https://github.com/miniufo/xvortices
This is a package for interpolating lat/lon gridded data onto a cylindrical coordinate translating with various kinds of vortices on Earth.
https://github.com/miniufo/xvortices
interpolation lagrangian mesoscale-eddies oceanic-eddies tropical-cyclone vortex vortices xarray
Last synced: 11 months ago
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This is a package for interpolating lat/lon gridded data onto a cylindrical coordinate translating with various kinds of vortices on Earth.
- Host: GitHub
- URL: https://github.com/miniufo/xvortices
- Owner: miniufo
- License: mit
- Created: 2021-11-11T03:38:45.000Z (over 4 years ago)
- Default Branch: master
- Last Pushed: 2022-06-17T12:10:27.000Z (almost 4 years ago)
- Last Synced: 2024-01-27T16:08:53.618Z (over 2 years ago)
- Topics: interpolation, lagrangian, mesoscale-eddies, oceanic-eddies, tropical-cyclone, vortex, vortices, xarray
- Language: Jupyter Notebook
- Homepage: https://xvortices.readthedocs.io/
- Size: 59.3 MB
- Stars: 9
- Watchers: 2
- Forks: 7
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# xvortices
[](https://zenodo.org/badge/latestdoi/426860265)
[](https://xvortices.readthedocs.io/en/latest/?badge=latest)
## 1. Introduction
`xvortices` is a python package built on [`xarray`](http://xarray.pydata.org/en/stable/) (starts with an `x`), targeting at extracting information about moving vortices from lat/lon gridded datasets. Moving vortices include [tornado](https://en.wikipedia.org/wiki/Tornado), [tropical cyclone](https://en.wikipedia.org/wiki/Tropical_cyclone), [extratropical cyclone](https://en.wikipedia.org/wiki/Extratropical_cyclone), [polar vortex](https://en.wikipedia.org/wiki/Polar_vortex) in the atmospheric context, as well as [mesoscale eddy](https://en.wikipedia.org/wiki/Eddy_(fluid_dynamics)) and [ocean gyre](https://en.wikipedia.org/wiki/Ocean_gyre) in the oceanic context. These moving vortices are usually described in a moving (also known as quasi-Lagrangian) cylindrical coordinate. As the coordinate system moves on the spherical earth as a whole, one could take a view of the vortex dynamics from a quasi-Lagrangian perspective instead of the traditional Eulerian perspective.
Basically, this package would do the following jobs:
- accept an `xarray.Dataset` (or a list of `xarray.DataArray`) as an input dataset, usually in a fashion of lat/lon grid;
- interpolate the data onto the cylindrical coordinates once the origin of the moving coordinate is given (this is naturally done using the builtin function of `xarray`);
- return the interpolated fields, including scalars and vectors;
- re-project the vectors onto the azimuthal/radial directions;
With this tool, one can perform quasi-Lagrangian diagnoses of the structure, evolution, budget, intensity etc in a perspective different from the Eulerian one.
---
## 2. How to install
**Requirements**
`xvortices` is developed under the environment with `numpy` (=version 1.15.4), `xarray` (=version 0.15.1), `matplotlib` (=version 3.4.3) and `cartopy` (=version 0.18.0). Older versions of these packages are not well tested.
**Install from github**
```
git clone https://github.com/miniufo/xvortices.git
```
---
## 3. Examples
### 3.1 A moving tropical cyclone
Here we demonstrate an application to the case of a moving tropical cyclone (TC) over the western North Pacific. One may need [`besttracks`](https://github.com/miniufo/besttracks) to load TC best-track data and cooperate with `xvortices`:
```python
import xarray as xr
from xvortices import load_cylind, project_to_cylind
azimNum, radiNum, radMax = 72, 31, 6
# variables inside are [u, v, w, h]
dset = xr.open_dataset('gridded.nc')
# one can obtain tropical cyclone (TC) best-track
# data from the `besttracks` package
olon = TC.get_as_xarray('lon') # timeseries of center longitudes
olat = TC.get_as_xarray('lat') # timeseries of center latitudes
uovel = TC.get_as_xarray('uo') # timeseries of center u-vel
vovel = TC.get_as_xarray('vo') # timeseries of center v-vel
[u, v, w, h], lons, lats, etas = load_cylind(dset, olon=olon, olat=olat,
azimNum=azimNum, radiNum=radiNum,
radMax=radMax)
urel = u - uovel # storm-relative u
vrel = v - vorel # storm-relative v
# storm-relative azimuthal/radial winds
uaz, vra = project_to_cylind(urel, vrel, etas)
```
Plotting its 3D structure is also easy:
```python
from xvortices import plot3D
# select the first time step to show
plot3D(lons[0], lats[0], uaz[0])
```
More details can be found at this [TC notebook](./notebooks/1.TCExample.ipynb).
---
### 3.2 A moving mesoscale eddy
This is a mesoscale eddy case over the southern Indian Ocean.
```python
import pandas as pd
import xarray as xr
from xvortices import load_cylind, project_to_cylind
# load an eddy positions from the "Mesoscale Eddy Trajectory Atlas" product
eddy = pd.read_csv('d:/SETIO2021.txt', sep='\s+', index_col='time', parse_dates=True)
# load AVISO gridded sea level anomaly and associated flow
dset = xr.open_dataset('D:/dataset-duacs-nrt-global-merged-allsat-phy-l4_SETIO_Eddy.nc')
# parameters of the cylindrical coordinates
azimNum, radiNum, radMax = 72, 31, 3
# interpolate from lat/lon grid to cylindrical grid
azimNum, radiNum, radMax = 72, 31, 4
[sla, adt, ugos, vgos], lons, lats, etas = load_cylind(dset[['sla','adt','ugos','vgos']],
olon=eddy.lons.to_xarray(),
olat=eddy.lats.to_xarray(),
azimNum=azimNum, radiNum=radiNum,
radMax=radMax,
lonname='longitude',
latname='latitude')
# calculate eddy's translating speed
uo = (eddy['lons'].diff()/86400*np.cos(np.deg2rad(eddy['lats']))).to_xarray()
vo = (eddy['lats'].diff()/86400).to_xarray()
# calculate eddy-relative current components
u_r = ugos - uo
v_r = vgos - vo
# project u/v to azimuthal/radial components
uaz, vra = project_to_cylind(u_r, v_r, etas)
```
More details can be found at this [notebook](./notebooks/2.EddyExample.ipynb).