Galveston Hurricane (1900) (spinup comparison)ΒΆ

See also

Video version

../_images/V3vV2c_Galveston_19000908061.png

MSLP Contours for the spinup stream (left) and the production stream (right)

The thin blue lines are mslp contours from each of 56 ensemble members (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 red dots are the IBTRACS best-track observations for unnamed tropical storms - the southernmost is the Galveston Hurricane.

Data for this period are available from two 20CRv3 streams. The production stream starting in September 1894, and a subsequent stream starting in September 1899, which is imperfectly spun-up by the date of the hurricane. This is a comparison of the two streams.


Make the figure:

#!/usr/bin/env python

# US region weather plot 
# Compare pressures from 20CRV3 spinup and operational versions

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=1900
month=9
day=8
hour=0o6
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)

# US-centred projection
projection=ccrs.RotatedPole(pole_longitude=110, pole_latitude=56)
scale=30
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 spinup
obs=twcr.load_observations_fortime(dte,version='4.5.1.spinup')
mg.observations.plot(ax_2c,obs,radius=0.15)
# Highlight the Hurricane obs
obs_h=obs[obs.Name=='NOT NAMED']
if not obs_h.empty:
    mg.observations.plot(ax_2c,obs_h,radius=0.25,facecolor='red',
                         zorder=100)

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

# Contour spaghetti plot of ensemble members
prmsl_r=prmsl.extract(iris.Constraint(member=list(range(0,56))))
mg.pressure.plot(ax_2c,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_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,'20CR3 spinup',
                     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)
# Highlight the Hurricane obs
obs_h=obs[obs.Name=='NOT NAMED']
if not obs_h.empty:
    mg.observations.plot(ax_3,obs_h,radius=0.25,facecolor='red',
                         zorder=100)

# 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=list(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,'20CR3 production',
                     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_Galveston_%04d%02d%02d%02d.png' % 
                                  (year,month,day,hour))