Cyclone track¶
In this last example, we showcases how RADWave can be used to query wave conditions from altimeter database along a specified cyclone track.
Loading RADWave library and initialisation¶
We first start by importing RADwave library into our working space.
import RADWave as rwave
%matplotlib inline
%config InlineBackend.figure_format = 'svg'
First we will use already downloaded dataset of track of cyclone YASI. The data was obtained from the Australian Bureau of Meteorology (BOM cyclone tracks).
To load this file in the waveAnalysis class, the cyclone track needs to be a CSV file with in the header having the following keyword names lon, lat & datetime.
To only visualise the tracks on a map, a minimal number of options have to be set as shown in the cell below:
cyc = rwave.waveAnalysis(cycloneCSV='../pracenv/dataset/2010-YASI.csv')
Plotting the tracks is done by using the following function:
cyc.plotCycloneTracks(title="Cyclone YASI Track", markersize=100, zoom=4,
extent=[138, 180, -30, -10], fsize=(12, 10))
/usr/share/miniconda/envs/coast/lib/python3.6/site-packages/cartopy/mpl/gridliner.py:307: UserWarning: The .xlabels_top attribute is deprecated. Please use .top_labels to toggle visibility instead.
warnings.warn('The .xlabels_top attribute is deprecated. Please '
/usr/share/miniconda/envs/coast/lib/python3.6/site-packages/cartopy/mpl/gridliner.py:331: UserWarning: The .ylabels_left attribute is deprecated. Please use .left_labels to toggle visibility instead.
warnings.warn('The .ylabels_left attribute is deprecated. Please '
/usr/share/miniconda/envs/coast/lib/python3.6/site-packages/cartopy/io/__init__.py:260: DownloadWarning: Downloading: https://naciscdn.org/naturalearth/10m/physical/ne_10m_coastline.zip
warnings.warn('Downloading: {}'.format(url), DownloadWarning)
The geographical extent of the cyclone path and the associated time frame can be infered from the figure above.
This was used to specify the altimeter data record location and temporal extent when using the Australian Ocean Data Network portal AODN.
As for the other examples, we recomend to look at RADWave documentation and the embeded video that explain how to select both a spatial bounding box and a temporal extent from the portal and how to export the file containing the
List of URLs. ThisTXTfile contains a list ofNETCDFfiles for each available satellites.
We will now create 2 new RADWave classes names (wa_east and wa_west) that will, in addition to the cyclone track, set the list of altimeter NETCDF URLs files to query for the analyse in 2 different regions.
For a detail overview of the options available in this class, you can have a look at the waveAnalysis API.
We also call the processAltimeterData function to query the actual dataset and store the altimeter data in each class. The description of this function is available from the API.
For the eastern region¶
wa_east = rwave.waveAnalysis(altimeterURL='../pracenv/dataset/IMOS_YASI_east.txt', bbox=[170, 175, -17, -12],
stime=[2011,1,27], etime=[2011,2,4], cycloneCSV='../pracenv/dataset/2010-YASI.csv')
wa_east.processAltimeterData(max_qc=1, altimeter_pick='all', saveCSV = 'altimeterDataE.csv')
Processing Altimeter Dataset
+ name JASON-2 / number of tracks 19
---------------------------------------------------------------------------
KeyboardInterrupt Traceback (most recent call last)
<ipython-input-4-fa97d545e912> in <module>
2 stime=[2011,1,27], etime=[2011,2,4], cycloneCSV='../pracenv/dataset/2010-YASI.csv')
3
----> 4 wa_east.processAltimeterData(max_qc=1, altimeter_pick='all', saveCSV = 'altimeterDataE.csv')
/usr/share/miniconda/envs/coast/lib/python3.6/site-packages/RADWave/altiwave.py in processAltimeterData(self, max_qc, altimeter_pick, saveCSV)
296 for k in range(len(picked_url)):
297
--> 298 ncs = NetCDFFile(picked_url[k])
299 if k == 0:
300 print(
src/netCDF4/_netCDF4.pyx in netCDF4._netCDF4.Dataset.__init__()
src/netCDF4/_netCDF4.pyx in netCDF4._netCDF4._get_vars()
src/netCDF4/_netCDF4.pyx in netCDF4._netCDF4.Variable.__init__()
/usr/share/miniconda/envs/coast/lib/python3.6/site-packages/netCDF4/utils.py in _find_dim(grp, dimname)
39 return [A[i] for i in sorted(range(len(A)), key=B.__getitem__)]
40
---> 41 def _find_dim(grp, dimname):
42 # find Dimension instance given group and name.
