Tmax

Verteilungsdichte der Schadensmeldungen in Abhängigkeit von der Maximaltemperatur

Ergebnis

Hohe Temperaturen begünstigen die Gleichzeitigkeit von Störmeldungen, weil diese zumeist großräumig auftreten.

Abb.: Verteilungsdichte der Störmeldungen (N) in Abhängigkeit von der Tagesmaximumtemperatur (Tmax) über alle PDs: N>0 und N>40.

---

Code

Module

# -*- coding: utf-8 -*-
import sys
import numpy as N
import shapefile
import scipy.stats as stats
import pylab as P
from matplotlib.collections import PatchCollection
from pylab import Polygon
from mpl_toolkits.basemap import Basemap

P.style.use('bmh')

params = {'legend.fontsize': 8,'font.family': 'serif'}
P.rcParams.update(params)

Reading

tmp=N.genfromtxt('../shp/GP.csv',names=True,comments='#',delimiter=';',dtype=None)#,encoding='UTF-8')
pds = tmp['Name']

print (pds)

Plotting

filter = 'Filter1'

P.figure(figsize=(8,5))

xx = []
yy = []

for pd in pds: 

        pd = pd.replace('\xfc', 'ü').replace('\xf6', 'ö')

        file = '../csv/obs-dwd/'+pd+'.csv'
        data=N.genfromtxt(file,names=True,comments='#',delimiter=';',dtype=None,encoding='utf-8')

        id = N.where((data['ja']>=2015)&(data['ja']<=2019))[0]

        tmax = data['tmax'][id] 
        wmax = data['wmax'][id]
        nied = data['nied'][id]
        mona = data['mo'][id]

        file = '../csv/pds-uas/'+pd+'.csv'
        data=N.genfromtxt(file,names=True,comments='#',delimiter=';',dtype=None,encoding='utf-8')
        id = N.where((data['ja']>=2015)&(data['ja']<=2019))[0]

        uas = data[filter][id]

        id = N.where((uas>0))[0]
        #id = N.where((uas>0)&(wmax<10))[0]

        for i in id:

            xx.append(tmax[i])
            yy.append(uas[i])   

xx = N.array(xx)
yy = N.array(yy)

for sw in [0,40]:

    id = N.where((yy>sw)&(xx>2))[0]

    xs = xx[id]
    ys = yy[id]

    values = N.vstack([xs,ys])
    kernel = stats.gaussian_kde(values)            
    X, Y = N.mgrid[-10:40:200j,0:100:200j]
    positions = N.vstack([X.ravel(),Y.ravel()])
    Z2 = N.reshape(kernel(positions).T, X.shape)

    #xnew = N.linspace(-10,40,num=200)
    #density = stats.kde.gaussian_kde(xx)
    #Z3 = density(xnew)

    lev = N.arange(0.2,5.0,0.2)

    CF=P.contourf(X,Y,1000*Z2,levels=lev,cmap=P.cm.YlOrRd,zorder=1)
    P.contour(X,Y,1000*Z2,levels=lev,colors='k',linewidths=0.5,zorder=1)

P.text(0,45,'N>40',size=22,weight='bold',ha='center',va='center',bbox=dict(boxstyle="circle", ec='k', fc='gray',alpha=0.5,lw=2))

P.tick_params(direction='out')
P.xlim(-5,40)
P.ylim(0,60)

P.grid(color='k')
P.xlabel('tmax [$^\circ$C]',fontsize=16,weight='bold')
P.ylabel('Meldungen pro Tag je PD',fontsize=16,weight='bold')
P.title('Meldungen ('+filter+') ~ Tmax',fontsize=16,weight='bold')

P.savefig('./img/tmax_'+filter+'.png',dpi=240,bbox_inches='tight')