Kritikalität

Monatliche Kritikalität von Großwetterlagen

Ergebnis

Welche monatlichen Eigenschaften Großwetterlagen (GWL) innerhalb des Zeitraum 2015-2019 aufweisen zeigt Abb.1.: HFA im Juli ist besonders heiß, TM im Juli ist besonders feucht und NWZ im März sehr stürmig. Diese GWL liegen im Mittelfeld der Häufigkeit.

Matrix

Abb.1: Monatliche Charakteristik von Großwetterlagen für den Zeitraum 2015-2019: Temperatur (o.l.), Niederschlag (o.r.), Windspitzen (m.l.), Meldungen (m.r.) und Häufigkeit (u.).

• leere Felder: diese GWL sind innerhalb des Zeitraums nicht aufgetreten.

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Kritikalität

Abb.2: Hitliste von monatlichen Großwetterlagen nach Meldungen für den Zeitraum 2015-2019. Beobachtete Änderungssignale 1961-1990 (blau) vs 1990-2019 (rot).

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Code

Python

Importing

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
import matplotlib as mpl
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']

file = '../dat/gwlneudatum.dat'

dd=N.genfromtxt(file,usecols=(0),skip_header=0,dtype="I")
mm=N.genfromtxt(file,usecols=(1),skip_header=0,dtype="I")
jj=N.genfromtxt(file,usecols=(2),skip_header=0,dtype="I")
gw=N.genfromtxt(file,usecols=(3),skip_header=0,dtype="S")

id = N.where((jj>=2015)&(jj<=2019))[0]
ni = len(id)
id = id[:ni-1]

dd = dd[id]
mm = mm[id]
jj = jj[id]
gw = gw[id]

#go = N.array(['HNA'])#,'HFZ','WA','SWA','BM','NEA','WW','HFA','SWZ','NEZ','HM','HB','HNZ','WZ','NA','SEZ','NWZ','NZ','TM','NWA','SEA','TB','TRM','HNFA','HNFZ','U','TRW','SZ','WS','SA'])
go = N.array(list(set(gw)))
ng = len(go)
mo = N.arange(1,13,1)
nm = len(mo)

Processing

filter = 'Filter1'

paras = {'tmax':[0,35,'Spectral_r'],'nied':[1,8,'YlGnBu','[]'],'wmax':[5,20,'gist_stern_r'],'bahn':[10,30,'RdPu']}

np = 34

tmax = N.zeros((np,ng,nm),float);tmax[:,:,:] = N.nan
nied = N.zeros((np,ng,nm),float);nied[:,:,:] = N.nan
wmax = N.zeros((np,ng,nm),float);wmax[:,:,:] = N.nan
bahn = N.zeros((np,ng,nm),float);bahn[:,:,:] = N.nan
gwls = N.zeros((ng,nm),float);gwls[:,:] = N.nan

m = Basemap(projection='merc',llcrnrlat=47,urcrnrlat=55.2,llcrnrlon=5,urcrnrlon=16,resolution='l')
m.readshapefile('../shp/OSS_M1_PROD_DURCH_FL_EXT_geo', 'pds',zorder=10)

px = -1

for info, shape in zip(m.pds_info, m.pds):

    px = px+1

    pd = info['NAME']

    for para in paras:

        if(para!='bahn'):

            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]
            ni = len(id)
            id = id[:ni-1]

            dum = data[para][id]

            for gx in range(ng):
                for mx in range(nm):

                    jd = N.where((mm==mo[mx])&(gw==go[gx]))[0]

                    #gwls[gx,mx] = len(jd)

                    if(len(jd)>0):

                        gwls[gx,mx] = len(jd)/5.    

                        if(para=='tmax'): tmax[px,gx,mx] = N.mean(dum[jd])
                        if(para=='nied'): nied[px,gx,mx] = N.mean(dum[jd])
                        if(para=='wmax'): wmax[px,gx,mx] = N.mean(dum[jd])

            else:

                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]

                for gx in range(ng):
                        for mx in range(nm):

                            jd = N.where((mm==mo[mx])&(gw==go[gx]))[0]

                            #gwls[gx,mx] = len(jd)

                            if(len(jd>0)):

                    gwls[gx,mx] = len(jd)/5.