43 # look in current group, and parents.
KeyboardInterrupt:
For the western region¶
wa_west = rwave.waveAnalysis(altimeterURL='../pracenv/dataset/IMOS_YASI_west.txt', bbox=[156, 161, -16, -12],
stime=[2011,1,27], etime=[2011,2,4], cycloneCSV='../pracenv/dataset/2010-YASI.csv')
wa_west.processAltimeterData(max_qc=1, altimeter_pick='all', saveCSV = 'altimeterDataW.csv')
Processing Altimeter Dataset
+ name JASON-2 / number of tracks 16
+ name CRYOSAT-2 / number of tracks 20
+ name ENVISAT / number of tracks 20
Processing altimeter dataset took: 2 s
In the case where a cyclone track is given when initialising the waveAnalysis class, the visualiseData plots not only the extent of the altimeter dataset but also the associated path.
wa_east.visualiseData(title="Altimeter data east side", extent=[138, 180, -30, -10.0],
markersize=35, zoom=4, fsize=(12, 10), fsave=None)
wa_west.visualiseData(title="Altimeter data west side", extent=[138, 180, -30, -10.0],
markersize=35, zoom=4, fsize=(12, 10), fsave=None)
Extracting relevant wave dataset¶
Once the data has been loaded, the following step consist in computing the wave parameters by running the generateTimeSeries function.
This function computes both instantaneous and monthly wave variables:
significant wave height (m) - wh & wh_rolling
wave period (s) - period & period_rolling
wave energy flux (kW/m) - power & power_rolling
wave average energy density (J/m2) - energy & energy_rolling
wave group velocity (m/s) - speed & speed_rolling
east_ts = wa_east.generateTimeSeries()
west_ts = wa_west.generateTimeSeries()
From the cyclone track, we find the closest processed altimeter geographical locations that have been recorded in the database (based on a KDTree search).
In addition to their coordinates, the altimeter dataset has to be recorded during a user defined time lapse close enough to the cyclone path time at each position.
This is done using the close2Track function that takes the following arguments:
radius, the maximum radius distance in degree between cyclone position and altimeter data coordinates [here set to 2.]dtmax, the maximum difference in time between recorded cyclone date and picked altimeter data (hours) [here set to 6]
This function stores a Pandas dataframe in the waveAnalysis class called cyclone_data that contains the following variables:
altimeter significant wave height (m) - wH
altimeter wave period (s) - period
altimeter wave energy flux (kW/m) - power
altimeter wave average energy density (J/m2) - energy
altimeter wave group velocity (m/s) - speed
distance between altimeter coordinates and cyclone path (km) - dist
cyclone date (datetime) - cdate
difference in time between recorded cyclone date and altimeter data for specific position (hours) - hours
cyclone latitude position - clat
cyclone longitude position - clon
altimeter data latitude position - lat
altimeter data longitude position - lon
Depending of the available altimeters dataset and the chosen radius and dtmax parameters, the Pandas dataframe cyclone_data will contain different number of values (or can potentially be empty).
east_track = wa_east.close2Track(radius=2.,dtmax=6.)
display(wa_east.cyclone_data)
west_track = wa_west.close2Track(radius=2.,dtmax=6.)
