CelesTrak Data Source¶

CelesTrak is a public source for satellite Two-Line Element (TLE) data, maintained by T.S. Kelso since 1985. It provides free, frequently updated orbital element sets for thousands of satellites, making it a useful resource for satellite tracking, orbit determination, and space situational awareness.

Overview¶

Website: https://celestrak.org

Maintainer: Dr. T.S. Kelso

Update frequency: Multiple times daily (varies by satellite priority)

Access: No registration or API key required

Why CelesTrak?¶

Advantages¶

Free and Open: - No registration required - No rate limits for reasonable use - Publicly accessible worldwide - Stable, long-term availability

Well-Organized: - Satellites grouped by function and constellation - Consistent naming conventions - Regular group updates as missions change - Special groupings for recent launches and interesting events

Limitations¶

Update Latency: - Data comes from Space-Track.org with some delay - Not as current as direct Space-Track access - Typically 1-6 hours behind during active tracking periods

Limited Historical Data: - Focuses on current/recent TLEs - Historical archives available but not comprehensive - For deep historical analysis, use Space-Track.org directly

Satellite Groups¶

CelesTrak organizes satellites into logical groups accessible via simple names. These groups are maintained as constellations evolve.

Temporal Groups¶

Group	Description	Typical Count
`active`	All active satellites	~5,000+
`last-30-days`	Recently launched satellites	20-100 (varies)
`tle-new`	Newly added TLEs (last 15 days)	Variable

Communications¶

Group	Description
`starlink`	SpaceX Starlink constellation
`oneweb`	OneWeb constellation
`kuiper`	Amazon Kuiper constellation
`intelsat`	Intelsat satellites
`eutelsat`	Eutelsat constellation
`orbcomm`	ORBCOMM constellation
`telesat`	Telesat constellation
`globalstar`	Globalstar constellation
`iridium-NEXT`	Iridium constellation
`qianfan`	Qianfan constellation
`hulianwang`	Hulianwang Digui constellation

Earth Observation¶

Group	Description
`weather`	Weather satellites (NOAA, GOES, Metop, etc.)
`earth-resources`	Earth observation (Landsat, Sentinel, etc.)
`planet`	Planet Labs imaging satellites
`spire`	Spire Global satellites

Group	Description
`gnss`	All navigation satellites (GPS, GLONASS, Galileo, BeiDou, QZSS, IRNSS)
`gps-ops`	Operational GPS satellites only
`glonass-ops`	Operational GLONASS satellites only
`galileo`	European Galileo constellation
`beidou`	Chinese BeiDou/COMPASS constellation
`sbas`	Satellite-Based Augmentation System (WAAS/EGNOS/MSAS)

Scientific and Special Purpose¶

Group	Description
`science`	Scientific research satellites
`noaa`	NOAA satellites
`stations`	Space stations (ISS, Tiangong)
`analyst`	Analyst satellites (tracking placeholder IDs)
`visual`	100 (or so) brightest objects
`gpz`	Geostationary Protected Zone
`gpz-plus`	Geostationary Protected Zone Plus

Note: Group names and contents evolve as missions launch, deorbit, or change status. Visit CelesTrak GP Element Sets for the current complete list.

Data Quality and Currency¶

TLE updates depend on the object itself, it's tracking priority, how difficult to track it is, and how often it's maneuvering. CelesTrak provides statistics on the median and mean age of TLEs by group. This has been about 0.8 and 1.5 days respectively in recent years, but can vary.

Accuracy Considerations¶

For most operational applications TLEs should be updated daily. For research or general analysis, it's generally fine to just download and save the ephemeris once.

Propagation accuracy: - Best within hours of epoch - Degrades beyond 3-7 days for LEO - Acceptable for 1-2 weeks for MEO/GEO

When to refresh: - LEO tracking: Daily updates recommended - GNSS analysis: Weekly updates acceptable - GEO operations: Monthly updates sufficient

Access Methods¶

Brahe Integration¶

Brahe provides convenient API for CelesTrak access:

import brahe as bh

# Get ephemeris data
ephemeris = bh.datasets.celestrak.get_ephemeris("gnss")
# Returns: List of (name, line1, line2) tuples

# Get as propagators
propagators = bh.datasets.celestrak.get_ephemeris_as_propagators(
    "gnss",
    step_size=60.0
)

# Download and save
bh.datasets.celestrak.download_ephemeris(
    "gnss",
    "gnss_satellites.json",
    content_format="3le",
    file_format="json"
)

Best Practices¶

Respectful Usage¶

While CelesTrak has no strict rate limits, follow these guidelines:

DO: - Cache downloaded data locally - Refresh on reasonable schedules (hourly at most) - Use appropriate update intervals for your orbit regime - Implement exponential backoff on errors

DON'T: - Poll every minute (excessive) - Download all groups when you need one - Ignore errors and retry immediately - Run unattended scripts without rate limiting

Error Handling¶

Implement robust error handling:

import brahe as bh
import time

def download_with_retry(group, max_retries=3):
    """Download ephemeris with exponential backoff"""
    for attempt in range(max_retries):
        try:
            ephemeris = bh.datasets.celestrak.get_ephemeris(group)
            return ephemeris
        except Exception as e:
            if attempt < max_retries - 1:
                wait_time = 2 ** attempt  # Exponential backoff
                print(f"Attempt {attempt + 1} failed: {e}")
                print(f"Retrying in {wait_time} seconds...")
                time.sleep(wait_time)
            else:
                print(f"All retries failed for {group}")
                raise

# Use with error handling
try:
    ephemeris = download_with_retry("gnss")
except Exception as e:
    print(f"Failed to download: {e}")
    # Fall back to cached data

Caching Strategy¶

Implement local caching to reduce load:

import brahe as bh
from pathlib import Path
import time
import json

def get_cached_ephemeris(group, cache_dir="./cache", max_age_hours=24):
    """Get ephemeris from cache or download if stale"""
    cache_path = Path(cache_dir)
    cache_path.mkdir(exist_ok=True)

    cache_file = cache_path / f"{group}.json"

    # Check if cache exists and is fresh
    if cache_file.exists():
        age_seconds = time.time() - cache_file.stat().st_mtime
        age_hours = age_seconds / 3600

        if age_hours < max_age_hours:
            # Use cached data
            with open(cache_file, 'r') as f:
                return json.load(f)

    # Cache miss or stale - download fresh data
    ephemeris = bh.datasets.celestrak.get_ephemeris(group)

    # Save to cache
    with open(cache_file, 'w') as f:
        json.dump(ephemeris, f)

    return ephemeris

# Use cached ephemeris
ephemeris = get_cached_ephemeris("gnss", max_age_hours=6)