Irish Gale of 1843

See also

Video version

../_images/V3vV2c_IG_1843011312.png

MSLP Contours for v2c (left) and v3 (right)

The thin blue lines are mslp contours from each of 56 ensemble members (all members for v2c, the first 56 members for v3). The thicker black lines are contours of the ensemble mean. The yellow dots mark pressure observations assimilated while making the field shown.

The 1843 Irish Gale is described in Stephen Burt’s paper on Extreme British Isles depressions and Symons Magazine in 1892 (page 164).

Download the data required:

import Meteorographica.data.twcr as twcr

twcr.fetch('prmsl',1843,1,version='4.5.1')
twcr.fetch_observations(1843,1,version='4.5.1')

# 20CRv2c has no released data for 1843 - I've used 
#  data from scout 3.5.4 instead, but this is not
#  publically available.

Make the figure:

# UK region weather plot 
# Compare pressures from 20CRV3 and 20CRV2c

import math
import datetime
import numpy
import pandas

import iris
import iris.analysis

import matplotlib
from matplotlib.backends.backend_agg import \
             FigureCanvasAgg as FigureCanvas
from matplotlib.figure import Figure

import cartopy
import cartopy.crs as ccrs

import Meteorographica.weathermap as wm
import Meteorographica.data.twcr as twcr

# Date to show
year=1843
month=1
day=13
hour=12
dte=datetime.datetime(year,month,day,hour)

# Landscape page
fig=Figure(figsize=(22,22/math.sqrt(2)),  # Width, Height (inches)
           dpi=100,
           facecolor=(0.88,0.88,0.88,1),
           edgecolor=None,
           linewidth=0.0,
           frameon=False,
           subplotpars=None,
           tight_layout=None)
canvas=FigureCanvas(fig)

# UK-centred projection
projection=ccrs.RotatedPole(pole_longitude=180, pole_latitude=35)
scale=15
extent=[scale*-1,scale,scale*-1*math.sqrt(2),scale*math.sqrt(2)]

# Two side-by-side plots
ax_2c=fig.add_axes([0.01,0.01,0.485,0.98],projection=projection)
ax_2c.set_axis_off()
ax_2c.set_extent(extent, crs=projection)
ax_3=fig.add_axes([0.505,0.01,0.485,0.98],projection=projection)
ax_3.set_axis_off()
ax_3.set_extent(extent, crs=projection)

# Background, grid and land for both
ax_2c.background_patch.set_facecolor((0.88,0.88,0.88,1))
ax_3.background_patch.set_facecolor((0.88,0.88,0.88,1))
wm.add_grid(ax_2c)
wm.add_grid(ax_3)
land_img_2c=ax_2c.background_img(name='GreyT', resolution='low')
land_img_3=ax_3.background_img(name='GreyT', resolution='low')

# Add the observations from 2c
obs=twcr.load_observations_fortime(dte,version='2c')
wm.plot_obs(ax_2c,obs,radius=0.1)

# load the 2c pressures
prmsl=twcr.load('prmsl',year,month,day,hour,
                                version='2c')

# For each ensemble member, make a contour plot
for m in range(0, 56):
    prmsl_e=prmsl.extract(iris.Constraint(member=m))
    prmsl_e.data=prmsl_e.data/100 # To hPa
    CS=wm.plot_contour(ax_2c,prmsl_e,
                   levels=numpy.arange(870,1050,10),
                   colors='blue',
                   label=False,
                   linewidths=0.1)

# Add the ensemble mean - with labels
prmsl_m=prmsl.collapsed('member', iris.analysis.MEAN)
prmsl_m.data=prmsl_m.data/100 # To hPa
prmsl_s=prmsl.collapsed('member', iris.analysis.STD_DEV)
prmsl_s.data=prmsl_s.data/100
# Mask out mean where uncertainties large
#prmsl_m.data[numpy.where(prmsl_s.data>3)]=numpy.nan
CS=wm.plot_contour(ax_2c,prmsl_m,
                   levels=numpy.arange(870,1050,10),
                   colors='black',
                   label=True,
                   linewidths=2)

# 20CR2c label
wm.plot_label(ax_2c,'20CR scout 534',
                     facecolor=fig.get_facecolor(),
                     x_fraction=0.02,
                     horizontalalignment='left')

# V3 panel

# Add the observations from v3
obs=twcr.load_observations_fortime(dte,version='4.5.1')
wm.plot_obs(ax_3,obs,radius=0.1)

# load the V3 pressures
prmsl=twcr.load('prmsl',year,month,day,hour,
                                version='4.5.1')

# For each ensemble member, make a contour plot
for m in range(1,57): # Same number as 2c
    prmsl_e=prmsl.extract(iris.Constraint(member=m))
    prmsl_e.data=prmsl_e.data/100 # To hPa
    CS=wm.plot_contour(ax_3,prmsl_e,
                   levels=numpy.arange(870,1050,10),
                   colors='blue',
                   label=False,
                   linewidths=0.1)

# Add the ensemble mean - with labels
prmsl_m=prmsl.collapsed('member', iris.analysis.MEAN)
prmsl_m.data=prmsl_m.data/100 # To hPa
prmsl_s=prmsl.collapsed('member', iris.analysis.STD_DEV)
prmsl_s.data=prmsl_s.data/100
# Mask out mean where uncertainties large
#prmsl_m.data[numpy.where(prmsl_s.data>3)]=numpy.nan
CS=wm.plot_contour(ax_3,prmsl_m,
                   levels=numpy.arange(870,1050,10),
                   colors='black',
                   label=True,
                   linewidths=2)

wm.plot_label(ax_3,'20CR v3',
                     facecolor=fig.get_facecolor(),
                     x_fraction=0.02,
                     horizontalalignment='left')

wm.plot_label(ax_3,
              '%04d-%02d-%02d:%02d' % (year,month,day,hour),
              facecolor=fig.get_facecolor(),
              x_fraction=0.98,
              horizontalalignment='right')

# Output as png
fig.savefig('V3vV2c_IG_%04d%02d%02d%02d.png' % 
                                  (year,month,day,hour))