display(wa_west.cyclone_data)
| period | speed | power | energy | dist | date | wH | lon | lat | clon | clat | hours | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 5.440683 | 8.491691 | 27.794308 | 8.491691 | 113.604 | 2011-01-28 18:00:00+00:00 | 1.614 | 174.444580 | -13.529891 | 174.1 | -14.5 | -2.284 |
| 1 | 5.749904 | 8.974317 | 29.812409 | 8.974317 | 33.699 | 2011-01-28 18:00:00+00:00 | 1.626 | 174.359543 | -14.330362 | 174.1 | -14.5 | -2.280 |
| 2 | 5.903791 | 9.214501 | 41.094925 | 9.214501 | 102.590 | 2011-01-28 18:00:00+00:00 | 1.884 | 174.243790 | -15.416570 | 174.1 | -14.5 | -2.275 |
| 3 | 5.893205 | 9.197977 | 27.622688 | 9.197977 | 213.351 | 2011-01-29 06:00:00+00:00 | 1.546 | 170.924179 | -14.505723 | 172.9 | -14.4 | -2.008 |
| period | speed | power | energy | dist | date | wH | lon | lat | clon | clat | hours | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 6.756970 | 10.546121 | 265.003090 | 10.546121 | 213.219 | 2011-01-31 12:00:00+00:00 | 4.4720 | 159.141006 | -14.474576 | 160.9 | -13.6 | -0.424 |
| 1 | 6.294248 | 9.823916 | 197.842215 | 9.823916 | 207.053 | 2011-01-31 12:00:00+00:00 | 4.0035 | 159.012177 | -13.911661 | 160.9 | -13.6 | -0.421 |
| 2 | 5.388097 | 8.409617 | 215.400314 | 8.409617 | 121.468 | 2011-01-31 12:00:00+00:00 | 4.5150 | 160.805725 | -12.505943 | 160.9 | -13.6 | 4.477 |
| 3 | 6.742534 | 10.523590 | 645.875203 | 10.523590 | 26.814 | 2011-01-31 12:00:00+00:00 | 6.9890 | 160.705750 | -13.449617 | 160.9 | -13.6 | 4.482 |
| 4 | 8.084588 | 12.618237 | 917.565692 | 12.618237 | 121.311 | 2011-01-31 12:00:00+00:00 | 7.6075 | 160.578125 | -14.650486 | 160.9 | -13.6 | 4.487 |
| 5 | 9.622043 | 15.017862 | 1129.556402 | 15.017862 | 218.935 | 2011-01-31 12:00:00+00:00 | 7.7370 | 160.483597 | -15.536715 | 160.9 | -13.6 | 4.491 |
| 6 | 6.898052 | 10.766320 | 272.597023 | 10.766320 | 207.058 | 2011-01-31 14:00:00+00:00 | 4.4890 | 159.369446 | -15.467758 | 160.0 | -13.7 | -2.428 |
| 7 | 6.756970 | 10.546121 | 265.003090 | 10.546121 | 126.292 | 2011-01-31 14:00:00+00:00 | 4.4720 | 159.141006 | -14.474576 | 160.0 | -13.7 | -2.424 |
| 8 | 6.294248 | 9.823916 | 197.842215 | 9.823916 | 109.344 | 2011-01-31 14:00:00+00:00 | 4.0035 | 159.012177 | -13.911661 | 160.0 | -13.7 | -2.421 |
| 9 | 6.351562 | 9.913371 | 182.333266 | 9.913371 | 123.287 | 2011-01-31 14:00:00+00:00 | 3.8260 | 158.898865 | -13.414909 | 160.0 | -13.7 | -2.419 |
| 10 | 6.061618 | 9.460833 | 114.016157 | 9.460833 | 195.525 | 2011-01-31 14:00:00+00:00 | 3.0970 | 158.688202 | -12.487484 | 160.0 | -13.7 | -2.414 |
| 11 | 5.388097 | 8.409617 | 215.400314 | 8.409617 | 158.380 | 2011-01-31 14:00:00+00:00 | 4.5150 | 160.805725 | -12.505943 | 160.0 | -13.7 | 2.477 |
| 12 | 6.742534 | 10.523590 | 645.875203 | 10.523590 | 81.251 | 2011-01-31 14:00:00+00:00 | 6.9890 | 160.705750 | -13.449617 | 160.0 | -13.7 | 2.482 |
