Daria’s sister storm of 1884

See also

Video version

../_images/V3vV2c_DS_1884012700.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.

Storm Daria in 1990 is an architype of a damaging European windstorm, but it might not be the worst we can expect. This storm in 1884 is called “Daria’s big sister” as it might be a useful example of an even-more-severe storm, if we can reconstruct it well enough. This storm produced the lowest pressure reading ever recorded over the British Isles and continental Europe. In Ochtertyre, close to the town of Crieff in Scotland, the barometer dropped to 925.6 hPa (Marriott 1884).

Download the data required:

import IRData.twcr as twcr
import datetime

dte=datetime.datetime(1884,1,1)
for version in ('2c','4.5.1'):
    twcr.fetch('prmsl',dte,version=version)
    twcr.fetch_observations(dte,version=version)

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 as mg
import IRData.twcr as twcr

# Date to show
year=1884
month=1
day=27
hour=0
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))
mg.background.add_grid(ax_2c)
mg.background.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')
mg.observations.plot(ax_2c,obs,radius=0.15)

# load the 2c pressures
prmsl=twcr.load('prmsl',dte,version='2c')

# Contour spaghetti plot of ensemble members
mg.pressure.plot(ax_2c,prmsl,scale=0.01,type='spaghetti',
                   resolution=0.25,
                   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)
mg.pressure.plot(ax_2c,prmsl_m,scale=0.01,
                   resolution=0.25,
                   levels=numpy.arange(870,1050,10),
                   colors='black',
                   label=True,
                   linewidths=2)

# 20CR2c label
mg.utils.plot_label(ax_2c,'20CR 2c',
                     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')
mg.observations.plot(ax_3,obs,radius=0.15)

# load the V3 pressures
prmsl=twcr.load('prmsl',dte,version='4.5.1')

# Contour spaghetti plot of ensemble members
# Only use 56 members to match v2c
prmsl_r=prmsl.extract(iris.Constraint(member=range(0,56)))
mg.pressure.plot(ax_3,prmsl_r,scale=0.01,type='spaghetti',
                   resolution=0.25,
                   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)
mg.pressure.plot(ax_3,prmsl_m,scale=0.01,
                   resolution=0.25,
                   levels=numpy.arange(870,1050,10),
                   colors='black',
                   label=True,
                   linewidths=2)

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

mg.utils.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_DS_%04d%02d%02d%02d.png' % 
                                  (year,month,day,hour))