Work with Weather in aWherePy

Learn how to get weather norms and observed weather with aWherePy.

Get Historical Norms and Observed Weather Data with aWherePy

Note

The example below will show you how to use the get_weather_norms() and get_weather_observed() functions to obtain weather data from the aWhere weather API.

In this vignette, you will use a single aWhere grid cell centroid near Rocky Mountain National Park, Colorado to get weather norms and observed weather for May 4 - May 13. Weather norms are data aggregated over many years and do not reference a specific year. The observed weather for this example is from 2014. In addition, you will plot a comparison of the historical weather norms to the observed data in 2014.

Import Packages

In order to use the functionality in this example, the following packages need to be imported.

import os
import matplotlib.pyplot as plt
import awherepy.grids as awg
import awherepy.weather as aww

Prerequisites

In order to make calls to any aWhere API, you must provide a valid API key and secret. The key and secret used in this example are stored as environment variables.

# Define aWhere API key and secret
awhere_api_key = os.environ.get("AWHERE_API_KEY")
awhere_api_secret = os.environ.get("AWHERE_API_SECRET")

Extract Centroids from an aWhere Grid and Isolate a Centroid for Analysis

In order to use an aWhere grid cell centroid, you must first create an aWhere grid from a shapefile, extract all centroids, and isolate a single centroid. This functionality is found in the awherepy.grid module.

# Define path to Rocky Mountain National Park, Colorado boundary
rmnp_bound_path = os.path.join(
    "..", "awherepy", "example-data", "colorado_rmnp_boundary.shp"
)

# Create aWhere grid and EPSG 4326 boudary for RMNP, CO
rmnp_grid, rmnp_bound_4326 = awg.create_grid(
    rmnp_bound_path, buffer_distance=0.12
)

# Plot RMNP grid and boundary - optional
fig, ax = awg.plot_grid(
    rmnp_grid,
    rmnp_bound_4326,
    plot_title="Rocky Mountain National Park, Colorado aWhere Grid",
    data_source="U.S. Department of the Interior",
)

# Extract RMNP grid centroids to list
rmnp_grid_centroids = awg.extract_centroids(rmnp_grid)

# Get first centroid
analysis_centroid = rmnp_grid_centroids[0]

Get aWhere Weather Norms Data

Get aWhere weather norms data by calling the get_weather_norms() function, with optional keyword arguments to use values outside the default.

# Define RMNP norms kwargs
rmnp_weather_norms_kwargs = {
    "location": (analysis_centroid[0], analysis_centroid[1]),
    "start_date": "05-04",
    "end_date": "05-13",
}

# Get RMNP weather norms, 05-04 to 05-13
rmnp_weather_norms = aww.get_weather_norms(
    awhere_api_key, awhere_api_secret, kwargs=rmnp_weather_norms_kwargs
)

# Get precipitation average from weather norms data
rmnp_precip_norms = rmnp_weather_norms[["precip_avg_mm"]]

Get aWhere Weather Observed Data

Get aWhere weather observed data by calling the get_weather_observed() function, with optional keyword arguments to use values outside the default.

# Define RMNP observed kwargs
rmnp_weather_observed_kwargs = {
    "location": (analysis_centroid[0], analysis_centroid[1]),
    "start_date": "2014-05-04",
    "end_date": "2014-05-13",
}

# Get observed weather
rmnp_weather_observed = aww.get_weather_observed(
    awhere_api_key, awhere_api_secret, kwargs=rmnp_weather_observed_kwargs
)

# Get precipitation amount from observed weather
rmnp_precip_observed = rmnp_weather_observed[["precip_amount_mm"]]

# Change date (YYYY-MM-DD) to day (MM-DD) for plotting
days = [value[5:] for value in rmnp_precip_observed.index.values]
rmnp_precip_observed.insert(1, "day", days, True)
rmnp_precip_observed.set_index("day", inplace=True)

Combine aWhere Weather Norms and Observed Data Subsets for Plotting

Merge the weather data subsets for precipitation into a single dataframe for use with plotting and visualiztion.

# Merge precipitation components (norms and observed)
rmnp_precip_may_2014 = rmnp_precip_observed.merge(rmnp_precip_norms, on="day")

# Add column for difference between observed and norms
rmnp_precip_may_2014["precip_diff_mm"] = (
    rmnp_precip_may_2014.precip_amount_mm - rmnp_precip_may_2014.precip_avg_mm
)

Plot a Comparison between the aWhere Norms and Observed Data for RMNP, CO

Plot the norms and observed data on the same subplot and also the difference between the data on a second subplot to see which days in the May 4 - May 13 temporal extent showed higher or lower precipatation compared to the historic norms.

# Plot daily precip comparison
with plt.style.context("dark_background"):

    # Create figure and axes
    fig, ax = plt.subplots(1, 2, figsize=(20, 10))

    # Set overall title
    plt.suptitle(
        (
            "Rocky Mountain National Park, CO\nLongitude: "
            f"{round(analysis_centroid[0], 4)}, Latitude: "
            f"{round(analysis_centroid[1], 4)}\nPrecipitation, May 2014"
        ),
        fontsize=24,
    )

    # Set spacing
    plt.subplots_adjust(top=0.8)

    # Subplot 1 - Observed and Historic Precipitation
    # Add grid
    ax[0].grid(zorder=1)

    # Add daily observed total (line)
    ax[0].plot(
        rmnp_precip_may_2014.precip_amount_mm,
        label="Observed",
        marker="o",
        color="#7fc97f",
        markersize=8,
        linewidth=2,
        linestyle="--",
        zorder=2,
    )

    # Add daily average total (line)
    ax[0].plot(
        rmnp_precip_may_2014.precip_avg_mm,
        label="Historic Average",
        marker="o",
        color="#beaed4",
        markersize=8,
        linewidth=2,
        linestyle="--",
        zorder=3,
    )

    # Configure axes
    ax[0].set_xlabel("Date", fontsize=16)
    ax[0].set_ylabel("Precipitation (mm)", fontsize=16)
    ax[0].set_title("Daily Total Precipitation", fontsize=20)
    ax[0].tick_params(axis="both", which="major", labelsize=16)
    plt.setp(ax[0].xaxis.get_majorticklabels(), rotation=45)

    # Add legend
    ax[0].legend(borderpad=0.75, edgecolor="w", fontsize=16, shadow=True)

    # Subplot 2 - Precipitation Difference
    # Add grid
    ax[1].grid(zorder=1)

    # Add difference (observed - norms)
    ax[1].bar(
        rmnp_precip_may_2014.index.values,
        rmnp_precip_may_2014.precip_diff_mm,
        label="Daily Total Difference",
        zorder=2,
        edgecolor="#ff7f00",
        color="#fdc086",
        linewidth=1,
    )

    # Configure axes
    ax[1].set_xlabel("Date", fontsize=16)
    ax[1].set_ylabel("Precipitation Difference (mm)", fontsize=16)
    ax[1].set_title(
        "Precipitation Difference (Observed - Average)", fontsize=20
    )
    ax[1].tick_params(axis="both", which="major", labelsize=16)
    plt.setp(ax[1].xaxis.get_majorticklabels(), rotation=45)

    # Add legend
    ax[1].legend(borderpad=0.75, edgecolor="w", fontsize=16, shadow=True)

    plt.show()