| 13 | 8.084588 | 12.618237 | 917.565692 | 12.618237 | 122.287 | 2011-01-31 14:00:00+00:00 | 7.6075 | 160.578125 | -14.650486 | 160.0 | -13.7 | 2.487 |
| 14 | 9.622043 | 15.017862 | 1129.556402 | 15.017862 | 209.795 | 2011-01-31 14:00:00+00:00 | 7.7370 | 160.483597 | -15.536715 | 160.0 | -13.7 | 2.491 |
| 15 | 10.164666 | 15.864775 | 1659.794099 | 15.864775 | 166.122 | 2011-02-01 00:00:00+00:00 | 9.1250 | 157.284393 | -15.489555 | 156.7 | -14.1 | 4.754 |
| 16 | 7.840593 | 12.237417 | 866.401715 | 12.237417 | 62.471 | 2011-02-01 00:00:00+00:00 | 7.5065 | 157.171509 | -14.427608 | 156.7 | -14.1 | 4.759 |
| 17 | 6.531067 | 10.193538 | 340.226704 | 10.193538 | 79.440 | 2011-02-01 00:00:00+00:00 | 5.1540 | 157.071121 | -13.480325 | 156.7 | -14.1 | 4.763 |
| 18 | 6.970117 | 10.878797 | 301.690431 | 10.878797 | 136.290 | 2011-02-01 00:00:00+00:00 | 4.6980 | 157.010422 | -12.906148 | 156.7 | -14.1 | 4.765 |
| 19 | 6.307313 | 9.844308 | 297.468306 | 9.844308 | 185.091 | 2011-02-01 00:00:00+00:00 | 4.9040 | 156.961929 | -12.446772 | 156.7 | -14.1 | 4.768 |
Visualising the relevant altimeters date¶
RADWave provides a plotting function to visualise the processed wave data called plotCycloneAltiPoint that can be used to also provide information about wave parameters for each data point (by turning the showinfo flag to True as explained in the API.
An example of how to call this function is presented below:
wa_west.plotCycloneAltiPoint(showinfo=False, extent=[138, 180, -18, -10],
markersize=35, zoom=4, fsize=(12, 5))
/Users/getafix/anaconda3/envs/coast/lib/python3.6/site-packages/cartopy/mpl/gridliner.py:307: UserWarning: The .xlabels_top attribute is deprecated. Please use .top_labels to toggle visibility instead.
warnings.warn('The .xlabels_top attribute is deprecated. Please '
/Users/getafix/anaconda3/envs/coast/lib/python3.6/site-packages/cartopy/mpl/gridliner.py:331: UserWarning: The .ylabels_left attribute is deprecated. Please use .left_labels to toggle visibility instead.
warnings.warn('The .ylabels_left attribute is deprecated. Please '
wa_west.plotCycloneAltiPoint(showinfo=True, extent=[138, 180, -18, -10],
markersize=35, zoom=4, fsize=(12, 5))
++++++++++++++++++++++++++++++++++++++++++++
Considered cyclone path point (160.9,-13.6) at 2011-01-31 12:00:00+00:00
Altimeter point (159.1,-14.5) records dt: -0.42h
+ Power 265.0 kW/m
+ Energy 10.55 J/m2
+ Celerity 10.55 m/s
+ Period 6.76 s
+ Height 4.47 m
Altimeter point (159.0,-13.9) records dt: -0.42h
+ Power 197.84 kW/m
+ Energy 9.82 J/m2
+ Celerity 9.82 m/s
+ Period 6.29 s
+ Height 4.0 m
Altimeter point (160.8,-12.5) records dt: 4.48h
+ Power 215.4 kW/m
+ Energy 8.41 J/m2
+ Celerity 8.41 m/s
+ Period 5.39 s
+ Height 4.52 m
Altimeter point (160.7,-13.4) records dt: 4.48h
