34  Meteogram

Author

Andres Patrignani

Published

January 14, 2024

Keywords

weather, meteogram, time series

A weather meteogram is a time-series graphical representation that displays detailed weather information for a specific location over a continuous period. This compact chart captures various meteorological variables such as air and soil temperature, wind speed and direction, precipitation, cloud cover, and atmospheric pressure. Each variable is typically plotted against time on the horizontal axis, allowing for a clear visual analysis of weather trends and patterns.

# Import necessary modules
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
# Read data and display the first 5 rows
filename = '../datasets/kings_creek_2022_2023_daily.csv'
df = pd.read_csv(filename, parse_dates=['datetime'])
df.head()
datetime pressure tmin tmax tavg rmin rmax prcp srad wspd wdir vpd vwc_5cm vwc_20cm vwc_40cm soiltemp_5cm soiltemp_20cm soiltemp_40cm battv discharge
0 2022-01-01 96.838 -14.8 -4.4 -9.60 78.475 98.012 0.25 2.098 5.483 0.969 0.028 0.257 0.307 0.359 2.996 5.392 7.425 8714.833 0.0
1 2022-01-02 97.995 -20.4 -7.2 -13.80 50.543 84.936 0.25 9.756 2.216 2.023 0.072 0.256 0.307 0.358 2.562 4.250 6.692 8890.042 0.0
2 2022-01-03 97.844 -9.4 8.8 -0.30 40.622 82.662 0.50 9.681 2.749 5.667 0.262 0.255 0.307 0.358 2.454 3.917 6.208 8924.833 0.0
3 2022-01-04 96.419 0.1 8.6 4.35 48.326 69.402 0.25 8.379 5.806 2.627 0.363 0.289 0.319 0.357 2.496 3.754 5.842 8838.292 0.0
4 2022-01-05 97.462 -11.1 -2.2 -6.65 50.341 76.828 0.00 5.717 4.207 1.251 0.126 0.313 0.337 0.357 1.688 3.429 5.567 8848.083 0.0 
# Select data for January
start_date = pd.to_datetime('2022-01-01')
end_date = pd.to_datetime('2023-01-01')
idx = (df['datetime'] >= start_date) & (df['datetime'] < end_date)
df = df[idx].copy().reset_index(drop=True)
df.head()
datetime pressure tmin tmax tavg rmin rmax prcp srad wspd wdir vpd vwc_5cm vwc_20cm vwc_40cm soiltemp_5cm soiltemp_20cm soiltemp_40cm battv discharge
0 2022-01-01 96.838 -14.8 -4.4 -9.60 78.475 98.012 0.25 2.098 5.483 0.969 0.028 0.257 0.307 0.359 2.996 5.392 7.425 8714.833 0.0
1 2022-01-02 97.995 -20.4 -7.2 -13.80 50.543 84.936 0.25 9.756 2.216 2.023 0.072 0.256 0.307 0.358 2.562 4.250 6.692 8890.042 0.0
2 2022-01-03 97.844 -9.4 8.8 -0.30 40.622 82.662 0.50 9.681 2.749 5.667 0.262 0.255 0.307 0.358 2.454 3.917 6.208 8924.833 0.0
3 2022-01-04 96.419 0.1 8.6 4.35 48.326 69.402 0.25 8.379 5.806 2.627 0.363 0.289 0.319 0.357 2.496 3.754 5.842 8838.292 0.0
4 2022-01-05 97.462 -11.1 -2.2 -6.65 50.341 76.828 0.00 5.717 4.207 1.251 0.126 0.313 0.337 0.357 1.688 3.429 5.567 8848.083 0.0 
# Display number of missing values for each column. 
df.isna().sum()
datetime          0
pressure          0
tmin              0
tmax              0
tavg              0
rmin              0
rmax              0
prcp              0
srad              0
wspd              1
wdir             28
vpd               2
vwc_5cm           0
vwc_20cm          0
vwc_40cm          0
soiltemp_5cm      0
soiltemp_20cm     0
soiltemp_40cm     0
battv             0
discharge         0
dtype: int64
# Replace missing values
df['wspd'] = df['wspd'].interpolate(method='linear')
df['vpd'] = df['vpd'].interpolate(method='linear')
# Display the new number of missing values for each column. 
df.isna().sum()
datetime          0
pressure          0
tmin              0
tmax              0
tavg              0
rmin              0
rmax              0
prcp              0
srad              0
wspd              0
wdir             28
vpd               0
vwc_5cm           0
vwc_20cm          0
vwc_40cm          0
soiltemp_5cm      0
soiltemp_20cm     0
soiltemp_40cm     0
battv             0
discharge         0
dtype: int64

Estimate some useful metrics

To characterize what happened during the entire year, let’s compute the annual rainfall, maximum wind speed, and maximum and minimum air temperature.

