Denver Colorado Maximum Annual Temperature from 1948-2024.¶

Data Source¶

This data[1] is drawn from The Global Historical Climatology Network - Daily (GHCN-Daily/GHCNd) dataset2 which integrates daily climate observations from approximately 30 different land surface sources from around the world.

According to the National Centers for Environmental Information2, "the dataset cannot be used to quantify all aspects of climate variability and change without any additional processing. In general, the stations providing daily observations were not managed to meet the desired standards for climate monitoring. Rather, the stations were deployed to meet the demands of agriculture, hydrology, weather forecasting, aviation, etc."

For this analysis I selected data station # USW00023062 located in Denver Central Park, Colorado. This station is the only one in the area with datasets going back over half a century, in this case to 1948. Unfortunately, the TOBS data had large gaps so I instead looked at the TMAX data for evidence of climate change.

[1]: Menne, Matthew J., Imke Durre, Bryant Korzeniewski, Shelley McNeill, Kristy Thomas, Xungang Yin, Steven Anthony, Ron Ray, Russell S. Vose, Byron E. Gleason, and Tamara G. Houston (2012): Global Historical Climatology Network - Daily (GHCN-Daily), Version 3, Denver Central Park, CO, Station USW00023062. NOAA National Climatic Data Center. doi:10.7289/V5D21VHZ [accessed Sept 23, 2024].

In [1]:
# Import required packages
import pandas as pd
In [2]:
denver_url = (
    'https://www.ncei.noaa.gov/access/services/data/v1?'
    'dataset=daily-summaries'
    '&dataTypes=TMAX,PRCP&stations=USW00023062'
    '&startDate=1948-01-01'
    '&endDate=2024-09-10'
    '&units=standard')
denver_url
Out[2]:
'https://www.ncei.noaa.gov/access/services/data/v1?dataset=daily-summaries&dataTypes=TMAX,PRCP&stations=USW00023062&startDate=1948-01-01&endDate=2024-09-10&units=standard'
In [3]:
# Import data into Python from NCEI API
pd.read_csv(
    denver_url,
    index_col='DATE',
    parse_dates=True)
Out[3]:
STATION PRCP TMAX
DATE
1948-01-01 USW00023062 0.0 43.0
1948-01-02 USW00023062 0.0 47.0
1948-01-03 USW00023062 0.0 56.0
1948-01-04 USW00023062 0.0 57.0
1948-01-05 USW00023062 0.0 53.0
... ... ... ...
2024-08-31 USW00023062 0.0 87.0
2024-09-02 USW00023062 0.0 87.0
2024-09-03 USW00023062 0.0 91.0
2024-09-09 USW00023062 0.0 88.0
2024-09-10 USW00023062 0.0 90.0

27976 rows × 3 columns

In [4]:
# Download the climate data
denver_df = pd.read_csv(
    denver_url,
    index_col='DATE',
    parse_dates=True,
    na_values=['NaN'])
denver_df
Out[4]:
STATION PRCP TMAX
DATE
1948-01-01 USW00023062 0.0 43.0
1948-01-02 USW00023062 0.0 47.0
1948-01-03 USW00023062 0.0 56.0
1948-01-04 USW00023062 0.0 57.0
1948-01-05 USW00023062 0.0 53.0
... ... ... ...
2024-08-31 USW00023062 0.0 87.0
2024-09-02 USW00023062 0.0 87.0
2024-09-03 USW00023062 0.0 91.0
2024-09-09 USW00023062 0.0 88.0
2024-09-10 USW00023062 0.0 90.0

27976 rows × 3 columns

Data Cleanup¶

Below, I am specifying the specific data I want to examine, in this case the maximum temperatures.

In [5]:
denver_df = denver_df[['TMAX']]
denver_df
Out[5]:
TMAX
DATE
1948-01-01 43.0
1948-01-02 47.0
1948-01-03 56.0
1948-01-04 57.0
1948-01-05 53.0
... ...
2024-08-31 87.0
2024-09-02 87.0
2024-09-03 91.0
2024-09-09 88.0
2024-09-10 90.0

27976 rows × 1 columns

Add Correct Units to the Data¶

Data was downloaded in the 'standard' format where temperatures are recorded in degrees Fahrenheit. I added this lable.

In [6]:
denver_df_u = denver_df.rename(columns={
    'TMAX': 'TMAX_F',
})
denver_df_u
Out[6]:
TMAX_F
DATE
1948-01-01 43.0
1948-01-02 47.0
1948-01-03 56.0
1948-01-04 57.0
1948-01-05 53.0
... ...
2024-08-31 87.0
2024-09-02 87.0
2024-09-03 91.0
2024-09-09 88.0
2024-09-10 90.0

27976 rows × 1 columns

Plot the Results¶

In [7]:
import holoviews as hv
import hvplot.pandas
In [8]:
denver_df.plot()
Out[8]:
<Axes: xlabel='DATE'>
No description has been provided for this image
In [9]:
# Plot the data using .plot
denver_df.plot(
    y='TMAX',
    title='Maximum Temp in Denver, CO between 1948 and 2024',
    xlabel='Year',
    ylabel='Temperature (F)',
    legend=False,
    fontsize=10,
    color='purple',
    )
Out[9]:
<Axes: title={'center': 'Maximum Temp in Denver, CO between 1948 and 2024'}, xlabel='Year', ylabel='Temperature (F)'>
No description has been provided for this image

Resample Data¶

I resampled the data from the start of each year, selecting only the maximum temperature. This will make any visuals easier to read.

