is, play with it!
mean_curl = gaussian_filter(data_curl, sigma)
# load u.v data
data = xr.open_dataset(directory + datasets[i] + windfiles).sel(lon=slice(lon_w,lon_e),lat=slice(lat_n,lat_s))
data_wind = data.mean('time')
mean_u = data_wind.u.values
mean_v = data_wind.v.values
lon_wind = data.lon.values
lat_wind = data.lat.values
# wind stress field
ws = np.sqrt(mean_u**2 + mean_v**2)
cd = (2.7/ws + .142 + ws/13.09)/1000
rhoaire = 1.222
matriz = ws*cd*rhoaire*mean_curl*1e7
mean_u = mean_u*ws*cd*rhoaire
mean_v = mean_v*ws*cd*rhoaire
if i==3:
matriz = matriz*0.9
ax = plt.axes(splt_pos[i], projection=ccrs.Mercator())
ax.set_extent(lonlatbox_list, crs=ccrs.PlateCarree())
ax.coastlines(resolution='50m', color='black', linewidths=0.2, zorder=5)
ax.add_feature(ccrs.cartopy.feature.LAND, edgecolor='k', color='white', zorder=4)
if i==0:
ax.set_xticks(lon_ticks, crs=ccrs.PlateCarree())
ax.set_yticks(lat_ticks, crs=ccrs.PlateCarree())
ax.set_xticklabels([])
lat_formatter = LatitudeFormatter()
ax.yaxis.set_major_formatter(lat_formatter)
elif i==1:
ax.set_xticks(lon_ticks, crs=ccrs.PlateCarree())
ax.set_yticks(lat_ticks, crs=ccrs.PlateCarree())
ax.set_xticklabels([])
ax.set_yticklabels([])
elif i==3:
ax.set_xticks(lon_ticks, crs=ccrs.PlateCarree())
ax.set_yticks(lat_ticks, crs=ccrs.PlateCarree())
ax.set_yticklabels([])
lon_formatter = LongitudeFormatter(zero_direction_label=True)
ax.xaxis.set_major_formatter(lon_formatter)
else:
ax.set_xticks(lon_ticks, crs=ccrs.PlateCarree())
ax.set_yticks(lat_ticks, crs=ccrs.PlateCarree())
lon_formatter = LongitudeFormatter(zero_direction_label=True)
lat_formatter = LatitudeFormatter()
ax.xaxis.set_major_formatter(lon_formatter)
ax.yaxis.set_major_formatter(lat_formatter)
ax.tick_params('both', labelsize=fontsize)
ax.set_aspect('equal', 'box')
ax.set_title(title[i], fontsize=fontsize, loc='left')
ccurl = plt.contourf(lon_curl, lat_curl, matriz, clevs_curl, cmap=cmap, transform=ccrs.PlateCarree(), extend='both', alpha=.8)
cbati = plt.contour(blon, blat, data_bati, [-200], colors='gray', linewidths=.25, linestyles='solid',
transform=ccrs.PlateCarree())
ccurl_line = plt.contour(lon_curl, lat_curl, matriz, clevs_curl_label, colors='k',
linewidths=.25, transform=ccrs.PlateCarree(), zorder=1, alpha=0.8)
plt.clabel(ccurl_line, inline=1, inline_spacing=-2, fmt='%2.1f', fontsize=4, colors='k')
ax.plot(saf_x, saf_y, color='darkblue', linestyle='-', linewidth=0.4, transform=ccrs.PlateCarree())
n = nodes[i]
qvr = ax.quiver(lon_wind[::1*n], lat_wind[::1*n], mean_u[::1*n,::1*n], mean_v[::1*n,::1*n], units='xy',
headaxislength=3.5, transform=ccrs.PlateCarree(), color='k', alpha=0.6)
ax.quiverkey(qvr, 0.75, 1.05, .1, '0.1 N m$^{-2}$', labelpos='E', coordinates='axes', color='k', fontproperties={'size':6})
cax = fig.add_axes([0.9, 0.07, 0.016, 0.8])
cb = fig.colorbar(ccurl, orientation='vertical', cax=cax)
cb.ax.set_ylabel('Wind stress curl [N m$^{-2}$ km$^{-1}$ *10$^{4}$]', fontsize=fontsize)
cb.ax.tick_params(labelsize=fontsize)
fig.savefig('/home/daniu/Documentos/figuras/fig2paper_windstresscurl', dpi=300, bbox_inches='tight')
fig.savefig('/home/daniu/Documentos/figuras/fig2paper_windstresscurl' + '.pdf', bbox_inches='tight')