Contents

NYSM_TimeSeries.ipynb

NYSM_TimeSeries.ipynb

This notebook generates time series of recent meteorological data for various New York State Mesonet (NYSM) sites.

TRY THIS: Edit the next Markdown cell with your first and last name.

Author: firstName lastName

Imports

from datetime import datetime
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns

Create a Pandas DataFrame to read in the file containing the past hour’s worth of NYSM data.

# First define the format and then define the lambda function
timeFormat = "%Y-%m-%d %H:%M:%S UTC"
# This function will iterate over each string in a 1-d array
# and use Pandas' implementation of strptime to convert the string into a datetime object.
parseTime = lambda x: datetime.strptime(x, timeFormat)
df = pd.read_csv('/data1/nysm/latest.csv',parse_dates=['time'], date_parser=parseTime).set_index('time')

Inspect the Dataframe.

df
station temp_2m [degC] temp_9m [degC] relative_humidity [percent] precip_incremental [mm] precip_local [mm] precip_max_intensity [mm/min] avg_wind_speed_prop [m/s] max_wind_speed_prop [m/s] wind_speed_stddev_prop [m/s] ... snow_depth [cm] frozen_soil_05cm [bit] frozen_soil_25cm [bit] frozen_soil_50cm [bit] soil_temp_05cm [degC] soil_temp_25cm [degC] soil_temp_50cm [degC] soil_moisture_05cm [m^3/m^3] soil_moisture_25cm [m^3/m^3] soil_moisture_50cm [m^3/m^3]
time
2023-02-15 02:40:00 ADDI 5.2 5.9 43.4 0.0 0.0 0.0 5.3 6.7 0.7 ... -1.0 0.0 0.0 0.0 0.8 1.6 2.2 0.54 0.44 0.43
2023-02-15 02:45:00 ADDI 5.2 5.9 43.3 0.0 0.0 0.0 4.4 6.6 0.7 ... -1.0 0.0 0.0 0.0 0.8 1.6 2.2 0.54 0.44 0.43
2023-02-15 02:50:00 ADDI 5.1 5.8 43.8 0.0 0.0 0.0 4.6 5.7 0.5 ... -1.0 0.0 0.0 0.0 0.8 1.6 2.2 0.54 0.44 0.43
2023-02-15 02:55:00 ADDI 4.9 5.6 44.3 0.0 0.0 0.0 4.1 5.4 0.5 ... -1.0 0.0 0.0 0.0 0.8 1.6 2.2 0.54 0.44 0.43
2023-02-15 03:00:00 ADDI 4.9 5.5 45.0 0.0 0.0 0.0 4.1 5.6 0.6 ... -1.0 0.0 0.0 0.0 0.8 1.6 2.2 0.54 0.44 0.43
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
2023-02-15 03:20:00 YORK 0.9 2.4 80.1 0.0 0.0 0.0 0.1 0.5 0.1 ... 0.0 0.0 0.0 0.0 3.0 3.1 3.0 0.26 0.29 0.34
2023-02-15 03:25:00 YORK 1.2 2.1 77.5 0.0 0.0 0.0 0.0 0.4 0.1 ... 0.0 0.0 0.0 0.0 3.0 3.1 3.0 0.26 0.29 0.34
2023-02-15 03:30:00 YORK 0.7 1.9 82.4 0.0 0.0 0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0 3.0 3.1 3.0 0.26 0.29 0.34
2023-02-15 03:35:00 YORK 0.5 1.8 82.0 0.0 0.0 0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0 3.0 3.1 3.0 0.26 0.29 0.34
2023-02-15 03:40:00 YORK 0.7 2.1 81.4 0.0 0.0 0.0 0.3 0.9 0.3 ... 0.0 0.0 0.0 0.0 3.0 3.1 3.0 0.26 0.29 0.34

1638 rows × 29 columns

Specify a particular NYSM site ID, and then create a Dataframe specific to that site.

TRY THIS: Edit the next cell with your own choice of NYSM site.
# Change the station ID to any one of the 126 NYSM stations.
stnID = 'VOOR'
dfStn = df.query(' station == @stnID ')

Inspect this Dataframe, now consisting only of the station that was specified in the above.

