GPM data analysis#

Packages Required#

Numpy, Xarray, DrPy, Matplotlib, Cartopy and so on… by running the command in your terminal/command prompt as given below

conda install -c conda-forge cartopy xarray git

Install DrPy

pip install git+https://github.com/dopplerchase/DRpy.git

Import these installed packages#

import numpy as np
import h5py                  
import matplotlib.pyplot as plt
import cartopy.crs as ccrs
# import netCDF4 as nc
import xarray as xr
import pandas as pd
import warnings
warnings.filterwarnings('ignore')

import cartopy.crs as ccrs
import cartopy.feature as feat
from cartopy.mpl.gridliner import LONGITUDE_FORMATTER, LATITUDE_FORMATTER

def gline(ax):
    gl = ax.gridlines(crs=ccrs.PlateCarree(), linewidth=1, color='black', alpha=0.5, 
                         linestyle='--', draw_labels=True)
    gl.xlabels_top = False
    gl.ylabels_left = True
    gl.ylabels_right=False
    gl.xlines = False
    gl.ylines = False
    gl.xformatter = LONGITUDE_FORMATTER
    gl.yformatter = LATITUDE_FORMATTER

#install drpy
!pip install git+https://github.com/dopplerchase/DRpy.git

Collecting git+https://github.com/dopplerchase/DRpy.git
  Cloning https://github.com/dopplerchase/DRpy.git to /private/var/folders/vr/4xwzfzr92_v2npmgfxdnq9vh0000gn/T/pip-req-build-12tweu8t

  Running command git clone --filter=blob:none --quiet https://github.com/dopplerchase/DRpy.git /private/var/folders/vr/4xwzfzr92_v2npmgfxdnq9vh0000gn/T/pip-req-build-12tweu8t

  Resolved https://github.com/dopplerchase/DRpy.git to commit 7d4246d977e926d02b19059de0a0c3793711e2f1

  Preparing metadata (setup.py) ... ?25l-

 done

?25h

import drpy
import datetime as dt

dt.datetime(2021,5,15,20,43)

datetime.datetime(2021, 5, 15, 20, 43)

Download Level 2 GPM data#

create your PPS account here :

https://registration.pps.eosdis.nasa.gov/registration/

io = drpy.io.netrunner(servername='Research',username='hamidsyed37@gmail.com',
                       start_time=dt.datetime(2021,5,15,20,43),
                      end_time=dt.datetime(2021,5,15,22,17))

Downloading: https://arthurhouhttps.pps.eosdis.nasa.gov/text/gpmdata/2021/05/15/radar/2A.GPM.DPR.V9-20211125.20210515-S204332-E221604.040979.V07A.HDF5

---------------------------------------------------------------------------
KeyboardInterrupt                         Traceback (most recent call last)
Input In [7], in <cell line: 1>()
----> 1 io = drpy.io.netrunner(servername='Research',username='hamidsyed37@gmail.com',
      2                        start_time=dt.datetime(2021,5,15,20,43),
      3                       end_time=dt.datetime(2021,5,15,22,17))

File ~/miniconda3/envs/syed/lib/python3.10/site-packages/drpy/io/io.py:48, in netrunner.__init__(self, servername, username, start_time, end_time, autorun, savedir)
     46 self.locate_file()
     47 #this will download it locally
---> 48 self.download(savedir=savedir)

File ~/miniconda3/envs/syed/lib/python3.10/site-packages/drpy/io/io.py:200, in netrunner.download(self, savedir)
    196 args = cmd.split()
    197 process = subprocess.Popen(args,
    198 stdout=subprocess.PIPE,
    199 stderr=subprocess.PIPE)
--> 200 process.wait()
    201 print('Done')

File ~/miniconda3/envs/syed/lib/python3.10/subprocess.py:1207, in Popen.wait(self, timeout)
   1205     endtime = _time() + timeout
   1206 try:
-> 1207     return self._wait(timeout=timeout)
   1208 except KeyboardInterrupt:
   1209     # https://bugs.python.org/issue25942
   1210     # The first keyboard interrupt waits briefly for the child to
   1211     # exit under the common assumption that it also received the ^C
   1212     # generated SIGINT and will exit rapidly.
   1213     if timeout is not None:

File ~/miniconda3/envs/syed/lib/python3.10/subprocess.py:1941, in Popen._wait(self, timeout)
   1939 if self.returncode is not None:
   1940     break  # Another thread waited.
-> 1941 (pid, sts) = self._try_wait(0)
   1942 # Check the pid and loop as waitpid has been known to
   1943 # return 0 even without WNOHANG in odd situations.
   1944 # http://bugs.python.org/issue14396.
   1945 if pid == self.pid:

