Persistenz

Ähnlichkeit von aufeinanderfolgenden Strömungsmustern über Europa

Europa/Deutschland: 50-55°N / 10-15°E

WPI ~ tas	WPI ~ pr

Abb.: Eine hohe Persistenz von großräumigen Strömungsmustern im Sommer über Europa können in Ostdeutschland sowohl eine heiße und trockene (bspw. 2018) aber auch warme und feuchte (bspw. 2017) Saison begünstigen.

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Code

Importing

import sys
import os
import matplotlib
matplotlib.use('Agg') # Must be before importing matplotlib.pyplot or pylab!
import matplotlib.pyplot as P
from mpl_toolkits.basemap import Basemap,shiftgrid
from netCDF4 import Dataset
import numpy as N
from scipy import stats as S
from scipy import signal

params = {      'legend.fontsize': 12,\
                'font.family': 'serif',\
                }
P.rcParams.update(params)
P.style.use('bmh')  

Running

jo = N.arange(1981,2020,1)

for p in ['tas','pr']:

    nc = Dataset('../data/era/wpi/1981-2019_JJA_abs.cdf','r')

    ssim = N.array(nc.variables['persistence'][:])

    nc.close()

    nc = Dataset('../data/era/%s/1981-2019_JJA_abs.cdf'%p,'r')

    data = N.array(nc.variables[p][:])

    lon = N.array(nc.variables['lon'][:]);nx = len(lon)
    lat = N.array(nc.variables['lat'][:]);ny = len(lat)

    nc.close()

    if(p=='pr'): data = data*24.*3600.
    if(p=='tas'): data = data-273.14

    ###############################

    P.figure(figsize=(6,4))
    ax = P.subplot(111)

    iy = N.where((lat>50)&(lat<55))[0]
    ix = N.where((lon>10)&(lon<15))[0]

    nj = 38

    xx = ssim[:,iy,:]
    yy = data[:,iy,:]

    xx = xx[:,:,ix]
    yy = yy[:,:,ix]

    values = N.vstack([xx.ravel(),yy.ravel()])
    kernel = S.gaussian_kde(values)

    if(p=='tas'): X, Y = N.mgrid[64:76:100j,14:20:100j]
    if(p=='pr'): X, Y = N.mgrid[64:76:100j,6:14:100j]

    positions = N.vstack([X.ravel(), Y.ravel()])
    Z2 = N.reshape(kernel(positions).T, X.shape)    

    if(p=='tas'): 

        CF=P.contourf(X,Y,Z2*100.,N.arange(1,8,1),cmap=P.cm.Reds,extend='max')
        P.contour(X,Y,Z2*100.,N.arange(1,8,1),colors='k')    

    if(p=='pr'): 

        CF=P.contourf(X,Y,Z2*100.,N.arange(1,8,1),cmap=P.cm.Blues,extend='max')
        P.contour(X,Y,Z2*100.,N.arange(1,8,1),colors='k')

    for j in jo:

        id = N.where(jo==j)[0]
        id = id[0]

        P.scatter(N.mean(xx[id,:,:]),N.mean(yy[id,:,:]),s=300,c='None',ec='k',lw=1,zorder=5)

    id = N.where(jo==2018)[0]
    id = id[0]

    P.scatter(N.mean(xx[id,:,:]),N.mean(yy[id,:,:]),s=300,c='None',ec='dodgerblue',lw=3,zorder=10,label='2018')

    id = N.where(jo==2017)[0]
    id = id[0]

    P.scatter(N.mean(xx[id,:,:]),N.mean(yy[id,:,:]),s=300,c='None',ec='orangered',lw=3,zorder=10,label='2017')

    P.xlim(64,76)
    if(p=='tas'):P.ylim(14,20)
    if(p=='pr'):P.ylim(6,14)
    P.grid(color='k')
    P.legend(loc=2,shadow=True)

    P.tick_params(direction='out')
    P.xlabel('Weather Persistence Index (%)',fontsize=16,weight='bold')
    if(p=='tas'): P.ylabel('Temperature ($^\circ$C)',fontsize=16,weight='bold')
    if(p=='pr'): P.ylabel('Precipitation (mm)',fontsize=16,weight='bold')

    P.savefig('./img/wpi-%s0.png'%p,dpi=240,transparent=False,bbox_inches='tight',pad_inches=0.0)