NASA API: World Map of Fireball Impacts¶

Overview¶

This notebook will cover all the steps to access bright meteor and fireball impact data collected by NASA’s CNEOS and produce a global plot of impact sites

Prerequisites
CNEOS Overview
Imports
Access and Organize Data
Plot Earth Fireball Impacts
Summary

Prerequisites¶

Concepts	Importance	Notes
Intro to Matplotlib	Necessary	Plotting on a data
Intro to GeoPandas	Necessary	Plotting on a world map
Intro to Pandas	Necessary	Familiarity with working with dataframes

CNEOS Overivew¶

A shooting star is a common term for a meteor and form bright trails of light that are often bright enough to be visible during the day. A “fireball” is a term for expectionally bright meteor that enters the Earth’s atmosphere at high speeds. A meteor that form fireballs can be over one meter long. A “bolide” typically refers to a fireball that breaks up in the atmosphere. These objects are tracked by the Center for Near Earth Object Studies (CNEOS) and information about the Peak Brightness, Velocity, and Joules of energy radiatated can be retrieved from CNEOS via an API

Fireball Data API¶

This notebook will query the Fireball API for data from the last decade of observations

JPL’s Center for Near-Earth Objects Studies API “Fireball” is an API that will return machine-readable data in the form of a JSON.

All queries to the Fireball Data API make requests to https://ssd-api.jpl.nasa.gov/fireball.api

The API accepts different query parameters to filter the data

Directly querying https://ssd-api.jpl.nasa.gov/fireball.api will return all the data of fireball impacts in reverse-chronological order

Parameter	Type	Description
date-min	string	exclude data earlier than this date `YYYY-MM-DD` or date/time `YYYY-MM-DDThh:mm:ss`
date-max	string	exclude data later than this date `YYYY-MM-DD` or date/time `YYYY-MM-DDThh:mm:ss`
energy-min	string	exclude data with total-radiated-energy less than this positive value in joules ×1010 (e.g., `0.3` = 0.3×1010 joules)
energy-max	string	exclude data with total-radiated-energy greater than this (see energy-min)
impact-e-min	string	exclude data with estimated impact energy less than this positive value in kilotons (kt) (e.g., `0.08` kt)
impact-e-max	string	exclude data with total-radiated-energy greater than this (see impact-e-min)
alt-min	number	exclude data from objects with an altitude less than this (e.g., `22` meaning objects smaller than this)
alt-max	number	exclude data from objects with an altitude greater than this (e.g., `17.75` meaning objects larger than this)
req-loc	boolean	location (latitude and longitude) required; when set `true`, exclude data without a location
req-alt	boolean	altitude required; when set `true`, exclude data without an altitude
req-vel-comp	boolean	pre-entry velocity components required; when set `true`, exclude data without pre-entry velocity components
vel-comp	boolean	include pre-entry velocity components
sort	string	sort data on the specified field: “date”, “energy”, “impact-e”, “vel”, or “alt” (default sort order is ascending: prepend “-“ for descending)
limit	number	limit data to the first N results (where N is the specified number and must be an integer value greater than zero)

Results from API will be returned as JSON with a number of fields

Field	Description
date	date/time of peak brightness (GMT)
lat	latitude at peak brightness (degrees)
lon	longitude at peak brightness (degrees)
lat-dir	latitude direction (“N” or “S”)
lon-dir	latitude direction (“E” or “W”)
alt	altitude above the geoid at peak brightness (km)
energy	approximate total radiated energy (10^10 joules)
impact-e	approximate total impact energy (kt)
vx	pre-entry velocity (Earth centered X component, km/s)
vy	pre-entry velocity (Earth centered Y component, km/s)
vz	pre-entry velocity (Earth centered Z component, km/s)

Example JSON result¶

Return the last 3 records https://ssd-api.jpl.nasa.gov/fireball.api?limit=3

{
    "signature":
        {
            "version":"1.0",
            "source":"NASA/JPL Fireball Data API"
        },
    "count":"3",
    "fields":["date","energy","impact-e","lat","lat-dir","lon","lon-dir","alt","vel"],
    "data":[
        ["2024-06-03 01:13:51","2.6","0.092","63.1","S","53.2","W","60.0",null],
        ["2024-06-01 23:24:59","7.9","0.25","1.0","S","15.9","W","26.5","12.6"],
        ["2024-05-27 03:19:36","8.4","0.26","1.7","N","39.4","W","56.0","39.2"]
    ]
}

Imports¶

import requests                  # submit API request query
import pandas as pd              # organizes data retrieved by the API
import geopandas                 # generate a world map
import matplotlib.pyplot as plt  # plotting data

Request Fireball Data via API¶

To retrieve all the fireballs recorded in the last decade (2014-2024)

data_since = "2014-01-01"
last_decade_fireball = requests.get(f"https://ssd-api.jpl.nasa.gov/fireball.api?date-min={data_since}")
last_decade_fireball.status_code # 200 is a valid request

200

fireball_json = last_decade_fireball.json()

# print out data labels
print(f"fields = {fireball_json['fields']}")

fields = ['date', 'energy', 'impact-e', 'lat', 'lat-dir', 'lon', 'lon-dir', 'alt', 'vel']

# convert JSON data to a Pandas dataframe
fireball_df = pd.DataFrame(fireball_json["data"], columns=fireball_json["fields"])
fireball_df