                                dum = data[filter][jd]#/20000.

                                #dum = []

                                #for ua in ['UA32','UA04','UA57','UA44','UA38','UA35','UA29']:

                                #    xxx = data[ua][id]

                                #    dum.append(N.sum(xxx[jd]))

                                #dum = N.array(dum)

                                #bahn[px,gx,mx] = N.sum(dum)/len(jd)
                bahn[px,gx,mx] = N.max(dum)

Plotting

fig = P.figure(figsize=(12,8))

gwl = N.nansum(gwls,1)
id = N.argsort(gwl)
id = id[::-1]

for para in paras: 

    if(para=='tmax'): ax = fig.add_subplot(3,2,1); data = N.mean(tmax[:,id,:],0)
    if(para=='nied'): ax = fig.add_subplot(3,2,2); data = N.mean(nied[:,id,:],0)
    if(para=='wmax'): ax = fig.add_subplot(3,2,3); data = N.mean(wmax[:,id,:],0)
    if(para=='bahn'): ax = fig.add_subplot(3,2,4); data = N.sum(bahn[:,id,:],0)/34.

    pc = ax.pcolor(data.T,edgecolor='k',linewidth=0.5,cmap=P.get_cmap(paras[para][2]),vmin=paras[para][0],vmax=paras[para][1],zorder=0)
    cb = P.colorbar(pc,orientation='vertical',extend='both',pad=-0.0)

    if(para=='bahn'): para = 'Meldungen'

    cb.set_label(para,fontsize=12,weight='bold')    

    gg = N.arange(ng)+0.5
    mm = N.arange(nm)+0.5
    mo = ['Jan','Feb','Mar','Apr','May','Jun','Jul','Aug','Sep','Okt','Nov','Dez']
    P.xticks(gg,go[id],rotation=90)
    P.yticks(mm,mo)
    P.tick_params(direction='out')
    #P.title(para,fontsize=14,weight='bold',ha='left')    

ax = fig.add_subplot(3,1,3)

data = gwls[id,:]

pc = ax.pcolor(data.T,edgecolor='k',linewidth=0.5,cmap=P.get_cmap('cool'),vmin=0,vmax=5,zorder=0)
cb = P.colorbar(pc,orientation='vertical',extend='both',pad=-0.0)

cb.set_label('Anzahl',fontsize=12,weight='bold')        

gg = N.arange(ng)+0.5
mm = N.arange(nm)+0.5
mo = ['Jan','Feb','Mar','Apr','May','Jun','Jul','Aug','Sep','Okt','Nov','Dez']
P.xticks(gg,go[id],rotation=90)
P.yticks(mm,mo)
P.tick_params(direction='out')

fig.tight_layout()
fig.savefig('./img/pcolor_'+filter+'.png',dpi=240,transparent=False,bbox_inches='tight',pad_inches=0.0)

Plotting

mi = N.arange(1,13,1);nm = len(mi)

for per in ['his','fut']:
    for mod in ['obs','ens']:

        if(mod=='ens'):

        file = '../dat/ens.dat'

        dd=N.genfromtxt(file,usecols=(0),skip_header=0,delimiter=';',comments='#',dtype="S")
            gg=N.genfromtxt(file,usecols=(3),skip_header=0,delimiter=';',comments='#',dtype="S")

        mm = []
        jj = []

        for id in dd:   

                jj.append(int(id[0:4])) 
                mm.append(int(id[5:7]))

            mm = N.array(mm)
            jj = N.array(jj)

            if(per=='his'): id = N.where((jj>=1981)&(jj<=2010))[0]
            if(per=='fut'): id = N.where((jj>=2061)&(jj<=2090))[0]

        if(mod=='obs'):

            file = '../dat/gwlneudatum.dat'

            mm=N.genfromtxt(file,usecols=(1),skip_header=0,dtype="I")
            jj=N.genfromtxt(file,usecols=(2),skip_header=0,dtype="I")
            gg=N.genfromtxt(file,usecols=(3),skip_header=0,dtype="S")

            if(per=='his'): id = N.where((jj>=1961)&(jj<=1990))[0]
            if(per=='fut'): id = N.where((jj>=1990)&(jj<=2019))[0]

        mm0 = mm[id]
        gg0 = gg[id]

        gwl = N.zeros((ng,nm),float)

        for gx in range(ng):
            for mx in range(nm):

                jd = N.where((mm0==mi[mx])&(gg0==go[gx]))[0]

                gwl[gx,mx] = len(jd)/30.