# Find and print total precipitation
P_total = df['prcp'].sum().round(2)
print(f'Total precipitation in 2022 was {P_total} mm')
Total precipitation in 2022 was 841.08 mm
# Find the total number of days with measurable precipitation
P_hours = (df['prcp'] > 0).sum()
print(f'There were {P_hours} days with precipitation')
There were 94 days with precipitation
# Find median air temperature. Print value.
Tmedian = df['tavg'].median()
print(f'Median air temperature was {Tmedian} Celsius')
Median air temperature was 13.65 Celsius
# Find value and time of minimum air temperature. Print value and timestamp.
fmt = '%A, %B %d, %Y'
Tmin_idx = df['tmin'].argmin()
Tmin_value = df.loc[Tmin_idx, 'tmin']
Tmin_timestamp = df.loc[Tmin_idx, 'datetime']
print(f'The lowest air temperature was {Tmin_value} on {Tmin_timestamp:{fmt}}')
The lowest air temperature was -22.4 on Thursday, December 22, 2022
# Find value and time of maximum air temperature. Print value and timestamp.
Tmax_idx = df['tmax'].argmax()
Tmax_value = df.loc[Tmax_idx, 'tmax']
Tmax_timestamp = df.loc[Tmax_idx, 'datetime']
print(f'The highest air temperature was {Tmax_value} on {Tmax_timestamp:{fmt}}')
The highest air temperature was 37.4 on Saturday, July 23, 2022
# Find max wind gust and time of occurrence. Print value and timestamp.
Wmax_idx = df['wspd'].argmax()
Wmax_value = df.loc[Wmax_idx, 'wspd']
Wmax_timestamp = df.loc[Wmax_idx, 'datetime']
print(f'The highest wind speed was {Wmax_value:.2f} m/s on {Wmax_timestamp:{fmt}}')
The highest wind speed was 8.15 m/s on Tuesday, April 12, 2022

Meteogram

# Create meteogram plot

# Define style
plt.style.use('ggplot')

# Define fontsize
font = 14

# Create plot
plt.figure(figsize=(14,30))

# Air temperature
plt.subplot(9,1,1)
plt.title('Kings Creek Meteogram for 2022', size=20)
plt.plot(df['datetime'], df['tmin'], color='navy')
plt.plot(df['datetime'], df['tmax'], color='tomato')
plt.ylabel('Air Temperature (°C)', size=font)
plt.yticks(size=font)

# Relative humidity
plt.subplot(9,1,2)
plt.plot(df['datetime'], df['rmin'], color='navy')
plt.plot(df['datetime'], df['rmax'], color='tomato')
plt.ylabel('Relative Humidity (%)', size=font)
plt.yticks(size=font)
plt.ylim(0,110)

# Atmospheric pressure
plt.subplot(9,1,3)
plt.plot(df['datetime'], df['pressure'], '-k')
plt.ylabel('Pressure (kPa)', size=font)
plt.yticks(size=font)

# Vapor pressure deficit
plt.subplot(9,1,4)
plt.plot(df['datetime'], df['vpd'], '-k')
plt.ylabel('Vapor pressure deficit (kPa)', size=font)
plt.yticks(size=font)

# Wind speed
plt.subplot(9,1,5)
plt.plot(df['datetime'], df['wspd'], '-k', label='Wind speed')
plt.ylabel(r'Wind Speed ($m \ s^{-1}$)', size=font)
plt.legend(loc='upper left')
plt.yticks(size=font)

# Solar radiation
plt.subplot(9,1,6)
plt.plot(df['datetime'], df['srad'])
plt.ylabel(r'Solar radiation ($W \ m^{-2}$)', size=font)
plt.yticks(size=font)

# Precipitation
plt.subplot(9,1,7)
plt.step(df['datetime'], df['prcp'], color='navy')
plt.ylabel('Precipitation (mm)', size=font)
plt.yticks(size=font)
plt.ylim(0.01,35)
plt.text(df['datetime'].iloc[5], 30, f"Total = {P_total} mm", size=14)

# Soil temperature
plt.subplot(9,1,8)
plt.plot(df['datetime'], df['soiltemp_5cm'], '-k', label='5 cm')
plt.plot(df['datetime'], df['soiltemp_20cm'], '-g', label='20 cm')
plt.plot(df['datetime'], df['soiltemp_40cm'], '--r', label='40 cm')
plt.ylabel('Soil temperature (°C)', size=font)
plt.yticks(size=font)
plt.ylim(df['soiltemp_5cm'].min()-10, df['soiltemp_5cm'].max()+10)
plt.grid(which='minor')
plt.legend(loc='upper left')

# Soil moisture
plt.subplot(9,1,9)
plt.plot(df['datetime'], df['vwc_5cm'], '-k', label='5 cm')
plt.plot(df['datetime'], df['vwc_20cm'], '-g', label='20 cm')
plt.plot(df['datetime'], df['vwc_40cm'], '--r', label='40 cm')
plt.ylabel(r'Soil Moisture ($m^3 \ m^{-3}$)', size=font)
plt.yticks(size=font)
plt.ylim(0, 0.5)
plt.grid(which='minor')
plt.legend(loc='best')

plt.subplots_adjust(hspace=0.2) # for space between columns wspace=0)
#plt.savefig('meteogram.svg', format='svg')
plt.show()