# remove nan and duplicated data rows and reindex rows
fireball_df.dropna(inplace=True, ignore_index=True)

# convert columnss from strings to a float
fireball_df["energy"] = fireball_df["energy"].astype(float)
fireball_df["impact-e"] = fireball_df["impact-e"].astype(float)
fireball_df["lat"] = fireball_df["lat"].astype(float)
fireball_df["lon"] = fireball_df["lon"].astype(float)
fireball_df["alt"] = fireball_df["alt"].astype(float)
fireball_df["vel"] = fireball_df["vel"].astype(float)

# convert latitude to negative if lat-dir is S and longitude to negative if lon-dir is W
fireball_df['lat'] = fireball_df['lat'].mask(fireball_df['lat-dir'].eq('S'), -fireball_df['lat'])
fireball_df['lon'] = fireball_df['lon'].mask(fireball_df['lon-dir'].eq('W'), -fireball_df['lon'])

# rename columns
fireball_df = fireball_df.rename(columns= {"impact-e": "impact energy",
                                           "lat": "latitude",
                                           "lat-dir": "latitude direction",
                                           "lon": "longitude",
                                           "lon-dir": "longitude direction",
                                           "alt": "altitude",
                                           "vel": "velocity"})


fireball_df

fireball_df.info()

<class 'pandas.DataFrame'>
RangeIndex: 263 entries, 0 to 262
Data columns (total 9 columns):
 #   Column               Non-Null Count  Dtype  
---  ------               --------------  -----  
 0   date                 263 non-null    str    
 1   energy               263 non-null    float64
 2   impact energy        263 non-null    float64
 3   latitude             263 non-null    float64
 4   latitude direction   263 non-null    str    
 5   longitude            263 non-null    float64
 6   longitude direction  263 non-null    str    
 7   altitude             263 non-null    float64
 8   velocity             263 non-null    float64
dtypes: float64(6), str(3)
memory usage: 24.1 KB

Plot the Energy of Fireballs¶

# retrieve world map from GeoPandas
world_map = geopandas.read_file("https://naciscdn.org/naturalearth/110m/cultural/ne_110m_admin_0_countries.zip")

# Set up world map plot
fig, ax = plt.subplots(figsize=(15, 10))
world_map.plot(color="grey", ax=ax)

# Plot Fireball Locations with Energy
x = fireball_df["longitude"]
y = fireball_df["latitude"]
z = fireball_df["energy"]
plt.scatter(x, y, s=5*z, c=z, cmap="autumn")
plt.colorbar(label="Approximate Total Radiated Energy (10^10 Joules)")

# Setup Axis Limits and Title/Labels
plt.xlim([-180, 180])
plt.ylim([-90, 90])
plt.title("Energy of Fireball Observations")
plt.xlabel("Longitude (Degrees)")
plt.ylabel("Latitude (Degrees)")
plt.show()

Plot the Impact Energy of Fireballs¶

# retrieve world map from GeoPandas
world_map = geopandas.read_file("https://naciscdn.org/naturalearth/110m/cultural/ne_110m_admin_0_countries.zip")

# Set up world map plot
fig, ax = plt.subplots(figsize=(15, 10))
world_map.plot(color="grey", ax=ax)

# Plot Fireball Locations with Energy
x = fireball_df["longitude"]
y = fireball_df["latitude"]
z = fireball_df["impact energy"]
plt.scatter(x, y, s=5*z, c=z, cmap="autumn")
plt.colorbar(label="Approximate Total Impact Energy (kt)")

# Setup Axis Limits and Title/Labels
plt.xlim([-180, 180])
plt.ylim([-90, 90])
plt.title("Total Impact Energy of Fireball Observations")
plt.xlabel("Longitude (Degrees)")
plt.ylabel("Latitude (Degrees)")
plt.show()

Plot the Velocity of Fireballs¶

# retrieve world map from GeoPandas
world_map = geopandas.read_file("https://naciscdn.org/naturalearth/110m/cultural/ne_110m_admin_0_countries.zip")

# Set up world map plot
fig, ax = plt.subplots(figsize=(15, 10))
world_map.plot(color="grey", ax=ax)

# Plot Fireball Locations with Velocity
x = fireball_df["longitude"]
y = fireball_df["latitude"]
z = fireball_df["velocity"]
plt.scatter(x, y, s=5*z, c=z, cmap="winter")
plt.colorbar(label="Pre-Entry Velocity (km/s)")

# Setup Axis Limits and Title/Labels
plt.xlim([-180, 180])
plt.ylim([-90, 90])
plt.title("Pre-Entry Velocity of Fireball Observations")
plt.xlabel("Longitude (Degrees)")
plt.ylabel("Latitude (Degrees)")
plt.show()

Plot the Altitude of Fireballs¶

# retrieve world map from GeoPandas
world_map = geopandas.read_file("https://naciscdn.org/naturalearth/110m/cultural/ne_110m_admin_0_countries.zip")

# Set up world map plot
fig, ax = plt.subplots(figsize=(15, 10))
world_map.plot(color="grey", ax=ax)

# Plot Fireball Locations with Energy
x = fireball_df["longitude"]
y = fireball_df["latitude"]
z = fireball_df["altitude"]
plt.scatter(x, y, s=5*z, c=z, cmap="bone")
plt.colorbar(label="Altitude Above Geoid at Peak Brightness (km)")

# Setup Axis Limits and Title/Labels
plt.xlim([-180, 180])
plt.ylim([-90, 90])
plt.title("Altitude of Fireball Observations")
plt.xlabel("Longitude (Degrees)")
plt.ylabel("Latitude (Degrees)")
plt.show()

Resources and references¶

More information about plotting a world map with GeoPandas