Figure copied from 'Vento_ERA5_WW3.ipynb', which shows the coordinates poits from both models and the ADCP position
The ERA5 model resolution for waves is 0,5o = WW3
Load wave data from ERA5 from a NetCDF (nc) file
The 'nc' file was downloaded using the API generated at
https://cds.climate.copernicus.eu/cdsapp#!/dataset/reanalysis-era5-single-levels?tab=form
Selected the nearest point from the ADCP to extract a time series (the same point of WW3!)
import numpy as np
import matplotlib.pyplot as plt
import netCDF4
import pickle
Python - NetCDF reading and writing example with plotting
http://schubert.atmos.colostate.edu/~cslocum/netcdf_example.html
Figure copied from 'Vento_ERA5_WW3.ipynb', which shows the coordinates poits from both models and the ADCP position
The ERA5 model resolution for waves is 0,5o = WW3
mod = netCDF4.Dataset('ERA_waves_download.nc')
# to find out the variables fields
for i in mod.variables:
print([i, mod.variables[i].units, mod.variables[i].shape])
['longitude', 'degrees_east', (7,)] ['latitude', 'degrees_north', (6,)] ['time', 'hours since 1900-01-01 00:00:00.0', (26304,)] ['mwd', 'Degree true', (26304, 6, 7)] ['mwp', 's', (26304, 6, 7)] ['swh', 'm', (26304, 6, 7)]
lat = mod.variables['latitude'][:]
lon = mod.variables['longitude'][:]
lat = np.array(lat)
lon = np.array(lon)
lonlon, latlat = np.meshgrid(lon, lat)
plt.plot(lonlon, latlat, '.')
# to be sure we are getting the rigth point!!!!
px = 3
py = 3
plt.plot(lonlon[py, px], latlat[py, px], 'ro')
print(lonlon[py, px], latlat[py, px])
plt.show()
-46.5 -0.5
# get the time series of height, period and direction for each point
wh = mod.variables['swh'][:, py, px]
wp = mod.variables['mwp'][:, py, px]
wd = mod.variables['mwd'][:, py, px]
wh.shape
(26304,)
# Transforms the time of the model to datetime
#https://stackoverflow.com/questions/60958754/typeerror-class-cftime-cftime-datetimegregorian-is-not-convertible-to-date
pega_tempo = mod.variables['time']
tempo = netCDF4.num2date(pega_tempo, pega_tempo.units, only_use_cftime_datetimes=False)
print(tempo[0])
print(tempo[0:3])
2015-01-01 00:00:00 [real_datetime(2015, 1, 1, 0, 0) real_datetime(2015, 1, 1, 1, 0) real_datetime(2015, 1, 1, 2, 0)]
plt.plot(tempo)
[<matplotlib.lines.Line2D at 0x246be5cb250>]
plt.plot(tempo, wh)
[<matplotlib.lines.Line2D at 0x246be8f0910>]
# saving!
hdr ='''
Dados de onda do ERA5 para coordenadas
longitude: -46.5
latitude: -0.5
para o período de 2015 até 2017
[tempo, altura, periodo, direcao]
'''
j = [hdr, tempo, wh, wp, wd]
# with open('Ondas_ERA5_2015_2017.pkl', 'wb') as io:
# pickle.dump(j, io)