In [10]:
# Clean up data by changing it to Annual Average 
ann_denver_df = denver_df.resample('YS').max()
ann_denver_df
Out[10]:
TMAX
DATE
1948-01-01 95.0
1949-01-01 95.0
1950-01-01 93.0
1951-01-01 97.0
1952-01-01 97.0
... ...
2020-01-01 99.0
2021-01-01 102.0
2022-01-01 101.0
2023-01-01 99.0
2024-01-01 99.0

77 rows × 1 columns

Plot the Data¶

In [11]:
# Plot the annual data

ann_denver_df.plot(
    y='TMAX',
    title='Maximum Temperature in Denver, CO between 1948 and 2024',
    xlabel='Year',
    ylabel='Temperature (F)',
    legend=False,
    fontsize=12,
    color='purple',
)
Out[11]:
<Axes: title={'center': 'Maximum Temperature in Denver, CO between 1948 and 2024'}, xlabel='Year', ylabel='Temperature (F)'>
No description has been provided for this image

Make the Plot Interactive¶

In [12]:
# Plot the annual data interactively
ann_temp_plot = ann_denver_df.hvplot(
    y='TMAX',
    title='Yearly High Temperature in Denver, CO (1948 and 2024)',
    xlabel='Year',
    ylabel='Temperature (F)',
    legend=False,
    fontsize=12,
    color='purple',
)
ann_temp_plot

/opt/conda/lib/python3.10/site-packages/holoviews/core/data/pandas.py:39: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`
  return dataset.data.dtypes[idx].type
/opt/conda/lib/python3.10/site-packages/holoviews/core/data/pandas.py:39: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`
  return dataset.data.dtypes[idx].type
Out[12]:

Establish a Trend Line for High Temps in Denver (1948-2024)¶

In [13]:
# Advanced options on matplotlib/seaborn/pandas plots
import matplotlib.pyplot as plt
# Common statistical plots for tabular data
import seaborn as sns
# Fit an OLS linear regression
from sklearn.linear_model import LinearRegression

The sample data indicated that there was a 100% complete dataset for TMAX. However, I added a filter to exclude years that had less than 99% complete data. This will exclude the partial year of 2024.

In [14]:
include = denver_df.TMAX.resample('YS').count() / 365 > .99
In [15]:
from sklearn.linear_model import LinearRegression
import pandas as pd
ann_denver_df = (
    ann_denver_df
    [include]
    .dropna()
)
ann_denver_df.plot(y='TMAX')
Out[15]:
<Axes: xlabel='DATE'>
No description has been provided for this image
In [16]:
# Reset index to make 'Year' a column if it's currently the index
ann_denver_df = ann_denver_df.reset_index()

# Define the independent variable (X) and the dependent variable (y)
x = ann_denver_df['DATE'].dt.year.values.reshape(-1, 1) # To select just the year and not seconds since 1970
y = ann_denver_df['TMAX']

# Create and fit the model
model = LinearRegression()
model.fit(x, y)

# Get the slope (coefficient) and intercept
slope = model.coef_[0]
intercept = model.intercept_

# Print results
print(f"Slope: {slope}")
print(f"Intercept: {intercept}")

Slope: 0.05931935945312204
Intercept: -18.6899508756983
In [22]:
# Plot annual average temperature data with a trend line
ax = sns.regplot(
    x=ann_denver_df.DATE.dt.year, 
    y=ann_denver_df.TMAX,
    ci=None, # Removed the confidence interval (because this is for a extreme temp)
    color='purple', # Color of the regression line and scatter points
    line_kws={'color': 'green'},  # Change the color of the x_estimator line (mean in this case)
    scatter_kws={'color': 'purple'},  # Scatter plot points color
    )
# Set plot labels
ax.set(
    title='Yearly High Temp in Denver, CO (1948 and 2024)',
    xlabel='Year',
    ylabel='Temperature ($^\circ$F)'
)
# Display the plot without extra text
plt.show()
No description has been provided for this image

This models shows a slight but clear trend upwards in terms of the highest annual temperature in Denver Colorado during the past 70 years. While not strictly linear, the trend indicates a tendency toward higher max temperatures. The next step would be to repeat this analysis with TMIN to see if the minimum temperatures are also rising or if weather is becoming more extreme in terms of both hot and cold.