+ Power 645.88 kW/m
+ Energy 10.52 J/m2
+ Celerity 10.52 m/s
+ Period 6.74 s
+ Height 6.99 m
Altimeter point (160.6,-14.7) records dt: 4.49h
+ Power 917.57 kW/m
+ Energy 12.62 J/m2
+ Celerity 12.62 m/s
+ Period 8.08 s
+ Height 7.61 m
Altimeter point (160.5,-15.5) records dt: 4.49h
+ Power 1129.56 kW/m
+ Energy 15.02 J/m2
+ Celerity 15.02 m/s
+ Period 9.62 s
+ Height 7.74 m
++++++++++++++++++++++++++++++++++++++++++++
Considered cyclone path point (160.0,-13.7) at 2011-01-31 14:00:00+00:00
Altimeter point (159.4,-15.5) records dt: -2.43h
+ Power 272.6 kW/m
+ Energy 10.77 J/m2
+ Celerity 10.77 m/s
+ Period 6.9 s
+ Height 4.49 m
Altimeter point (159.1,-14.5) records dt: -2.42h
+ Power 265.0 kW/m
+ Energy 10.55 J/m2
+ Celerity 10.55 m/s
+ Period 6.76 s
+ Height 4.47 m
Altimeter point (159.0,-13.9) records dt: -2.42h
+ Power 197.84 kW/m
+ Energy 9.82 J/m2
+ Celerity 9.82 m/s
+ Period 6.29 s
+ Height 4.0 m
Altimeter point (158.9,-13.4) records dt: -2.42h
+ Power 182.33 kW/m
+ Energy 9.91 J/m2
+ Celerity 9.91 m/s
+ Period 6.35 s
+ Height 3.83 m
Altimeter point (158.7,-12.5) records dt: -2.41h
+ Power 114.02 kW/m
+ Energy 9.46 J/m2
+ Celerity 9.46 m/s
+ Period 6.06 s
+ Height 3.1 m
Altimeter point (160.8,-12.5) records dt: 2.48h
+ Power 215.4 kW/m
+ Energy 8.41 J/m2
+ Celerity 8.41 m/s
+ Period 5.39 s
+ Height 4.52 m
Altimeter point (160.7,-13.4) records dt: 2.48h
+ Power 645.88 kW/m
+ Energy 10.52 J/m2
+ Celerity 10.52 m/s
+ Period 6.74 s
+ Height 6.99 m
Altimeter point (160.6,-14.7) records dt: 2.49h
+ Power 917.57 kW/m
+ Energy 12.62 J/m2
+ Celerity 12.62 m/s
+ Period 8.08 s
+ Height 7.61 m
Altimeter point (160.5,-15.5) records dt: 2.49h
+ Power 1129.56 kW/m
+ Energy 15.02 J/m2
+ Celerity 15.02 m/s
+ Period 9.62 s
+ Height 7.74 m
++++++++++++++++++++++++++++++++++++++++++++
Considered cyclone path point (156.7,-14.1) at 2011-02-01 00:00:00+00:00
Altimeter point (157.3,-15.5) records dt: 4.75h
+ Power 1659.79 kW/m
+ Energy 15.86 J/m2
+ Celerity 15.86 m/s
+ Period 10.16 s
+ Height 9.12 m
Altimeter point (157.2,-14.4) records dt: 4.76h
+ Power 866.4 kW/m
+ Energy 12.24 J/m2
+ Celerity 12.24 m/s
+ Period 7.84 s
+ Height 7.51 m
Altimeter point (157.1,-13.5) records dt: 4.76h
+ Power 340.23 kW/m
+ Energy 10.19 J/m2
+ Celerity 10.19 m/s
+ Period 6.53 s
+ Height 5.15 m
Altimeter point (157.0,-12.9) records dt: 4.76h
+ Power 301.69 kW/m
+ Energy 10.88 J/m2
+ Celerity 10.88 m/s
+ Period 6.97 s
+ Height 4.7 m
Altimeter point (157.0,-12.4) records dt: 4.77h
+ Power 297.47 kW/m
+ Energy 9.84 J/m2
+ Celerity 9.84 m/s
+ Period 6.31 s
+ Height 4.9 m
As already mentioned in the different examples, the class waveAnalysis() saves most of the processed wave data as Pandas dataframe (such as
timeseriesorcyclone_data) that can be used for further analysis.