jcclass 0.0.4

Jenkinson and Collison automated gridded classification

Jenkinson - Collison automated gridded classification for Python

This is an adapted version for python of the Jenkinson - Collison automated classfication based on the original Lamb Weather Types. This gridded version is based on the application made by Otero (2018) using a moving central gridded point with that allows to compute the synoptic circulation types on a gridded Mean Sea Level Pressure (MSLP) domain.

How does it work?

The method uses grid-point MSLP data to obtain numerical values of wind flow and vorticity which can be used to determine Cyclonic and Anticyclonic patterns as well as their dominant advective (direction of wind flow) characteristics. The 16 gridded points are moved along the region in reference to a central point where the dominant circulation type will be designated.

The Circulation Types (CTs)

The application of the automated classification allows to derive 27 synoptic circulations. 26 of them based on the dominant pressure pattern and wind direction plus a Low Flow (LF) type which is characterised by days when pressure gradients are to weak and a dominant circulation or advective direction can not be assigned.

Name	Abreviation	Coding	Name	Abreviation	Coding	Name	Abreviation	Coding
Low Flow	LF	-1
Anticyclonic	A	0				Cyclonic	C	20
Anticyclonic Northeasterly	ANE	1	Northeasterly	NE	11	Cyclonic Northeasterly	CNE	21
Anticyclonic Easterly	AE	2	Easterly	E	12	Cyclonic Easterly	CE	22
Anticyclonic Southeasterly	ASE	3	Southeasterly	SE	13	Cyclonic Southeasterly	CSE	23
Anticyclonic Southerly	AS	4	Southerly	S	14	Cyclonic Southerly	CS	24
Anticyclonic Southwesterly	ASW	5	Southwesterly	SW	15	Cyclonic Southwesterly	CSW	25
Anticyclonic Westerly	AW	6	Westerly	W	16	Cyclonic Westerly	CW	26
Anticyclonic Northwesterly	ANW	7	Northwesterly	NW	17	Cyclonic Northwesterly	CNW	27
Anticyclonic Northerly	AN	8	Northerly	N	18	Cyclonic Northerly	CN	28

The original 27 circulations can be reduced to a set of 11 patterns based on their dominant advection.

Name	Abreviation	Coding
Low Flow	LF	-1
Anticyclonic	A	0
Northeasterly	NE	1
Easterly	E	2
Southeasterly	SE	3
Southerly	S	4
Southwesterly	SW	5
Westerly	W	6
Northwesterly	NW	7
Northerly	N	8
Cyclonic	C	9

Working datasets

The current code has been has been tested for the following datasets:

• ERA5 Reanalysis -NOAA 20th Century Reanalysis (V3)
• Global Climate Models from the Coupled Model Intercomparison Project (CMIP6)

The method can be applied for any other netcdf files with latitude coordinates names as "latitude" or "lat", or longitudes coordinates as "longitude" or "lon" and MSLP coordinate names as "msl" or "psl".

Sample datasets from ERA5 is provided and available here

Installation

Simply run in the terminal

pip install jcclass

How to use?

Importing the module

from jcclass import jc

Computing the automated circulation types based on gridded MSLP

Sample datasets available here.

filename = 'era5_daily_lowres.nc'
cts_27 = jc(filename).classification()

Computing the reduced eleven circulation types

cts_11 = jc.eleven_cts(cts_27)

Ploting the circulation types on a map

import xarray as xr
date = cts_27.time[0] #selecting the first time
cts = cts_27.sel(time = date)
fig = jc.plot_cts(cts, *args)

• cts : a 2D['lat','lon'] xarray.DataArray of the 27 CTs
• *args : int, optional (lat_south, lat_north, lon_west, lon_east)

Plotting the circulation types and MSLP contour lines on a map

import xarray as xr
mslp = xr.open_dataset(filename).sel(time = date)
fig = jc.plot_cts_mslp(cts, mslp, *args)

• cts : a 2D['lat','lon'] xarray.DataArray of the 27 CTs
• mslp : a 2D['lat','lon'] xarray.Dataset of MSLP
• *args : int, optional (lat_south, lat_north, lon_west, lon_east)
  

Plotting the circulation types and MSLP contour lines on a Nearside perspective cartopy projection

fig = jc.plot_cts_globe(cts, mslp, *argsglobe)

• cts : a 2D['lat','lon'] xarray.DataArray of the 27 CTs
• mslp : a 2D['lat','lon'] xarray.Dataset of MSLP
• *argsglobe : int, optional (lat_central=30, lon_central=0)
  

Saving the figures

You can save anytime any of the figures using fig.savefig.

fig.savefig('figname.png', dpi = 150)

Documentation

Detailed documentation and tutorial on the jcclass module can be found in the Documentation

Acknowledging this work

The code can be used and modified freely without any restriction. If you use it for your own research, I would appreciate if you cite this work as follows:

Herrera-Lormendez P., 2022: PedroLormendez/jcclass: version x.y.z doi:10.5281/zenodo.7025220

Reports on errors are welcomed by raising an issue

Further literature on the method

• Jenkinson AF, Collison FP. 1977. An Initial Climatology of Gales over the North Sea. Synoptic Climatology Branch Memorandum, No. 62., Meteorological Office, Bracknell.
• Lamb HH. 1972. British Isles weather types and a register of daily sequence of circulation patterns, 1861-1971: Geophysical Memoir. HMSO.
• Jones PD, Hulme M, Briffa KR. 1993. A comparison of Lamb circulation types with an objective classification scheme. International Journal of Climatology. John Wiley & Sons, Ltd, 13(6): 655–663. https://doi.org/10.1002/joc.3370130606.
• Otero N, Sillmann J, Butler T. 2018. Assessment of an extended version of the Jenkinson–Collison classification on CMIP5 models over Europe. Climate Dynamics. Springer Verlag, 50(5–6): 1559–1579. https://doi.org/10.1007/s00382-017-3705-y.