File ~/miniconda3/envs/syed/lib/python3.10/subprocess.py:1899, in Popen._try_wait(self, wait_flags)
   1897 """All callers to this function MUST hold self._waitpid_lock."""
   1898 try:
-> 1899     (pid, sts) = os.waitpid(self.pid, wait_flags)
   1900 except ChildProcessError:
   1901     # This happens if SIGCLD is set to be ignored or waiting
   1902     # for child processes has otherwise been disabled for our
   1903     # process.  This child is dead, we can't get the status.
   1904     pid = self.pid

KeyboardInterrupt: 

file = '2A.GPM.DPR.V9-20211125.20210515-S204332-E221604.040979.V07A.HDF5'

Read the data#

Reading data by using DrPy library

dpr = drpy.core.GPMDPR(filename=file)

Data analysis#

Let’s have a look at the data

fig = plt.figure(figsize = [10,6.5])
ax = plt.axes(projection = ccrs.PlateCarree())
ax.coastlines()
dpr.ds['zFactorFinal'].isel(nfreq=0).max(axis=2).plot(x = "Longitude", 
                                                      y = "Latitude", 
                                                      vmin = 0, 
                                                      vmax = 60, 
                                                      cbar_kwargs = {"pad":0.02,
                                                                     "shrink":0.5
                                                                    } 
                                                     )
gline(ax)

Slicing the data#

Now if you see there are four dimensions in this dataset viz., when you type dpr.ds[‘zFactorFinal’]

(nscan: 7932, nrayNS: 49, nbin: 176, nfreq: 2)

The above figure shows that the GPM satellite travels in between latitudes ranging from 60ºS to 60ºN by making a sinwave like pattern around the globe.
This sinwave like pattern is called as the swath of the satellite (you can call it a path as well)
This swath is basically the data of reflectivity (dBZ)
If we plot this without this map or PlateCarre projection the data will look like as plotted below
It shows us that the “nscan” dimension is x-axis and “nrayNS” is y-axis and similarly “nbin” is the height or z-axis
We can also note that there is much data only as few lines, so let’s see what it is, so if we slice “nscan” somewhere between 5000 to 6000, we can see the Cyclone Tauktae over Indian ocean, because the GPM has passed over the Indian Ocean and we can clearly see that there are some data patches present over the region.

fig = plt.figure(figsize = [10,6.5])
ax = plt.axes()
dpr.ds['zFactorFinal'].isel(nfreq=0).max(axis=2).plot(x = "nscan",
                                                      vmin = 0, 
                                                      vmax = 60, 
                                                      cbar_kwargs = {"pad":0.02,
                                                                     "shrink":0.5
                                                                    } 
                                                     )

<matplotlib.collections.QuadMesh at 0x133ab69b0>

start = 5550
end = 5630

dpr.ds['precipRateNearSurface'][start:end,:].plot.contourf(x='nscan',levels=range(0,200,2),cmap='turbo')
plt.gca().invert_yaxis()

dpr.ds['zFactorFinal'].isel(nscan=slice(start,end),nfreq=1).max(axis=2).plot.contourf(x='nscan',
                                                                                            levels=range(0,60,2),
                                                                                           cmap='turbo')
plt.title(pd.to_datetime(dpr.ds['zFactorFinal'].isel(nscan=slice(start,end),nfreq=1).time[0,0].values).strftime(
    '%Y%m%d %H:%M:%S'))
plt.gca().invert_yaxis()

dpr.ds['flagHeavyIcePrecip'][start:end].plot(x = "nscan")
plt.gca().invert_yaxis()

plt.figure(figsize=[8,8])
ax = plt.axes(projection=ccrs.PlateCarree())
dpr.ds['zFactorFinal'][start:end,:,:,0].max(axis=2).plot.contourf(x='Longitude',y='Latitude',levels=range(0,60,9),
                                                        cmap='turbo')
ax.set_title(pd.to_datetime(dpr.ds['zFactorFinal'][start:end,:,160,0].time[0,0].values).strftime('%Y%m%d %H:%M:%S'))
ax.coastlines()
gline(ax)

GPM Data Analysis

GPM data analysis

Contents

GPM data analysis#

Packages Required#

Import these installed packages#

Download Level 2 GPM data#

Read the data#

Data analysis#

Slicing the data#