Next we will show how to create the animation of global sea surface salinity (SSS) over the period 27-03-2015 to 16-04-2018 based on the 8-day running mean version 3.0 SMAP product from Remote Sensing Systems at a spatial resolution of 70km. The animation is published in PODAAC YouTube channel at https://www.youtube.com/watch?v=wadOEbvjl9s. The data set can be downloaded from PODAAC Portal at https://podaac.jpl.nasa.gov/dataset/SMAP_RSS_L3_SSS_SMI_8DAY-RUNNINGMEAN_V3_70KM.
Here is the python code which creates all the animation frames from the downloaded SSS data files. Each frame represents one day of data for this dataset.
- Code: Select all
#! /usr/bin/env python
#
# Caution: This is a Python script, and Python takes indentation seriously.
# DO NOT CHANGE INDENTATION OF ANY LINE BELOW!
from matplotlib import pyplot as plt
import numpy as np
from mpl_toolkits.basemap import Basemap
from netCDF4 import Dataset
import matplotlib
import os.path
from os import path
import os
import glob
import datetime
# Data Files Directory
data_dir = './8day_running/70KM/'
# set up map projection
parallels = np.arange(-90,90+30,30)
meridians = np.arange(0,360+60,60)
# setup contour levels and colarmap
clevs = np.linspace(32, 40, 101)
cmap=plt.get_cmap("jet")
cmap.set_under("black")
cmap.set_over("DarkViolet")
# setup figure object
plt.figure(figsize=(6, 3.375), dpi=300)
# read each data file in the list
nf = 0
for k in range(2015, 2019):
for j in range(1, 367):
strtemp = data_dir+str(k)+'/'+"{0:0>3}".format(j)+'/RSS_smap_SSS_L3_8day_running*v03.0.nc'
for filename in glob.glob(strtemp):
print(filename)
ncin = Dataset(filename, 'r')
lon = ncin.variables['lon'][:]
lat = ncin.variables['lat'][:]
data = ncin.variables['sss_smap'][:]
ncin.close()
lons,lats = np.meshgrid(lon,lat)
m = Basemap(projection='cyl', llcrnrlon=0.0, llcrnrlat=-90.0,
urcrnrlon=360, urcrnrlat=90.0)
m.bluemarble()
m.drawparallels(parallels,labels=[1,0,0,0],fontsize=6)
meri = m.drawmeridians(meridians,labels=[1,0,0,1],fontsize=6)
xx, yy = m(lons, lats)
cs=m.contourf(lons,lats, data, clevs, cmap=cmap, extend='both')
cb = m.colorbar(cs, 'right', size='2%', pad='0.5%')
cb.ax.set_yticklabels(cb.ax.get_yticklabels(), fontsize=6)
cb.set_label('SSS (PSU)', fontsize=7,fontweight="bold")
cb.set_ticks(range(32,41,1))
d = datetime.date(k,1,1) + datetime.timedelta(j-1)
plt.title(str(k)+"-"+'{0:02d}'.format(d.month) + "-"+'{0:02d}'.format(d.day)+" (RSS SMAP 70km 8Day)", fontsize=8, fontname="Times New Roman",fontweight="bold")
nf = nf + 1
plt.subplots_adjust(left=0.05, right=0.92, top=0.95, bottom=0.01)
plt.savefig("frame{0}".format(str(nf).rjust(4, "0")), dpi = 300)
plt.clf()
The python script creates a series of picture frames starting from frame0001.png. Here we show the first three frames:
- First Frame
- frame0001.png (197.32 KiB) Viewed 20273 times
- Second Frame
- frame0002.png (193.77 KiB) Viewed 20273 times
- Third Frame
- frame0003.png (191.82 KiB) Viewed 20273 times
- Fourth Frame
- frame0004.png (190.88 KiB) Viewed 20273 times
Next the ffmpeg command is used to aggregate all the picture frames into the video animation from the following command:
- Code: Select all
%> ffmpeg -framerate 25 -i frame%04d.png -c:v libx264 -crf 23 -pix_fmt yuv420p -qp 0 sss_animation.mp4
The ffmpeg documentation can be found from the website https://ffmpeg.org/ffmpeg.html.