On the development of hydrological evaluations, there is a need to assess which weather stations will be used for the numerical modeling stage. The main acceptance criteria would be the amount of climate variables and records available however huge areas could have docens or hundreds of stations delivered in one text file. This tutorial shows the procedure to plot climate variable records from a single text file on a polar plot with aperture angle related to value statistics. The tutorial is done with standar Python packages as Matplotlib, Numpy and Pandas.

Interactive visualization

Tutorial

Code

This is the Python code:

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt

#open the csv
climateData = pd.read_csv('../Data/2805958.csv',parse_dates=True,index_col=1)
#description of variables
climateData.describe()

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  exec(code_obj, self.user_global_ns, self.user_ns)

	LATITUDE	LONGITUDE	ELEVATION	AWND	PRCP	TMAX	TMIN	TSUN
count	53094.000000	53094.000000	53094.000000	7198.000000	52218.000000	24791.000000	24757.000000	889.0
mean	26.157937	-81.729122	9.599311	3.318394	4.121282	29.018071	19.001474	0.0
std	0.104336	0.041891	34.862054	1.188413	11.728384	4.190826	5.239559	0.0
min	25.949100	-81.816683	1.200000	0.600000	0.000000	5.000000	-2.200000	0.0
25%	26.128961	-81.763369	2.700000	2.500000	0.000000	26.700000	15.600000	0.0
50%	26.165300	-81.722053	4.300000	3.100000	0.000000	30.000000	20.600000	0.0
75%	26.235972	-81.689149	12.200000	4.000000	1.500000	32.200000	23.300000	0.0
max	26.375011	-81.624320	388.900000	15.200000	245.900000	36.700000	34.400000	0.0

Main climate variables

AWND = Average daily wind speed (meters per second or miles per hour as per user preference)

PRCP = Precipitation (mm or inches as per user preference, inches to hundredths on Daily Form pdf file)

TMAX = Maximum temperature (Fahrenheit or Celsius as per user preference, Fahrenheit to tenths on
Daily Form pdf file

TMIN = Minimum temperature (Fahrenheit or Celsius as per user preference, Fahrenheit to tenths on
Daily Form pdf file

TSUN = Daily total sunshine (minutes).

#show stations
weatherStations = pd.unique(climateData.index)
print(weatherStations)

['NAPLES 5.7 SE, FL US' 'NAPLES 1.3 NW, FL US' 'NAPLES 1.3 SE, FL US'
 'NAPLES 8.7 SE, FL US' 'NORTH NAPLES 5.1 NE, FL US'
 'NAPLES 7.4 NNE, FL US' 'NAPLES 11.8 ENE, FL US'
 'BONITA SPRINGS 3.1 NW, FL US' 'NAPLES PARK 3.7 ENE, FL US'
 'COLLIER 14.1 NE, FL US' 'NAPLES, FL US'
 'NAPLES MUNICIPAL AIRPORT, FL US' 'NAPLES 9.0 NE, FL US'
 'MARCO ISLAND 6.6 NNE, FL US' 'GOLDEN GATE 2.2 SW, FL US'
 'NAPLES 3.6 N, FL US' 'NORTH NAPLES 7.3 E, FL US'
 'NAPLES 12.5 N NUMBER 9 NORTH, FL US'
 'NAPLES 11.7 N NUMBER 17 NORTH, FL US' 'EAST NAPLES 0.9 NE, FL US'
 'NAPLES 11.0 N CLUBHOUSE, FL US' 'NAPLES 11.9 SSE, FL US'
 'NAPLES MANOR 2.3 E, FL US' 'NORTH NAPLES 4.8 NNE, FL US'
 'NAPLES 6.7 NE, FL US' 'NAPLES 1.4 NNW, FL US'
 'NAPLES 12.0 N NUMBER 2 SOUTH, FL US' 'PALM RIVER 3.9 E, FL US'
 'NAPLES 11.5 N NUMBER 17 SOUTH, FL US' 'NAPLES 0.7 SSW, FL US'
 'MARCO ISLAND, FL US']

#list of parameters to plot
paramValues = ['AWND','PRCP','TMAX','TMIN','TSUN']
N = len(paramValues)

#analysis of record distribution for one station
stationName = 'MARCO ISLAND, FL US'
stationDf = climateData.loc[stationName]

#get number of windspeed, precipitation, tmax, tmin, tsun
paramArray = stationDf[paramValues].count().values
#get the standard deviation
stdArray = stationDf[paramValues].std().values
#replace nan by 0
stdArray = np.nan_to_num(stdArray)
print(paramArray)
print(stdArray)

[   0 7118 7106 7106    0]
[ 0.         12.15939423  4.38560156  5.36778437  0.        ]

#example of polar plot
theta = np.linspace(0.0, 2 * np.pi, N, endpoint=False)
radii = paramArray

ax = plt.subplot(projection='polar')
ax.bar(theta, radii)
plt.show()

# Improved plot for record amounts
theta = np.linspace(0.0, 2 * np.pi, N, endpoint=False)
radii = paramArray
width = stdArray * np.pi / 180
colors = plt.cm.viridis(radii / 10.)

ax = plt.subplot(projection='polar')

for index, value in enumerate(paramValues):
    ax.bar(theta[index], radii[index], width=width[index], label=value)
ax.legend(loc='lower left', bbox_to_anchor=(1.0, 0))
ax.set_title(stationName)
ax.set_ylim(0,climateData.count()[0])
plt.gcf().set_size_inches(15, 8)
plt.show()

#create plot function
def plotRecords(stationName,yMax):
    stationDf = climateData.loc[stationName]

    #get number of windspeed, precipitation, tmax, tmin, tsun
    paramArray = stationDf[paramValues].count().values
    #get the standard deviation
    stdArray = stationDf[paramValues].std().values
    #replace nan by 0
    stdArray = np.nan_to_num(stdArray)

    theta = np.linspace(0.0, 2 * np.pi, N, endpoint=False)
    radii = paramArray
    width = stdArray * np.pi / 180
    colors = plt.cm.viridis(radii / 10.)

    ax = plt.subplot(projection='polar')

    for index, value in enumerate(paramValues):
        ax.bar(theta[index], radii[index], width=width[index], label=value)
    ax.legend(loc='lower left', bbox_to_anchor=(1.0, 0))
    ax.set_title(stationName)
    ax.set_ylim(0,yMax)
    plt.gcf().set_size_inches(15, 8)
    plt.show()

plotRecords('MARCO ISLAND, FL US',10000)

import ipywidgets as widgets
from ipywidgets import interact

def interactivePlot(index, yMax):
    plotRecords(weatherStations[index], yMax)

interact(interactivePlot, index=(0,weatherStations.shape[0]-1), yMax=(0,5000,200))

interactive(children=(IntSlider(value=15, description='index', max=30), IntSlider(value=2400, description='yMa…





<function __main__.interactivePlot(index, yMax)>

Input data

You can download the input data from this link.

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