        if((mod=='obs')&(per=='his')): gwl_obs_his = gwl
        if((mod=='obs')&(per=='fut')): gwl_obs_fut = gwl
        if((mod=='ens')&(per=='his')): gwl_ens_his = gwl
        if((mod=='ens')&(per=='fut')): gwl_ens_fut = gwl

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

fig = P.figure(figsize=(7,8))

data = N.sum(bahn,0)/34.
data = N.ravel(data)

id = N.where(N.invert(N.isnan(data)))[0]
data = data[id]

mon,gwl = N.meshgrid(mo,go)

mon = N.ravel(mon)
gwl = N.ravel(gwl)

gwl_obs_his = N.ravel(gwl_obs_his)
gwl_obs_fut = N.ravel(gwl_obs_fut)
gwl_ens_his = N.ravel(gwl_ens_his)
gwl_ens_fut = N.ravel(gwl_ens_fut)

mon = mon[id]
gwl = gwl[id]

gwl_obs_his = gwl_obs_his[id]
gwl_obs_fut = gwl_obs_fut[id]
gwl_ens_his = gwl_ens_his[id]
gwl_ens_fut = gwl_ens_fut[id]

id = N.argsort(data)
id = id[::-1]
id = id[0:30][::-1]

nam = []
col = []

for i in id:

    nam.append('%s-%s'%(gwl[i],mon[i])) 

    if((gwl[i]=='WZ')|(gwl[i]=='WA')|(gwl[i]=='NWZ')|(gwl[i]=='NZ')): 

        col.append('limegreen') 

    elif((gwl[i]=='BM')|(gwl[i]=='HM')|(gwl[i]=='HNFZ')|(gwl[i]=='SWZ')|(gwl[i]=='HNZ')|(gwl[i]=='WW')|(gwl[i]=='SEZ')|(gwl[i]=='HFZ')):

        col.append('orangered')

    elif((gwl[i]=='TRM')|(gwl[i]=='TRW')):

        col.append('blueviolet')

    else:

        col.append('gray')


ii = N.arange(len(id))

P.barh(ii,data[id],0.8,color=col,alpha=0.6,edgecolor='k',linewidth=1)

j = -1

for i in id:

    j = j+1 

    delta = gwl_obs_fut[i]-gwl_obs_his[i]

    if(delta>0.5):

        P.text(data[i]+1,ii[j],r'$\nearrow$',ha='left',va='center',weight='bold',fontsize=12)

    elif(delta<-0.5):

        P.text(data[i]+1,ii[j],r'$\searrow$',ha='left',va='center',weight='bold',fontsize=12)

    else:

    P.text(data[i]+1,ii[j],r'$\rightarrow$',ha='left',va='center',weight='bold',fontsize=12)

    delta = gwl_ens_fut[i]-gwl_ens_his[i]

    if(delta>0.5):

        P.text(data[i]+3,ii[j],r'$\nearrow$',ha='left',va='center',weight='bold',fontsize=12)

    elif(delta<-0.5):

        P.text(data[i]+3,ii[j],r'$\searrow$',ha='left',va='center',weight='bold',fontsize=12)

    else:

        P.text(data[i]+3,ii[j],r'$\rightarrow$',ha='left',va='center',weight='bold',fontsize=12)

P.yticks(ii,nam)
P.xlim(0,40)
P.ylim(-1,30)
P.xlabel('max. Meldungen pro Tag',fontsize=16,weight='bold')
P.title('Kritikalitaet von Grosswetterlagen 2015-2019',weight='bold',fontsize=14)
P.legend(loc=4,shadow='true')

P.tick_params(direction='out')

fig.tight_layout()
fig.savefig('./img/bar_'+filter+'.png',dpi=240,transparent=False,bbox_inches='tight',pad_inches=0.0)