METAR Temperature Visualization with Lonboard

Overview¶

Within this notebook, we will create an interactive visualization of the latest METAR data across all stations. We will use the following libraries for our visualizations:

Prerequisites¶

Concepts	Importance	Notes
Pandas	Required	Tabular Datasets

Time to learn: 10 minutes

from IPython.display import Image
import duckdb
import numpy as np
import pandas as pd
import geopandas as gpd
from datetime import datetime, timedelta, timezone
from lonboard import viz, Map, ScatterplotLayer, HeatmapLayer
from lonboard.colormap import apply_continuous_cmap
from palettable.colorbrewer.diverging import BrBG_10, RdBu_10
from pathlib import Path
import geopandas as gpd
import shapely

url = 'https://data.source.coop/dynamical/asos-parquet/year=2026/data.parquet'

Get the past three days of data¶

This query will request just the latest hours worth of data across all stations.

time_1 = datetime.now()
print(f"end time: {time_1}")

time_0 = datetime.now() - timedelta(hours=72)
print(f"start time: {time_0}")

end time: 2026-09-11 04:15:29.857316
start time: 2026-09-08 04:15:29.857482

Query the database for all data between those timestamps¶

df = duckdb.execute("""
    SELECT *
    FROM read_parquet($1, hive_partitioning=true)
    WHERE 
---      country = 'US' AND
    valid BETWEEN $2 AND $3
    ORDER BY country
""", [url, time_0, time_1]).fetchdf()

Print the first couple columns of data to get an understanding of the structure

df.head(4)

Understanding the data columns¶

The documentation can be found here for the individual columns:

https://github.com/dynamical-org/asos-parquet#key-fields

Print the columns as follows:

df.columns

Index(['station', 'valid', 'longitude', 'latitude', 'tmpf', 'tmpc', 'dwpf',
       'dwpc', 'relh', 'drct', 'sknt', 'gust', 'alti', 'mslp', 'vsby', 'p01i',
       'p01m', 'state', 'geometry', 'name', 'elevation', 'country', 'county',
       'wfo', 'tzname', 'bbox', 'year'],
      dtype='str')

Now create a dictionary which assigns descriptions to each column

variables = {
    'tmpf': "Temperature in Fahrenheit",
    'tmpc': "Temperature in Celsius",
    'dwpf': "Dewpoint in Fahrenheit",
    'dwpc': "Dewpoint in Celsius", 
    'relh': "Relative humidity in percent", 
    'drct': "Wind direction in degrees", 
    'sknt': "Wind speed in knots", 
    'gust': "Wind gusts in knots", 
    'alti': "Altimeter setting in inches of mercury", 
    'mslp': "Mean sea level pressure", 
    'vsby': "Visibility in statute miles", 
    'p01i': "P01I", 
    'p01m': "P01M"
}

Plot Timeseries data from a specific station¶

Select some of the variables from the Boulder, Colorado station, and set the time of each observation as the Dataframe’s index.

df_bdu = df.loc[df['station'] == "BDU"]
df_bdu.head(2)

df_select = pd.DataFrame({
    'date': df_bdu['valid'],
    'tmpc': df_bdu['tmpc'],
    'dwpc': df_bdu['dwpc'],
    'relh': df_bdu['relh'],
    'drct': df_bdu['drct'],
    'sknt': df_bdu['sknt'],
    'alti': df_bdu['alti'],
}).set_index('date')

# print the head of the dataframe
df_select.head(2)

Plotting Across All Stations using Lonboard¶

Now we will use the library (Lonboard)[https://developmentseed.org/lonboard/latest/] to plot the latest temperature data across all stations.

Get the newest observations from each station

df_latest = df.groupby('station').tail(1).sort_values(by='station', ignore_index=True)
df_latest.head(3)

df_latest.columns

Index(['station', 'valid', 'longitude', 'latitude', 'tmpf', 'tmpc', 'dwpf',
       'dwpc', 'relh', 'drct', 'sknt', 'gust', 'alti', 'mslp', 'vsby', 'p01i',
       'p01m', 'state', 'geometry', 'name', 'elevation', 'country', 'county',
       'wfo', 'tzname', 'bbox', 'year'],
      dtype='str')

Create a geometry from the point data using longitude and latitude

geometry = gpd.points_from_xy(df_latest['longitude'], df_latest['latitude'])

And create a GeoPandas DataFrame. We want the data in a form with columns of: [index, feature0, ..., featureN, geometry]. We will use tmpf which is the temperature in degrees Fahrenheit as the singular feature.

gdf2 = gpd.GeoDataFrame(
    df_latest[['tmpf', 'relh']],
    geometry=geometry,
    crs="EPSG:4326",
)
gdf2.head(3)

Figure out the minimum and maximum temperature values for the dataset

min_bound = np.min(gdf2['tmpf'])
max_bound = np.max(gdf2['tmpf'])

print(f"min temperature: {np.min(gdf2['tmpf'])} degF, max temperature: {np.max(gdf2['tmpf'])} degF")

min temperature: 19.4 degF, max temperature: 107.0 degF

normalized_value = 1 - (gdf2["tmpf"] - min_bound) / (max_bound - min_bound)
fill_color = apply_continuous_cmap(normalized_value, RdBu_10)
radius = 20_000

layer = ScatterplotLayer.from_geopandas(
    gdf2,
    get_fill_color=fill_color,
    get_radius=radius,
    radius_units="meters",
    radius_min_pixels=0.1,
)

Mapping the Temperature Gradient with Lonboard¶

Colors are plotted with a red-blue divergent color scheme. Warmer temperatures are denoted with ‘red’ and cooler temperatures are ‘blue’. An example is below:

Image(filename='thumbnails/temperature.png')

m = Map(layer)
m

Next, Relative Humidity (‘relh’)¶

Dark green means a higher value. Red means a lower value.

Image(filename='thumbnails/relative_humidity.png')

min_bound = np.min(gdf2['relh'])
max_bound = np.max(gdf2['relh'])
normalized_value = (gdf2["relh"] - min_bound) / (max_bound - min_bound)
fill_color2 = apply_continuous_cmap(normalized_value, BrBG_10)
radius2 = 20_000
layer2 = ScatterplotLayer.from_geopandas(
    gdf2,
    # extensions=[filter_extension],
    get_fill_color=fill_color2,
    get_radius=radius2,
    radius_units="meters",
    radius_min_pixels=0.1,
)
m = Map(layer2)
m