3.03.1 Display Meteorological Data¶
Same period but more parameters to compare
Station: Kloten/Zürich Flughafen
Legende:
Einheit Beschreibung
tso020s0 °C Bodentemperatur 20 cm Tiefe; Momentanwert
fkl010z1 m/s Böenspitze (Sekundenböe); Maximum
xchills0 °C Chill Temperatur; Momentanwert
brefarz0 No Fernblitze (Entfernung 3 - 30 km); Zehnminutensumme
gre000z0 W/m² Globalstrahlung; Zehnminutenmittel
prestas0 hPa Luftdruck auf Stationshöhe (QFE); Momentanwert
tre200s0 °C Lufttemperatur 2 m über Boden; Momentanwert
brecloz0 No Nahblitze (Entfernung weniger als 3 km); Zehnminutensumme
rre150z0 mm Niederschlag; Zehnminutensumme
rii000s0 mm/h Niederschlagsrate (Intensität); Momentanwert
ure200s0 % Relative Luftfeuchtigkeit 2 m über Boden; Momentanwert
vhoauts0 m Sichtweite; automatische Messung
fkl010z0 m/s Windgeschwindigkeit skalar; Zehnminutenmittel
dkl010za ° Windrichtung; Standardabweichung
dkl010z0 ° Windrichtung; Zehnminutenmittel
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import matplotlib.ticker as plticker
from datetime import date
def scale(a): return (a-a.min())/(a.max()-a.min())
Read Meteorological Data¶
def read_meteo_data(fName):
colNames = ['Stao','time', 'T_Boden_20cm', 'V_Windböe', 'T_Chill', 'Flash_30km', 'Glob_rad', 'QFE','T_2m','Flash_3km','Rain_Sum','Rain_intens','H_rel','visibi','V_wind','stabw_V_wind','direction_wind']
df = pd.read_csv(fName,sep=';', skiprows=3, names=colNames, na_values='-')
print(df.head())
return df
fPath = '/mnt/daten/04_Schule/42_Kanti/Matrua/Music_generation/Organisation/MeteoSchweiz/Daten/'
fName = 'order_74947_data.txt'
dM = read_meteo_data(fPath+fName)
#---- Parameter bestimmen -----------
NT, MP = dM.shape
print('-----------------')
print('NT, MP', NT, MP)
Stao time T_Boden_20cm V_Windböe T_Chill Flash_30km Glob_rad 0 KLO 201908270000 20.4 1.2 14.2 0 2
1 KLO 201908270010 20.4 0.9 14.4 0 2
2 KLO 201908270020 20.4 1.1 14.4 0 1
3 KLO 201908270030 20.4 0.8 13.8 0 2
4 KLO 201908270040 20.4 0.5 14.2 0 2
QFE T_2m Flash_3km Rain_Sum Rain_intens H_rel visibi V_wind 0 968.2 14.2 0 0.0 0.0 99.6 6626.0 0.7
1 968.2 14.4 0 0.0 0.0 98.5 1277.0 0.5
2 968.2 14.4 0 0.0 0.0 98.6 4900.0 0.5
3 968.3 13.8 0 0.0 0.0 99.0 7417.0 0.6
4 968.2 14.2 0 0.0 0.0 99.7 981.0 0.4
stabw_V_wind direction_wind
0 9 110
1 40 198
2 28 333
3 4 326
4 30 256
-----------------
NT, MP 2160 17
Parse begin and end date¶
def parse_date(A):
yr = int(str(A)[0:4])
mo = int(str(A)[4:6])
dy = int(str(A)[6:8])
hr = int(str(A)[8:10])
mi = int(str(A)[10:12])
return date(yr,mo,dy)
firstDateM = dM['time'].iloc[0]
lastDateM = dM['time'].iloc[-1]
firstDate = parse_date(firstDateM); print('firstDate', firstDate)
lastDate = parse_date(lastDateM); print('lastDate', lastDate)
firstDate 2019-08-27
lastDate 2019-09-10
Plot data¶
# ---- Parameter festlegen ----------
w3 = 18
w6 = 6
h24 = 6*24
tt = np.arange(NT)/h24 # Zeitachse in Tagen
#---- graphics ---------------------
with plt.style.context('fivethirtyeight'):
for k in range(2,MP,1):
fig = plt.figure(figsize=(22,3))
ax = fig.add_subplot(111)
Y = np.array(dM[dM.columns[k]])
Y3 = np.array(dM[dM.columns[k]].rolling(window=w3,center=True).mean())
Y6 = np.array(dM[dM.columns[k]].rolling(window=w6,center=True).mean())
plt.plot(tt,Y,linewidth=1.0, label=dM.columns[k])
plt.fill_between(tt,Y,Y.min(),alpha=0.2)
plt.plot(tt,Y3,linewidth=1.0, label=dM.columns[k]+', moving average 3h')
plt.plot(tt,Y6,linewidth=1.0, label=dM.columns[k]+', moving average 1h')
plt.hlines(Y.min(),5.5, 6.5, colors='lime', linewidth=8, linestyles='solid', label='change')
loc = plticker.MultipleLocator(base=1.0) # this locator puts ticks at regular intervals
ax.xaxis.set_major_locator(loc)
plt.title('Period: '+str(firstDate)+' to '+str(lastDate))
plt.xlabel('days')
plt.legend(prop={'size':15})
plt.show()
for k in range(2,MP,1):
print(k, dM.columns[k])
2 T_Boden_20cm
3 V_Windböe
4 T_Chill
5 Flash_30km
6 Glob_rad
7 QFE
8 T_2m
9 Flash_3km
10 Rain_Sum
11 Rain_intens
12 H_rel
13 visibi
14 V_wind
15 stabw_V_wind
16 direction_wind