dfStn
station temp_2m [degC] temp_9m [degC] relative_humidity [percent] precip_incremental [mm] precip_local [mm] precip_max_intensity [mm/min] avg_wind_speed_prop [m/s] max_wind_speed_prop [m/s] wind_speed_stddev_prop [m/s] ... snow_depth [cm] frozen_soil_05cm [bit] frozen_soil_25cm [bit] frozen_soil_50cm [bit] soil_temp_05cm [degC] soil_temp_25cm [degC] soil_temp_50cm [degC] soil_moisture_05cm [m^3/m^3] soil_moisture_25cm [m^3/m^3] soil_moisture_50cm [m^3/m^3]
time
2023-02-15 02:40:00 VOOR -2.7 -1.3 84.6 0.0 0.0 0.0 1.2 1.6 0.2 ... 0.0 0.0 0.0 0.0 0.6 0.9 1.3 0.25 0.29 0.19
2023-02-15 02:45:00 VOOR -2.4 -1.3 84.8 0.0 0.0 0.0 0.4 1.0 0.3 ... 0.0 0.0 0.0 0.0 0.6 0.9 1.3 0.25 0.29 0.19
2023-02-15 02:50:00 VOOR -2.2 -0.8 84.1 0.0 0.0 0.0 0.3 0.5 0.1 ... 0.0 0.0 0.0 0.0 0.6 0.9 1.3 0.25 0.29 0.19
2023-02-15 02:55:00 VOOR -2.6 -0.8 83.8 0.0 0.0 0.0 0.6 1.1 0.1 ... 0.0 0.0 0.0 0.0 0.6 0.9 1.3 0.25 0.29 0.19
2023-02-15 03:00:00 VOOR -2.3 -0.4 84.6 0.0 0.0 0.0 0.9 1.8 0.3 ... 0.0 0.0 0.0 0.0 0.6 0.9 1.3 0.25 0.29 0.19
2023-02-15 03:05:00 VOOR -2.5 0.0 83.7 0.0 0.0 0.0 0.9 1.3 0.2 ... 0.0 0.0 0.0 0.0 0.6 0.9 1.3 0.25 0.29 0.19
2023-02-15 03:10:00 VOOR -2.5 0.0 84.4 0.0 0.0 0.0 0.8 1.6 0.3 ... 0.0 0.0 0.0 0.0 0.6 0.9 1.3 0.25 0.29 0.19
2023-02-15 03:15:00 VOOR -2.5 -0.3 85.1 0.0 0.0 0.0 0.6 1.5 0.4 ... 0.0 0.0 0.0 0.0 0.6 0.9 1.3 0.25 0.29 0.19
2023-02-15 03:20:00 VOOR -2.2 -0.6 84.8 0.0 0.0 0.0 0.0 0.3 0.0 ... 0.0 0.0 0.0 0.0 0.6 0.9 1.3 0.25 0.29 0.19
2023-02-15 03:25:00 VOOR -2.4 -0.7 84.0 0.0 0.0 0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0 0.6 0.9 1.3 0.25 0.29 0.19
2023-02-15 03:30:00 VOOR -2.3 -0.6 84.1 0.0 0.0 0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0 0.5 0.9 1.3 0.25 0.29 0.19
2023-02-15 03:35:00 VOOR -2.2 -0.3 84.9 0.0 0.0 0.0 0.4 0.6 0.1 ... 0.0 0.0 0.0 0.0 0.5 0.9 1.3 0.25 0.29 0.19
2023-02-15 03:40:00 VOOR -2.2 -0.4 84.0 0.0 0.0 0.0 0.3 0.7 0.3 ... 0.0 0.0 0.0 0.0 0.5 0.9 1.3 0.25 0.29 0.20

13 rows × 29 columns

Output the names of all the variables (i.e., columns) in the Dataframe.

dfStn.columns

Index(['station', 'temp_2m [degC]', 'temp_9m [degC]',
       'relative_humidity [percent]', 'precip_incremental [mm]',
       'precip_local [mm]', 'precip_max_intensity [mm/min]',
       'avg_wind_speed_prop [m/s]', 'max_wind_speed_prop [m/s]',
       'wind_speed_stddev_prop [m/s]', 'wind_direction_prop [degrees]',
       'wind_direction_stddev_prop [degrees]', 'avg_wind_speed_sonic [m/s]',
       'max_wind_speed_sonic [m/s]', 'wind_speed_stddev_sonic [m/s]',
       'wind_direction_sonic [degrees]',
       'wind_direction_stddev_sonic [degrees]', 'solar_insolation [W/m^2]',
       'station_pressure [mbar]', 'snow_depth [cm]', 'frozen_soil_05cm [bit]',
       'frozen_soil_25cm [bit]', 'frozen_soil_50cm [bit]',
       'soil_temp_05cm [degC]', 'soil_temp_25cm [degC]',
       'soil_temp_50cm [degC]', 'soil_moisture_05cm [m^3/m^3]',
       'soil_moisture_25cm [m^3/m^3]', 'soil_moisture_50cm [m^3/m^3]'],
      dtype='object')

Create a nested set of for loops that plots time series traces for several variables at several sites.

TRY THIS: Edit the next two cells with your own choice of NYSM sites and variables.
# Set your own list of variarbles here
varList = ['temp_2m [degC]', 'temp_9m [degC]', 'relative_humidity [percent]']

# Set your own list of sites here
sites = ['SCHO','QUEE','CHAZ']

Invoke the seaborn library to make our time series plots more readable.

sns.set()
NOTE: To reset the plots to their default look, uncomment and run the following cell. Note how the seaborn-enabled plots look nicer.
#sns.reset_orig()

Create a nested for loop over the sites and variables, and make time series plots.

for site in sites:
    for var in varList:
        dfStn = df.query(' station == @site ')
        titleStr = "Past hour time series of " + var + " for site " + site
        dfStn[var].plot(title=titleStr)
        plt.show()
../../_images/NYSM_TimeSeries_25_0.png ../../_images/NYSM_TimeSeries_25_1.png ../../_images/NYSM_TimeSeries_25_2.png ../../_images/NYSM_TimeSeries_25_3.png ../../_images/NYSM_TimeSeries_25_4.png ../../_images/NYSM_TimeSeries_25_5.png ../../_images/NYSM_TimeSeries_25_6.png ../../_images/NYSM_TimeSeries_25_7.png ../../_images/NYSM_TimeSeries_25_8.png
Before you leave ... :
  1. Save the notebook (File-->Save Notebook)
  2. Shutdown the notebook (File-->Close and Shutdown Notebook)

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