How do I set up a contact planning tool for weather-aware downlink routing across multiple ground stations?

Here’s a practical way to set up a contact planning tool for weather-aware downlink routing across multiple ground stations.

1) Define the goal

You want the planner to choose, for each satellite downlink opportunity:

• which ground station(s) can receive it
• when the contact is possible
• whether weather makes it usable
• how to route downlinks to maximize success, throughput, or priority

This is usually a constraint-based scheduling problem with weather-dependent availability.

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2) Gather the required inputs

Satellite/mission inputs

• Orbit ephemeris or TLEs
• Downlink window requirements
  • minimum elevation angle
  • frequency bands
  • data volume to send
  • contact duration constraints
• Priority rules
  • urgent telemetry
  • science data
  • deadlines
• Link budget parameters
  • transmit power
  • antenna gains
  • modulation/coding
  • rain fade margin if relevant

Ground station inputs

For each station:

• latitude/longitude/altitude
• antenna capabilities
• supported bands
• max slew rate / pointing limits
• processing capacity
• maintenance blackout windows
• cost or preference score

Weather inputs

Per station, time-indexed:

• precipitation rate
• cloud cover
• humidity
• atmospheric attenuation estimates
• lightning / severe weather alerts
• wind limits for antenna safety

For RF downlinks, the most important weather variables are usually:

• rain rate
• cloud-free constraints if optical links are involved
• atmospheric attenuation
• storm warnings / safety shutdowns

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3) Build the contact prediction layer

First compute all nominal satellite-to-ground visibility windows.

Typical logic:

• propagate orbit
• find access intervals above elevation threshold
• compute expected SNR or link margin for each interval
• store as candidate contacts

Output should look like:

sat	station	start	end	max_elev	duration	link_margin
S1	GS-A	10:14	10:21	32°	7 min	4.2 dB
S1	GS-B	10:18	10:26	58°	8 min	7.8 dB

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4) Add weather-aware station availability

For each candidate contact, score weather impact at that station and time.

Example:

• If rain rate exceeds threshold, reduce link margin
• If wind exceeds safety threshold, mark station unavailable
• If severe weather alert, block contact

A simple rule set could be:

• Available if:

  • no severe weather alert
  • wind < antenna safety limit
  • rain attenuation keeps link margin above minimum

• Degraded if:

  • weather is marginal but still workable with reduced modulation

• Unavailable if:

  • station is forced offline
  • link margin drops below zero
  • safety constraint violated

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5) Choose a routing objective

Decide what the planner should optimize. Common objectives:

• maximize total downlinked data
• maximize success probability
• minimize cost
• prioritize urgent payloads
• balance load across stations
• minimize weather risk

A practical objective is often a weighted score:

score = data_value × reliability × station_preference − cost − risk_penalty

Where reliability comes from weather + link margin.

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6) Model the scheduling problem

You can formulate this as:

Option A: Greedy heuristic

Good for a first version.

1. generate candidate contacts
2. remove unavailable ones
3. sort by priority and quality score
4. assign downlinks until data is exhausted

Pros: easy, fast
Cons: may miss globally optimal plans

Option B: Integer Linear Programming (ILP)

Better for real planning. Decision variables:

• assign contact or not
• amount of data sent on each contact

Constraints:

• each station can only handle one contact at a time
• satellite cannot downlink to two stations simultaneously
• data volume must not exceed contact capacity
• weather constraints remove/penalize contacts
• deadline constraints

Objective:

• maximize value or minimize risk/cost

Option C: MILP with stochastic weather

Useful if weather uncertainty matters.

• use probabilities or scenarios
• optimize expected utility
• include fallback plans

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7) Create the weather decision logic

You have three common ways to incorporate weather:

Hard constraint

If weather exceeds threshold, contact is invalid.

Use when:

• station safety is critical
• you do not want any risky links

Soft penalty

Weather reduces score but doesn’t eliminate contact.

Use when:

• some degraded links are acceptable
• you want flexible routing

Probabilistic availability

Estimate success probability from weather forecast uncertainty.

Use when:

• forecasts are noisy
• you want robust planning

A simple success model might be:

• rain < threshold → 0.99 success
• moderate rain → 0.85
• heavy rain → 0.30
• severe storm → 0.0

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8) Implementation architecture

Core components

1. Orbit/contact generator

   • SGP4 or high-precision propagator
   • outputs station visibility windows

2. Weather ingestion service

   • pulls forecast/API data
   • maps weather to each station/time interval

3. Scoring engine

   • computes link margin and weather-adjusted score

4. Optimizer

   • greedy or MILP solver
   • produces schedule

5. Visualization/UI

   • timeline, map, station status, route assignments

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9) Suggested data structures

A simple schema:

{
  "satellite": "S1",
  "contacts": [
    {
      "station": "GS-A",
      "start": "2026-07-27T10:14:00Z",
      "end": "2026-07-27T10:21:00Z",
      "capacity_mb": 120,
      "link_margin_db": 4.2,
      "weather_risk": 0.12,
      "score": 78.5
    }
  ]
}

For weather, use:

{
  "station": "GS-A",
  "time": "2026-07-27T10:15:00Z",
  "rain_mm_hr": 6.2,
  "wind_mps": 8.1,
  "storm_alert": false,
  "availability": "degraded"
}

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10) Practical workflow

1. Load satellite ephemeris and station list
2. Compute contact windows
3. Pull weather forecasts for each station
4. Evaluate each contact’s weather-adjusted feasibility
5. Run optimizer
6. Export schedule
7. Re-run periodically as forecasts update

A rolling-horizon approach works well:

• plan 24–72 hours ahead
• refresh every 1–6 hours
• reschedule if weather changes

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11) Recommended tech stack

If you’re building this from scratch:

Python stack

• sgp4 or orekit for orbit propagation
• numpy/pandas for data handling
• pvlib or custom attenuation model for weather effects
• OR-Tools or PuLP for optimization
• FastAPI for service layer
• Plotly/Dash for dashboard

Weather data sources

• NOAA / NWS
• ECMWF-based products
• commercial forecast APIs
• station-local sensors, if available

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12) Start with a simple prototype

A good MVP:

• 1 satellite
• 3–5 ground stations
• hourly forecast grid
• hard weather thresholds
• greedy scheduler

Then upgrade to:

• finer time resolution
• better attenuation modeling
• MILP optimization
• forecast uncertainty handling

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13) Example decision rule

For each contact:

1. Compute nominal capacity from link budget and duration
2. Subtract weather attenuation
3. If remaining margin < minimum threshold, reject
4. Otherwise assign a score:
   • higher elevation
   • better weather
   • higher station priority
   • lower cost

Then choose the best non-conflicting contacts.

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14) Key design tips

• Keep contact generation separate from weather evaluation
• Store both forecast time and contact time because forecasts update
• Use station-specific weather thresholds
• Include fallback stations in your routing logic
• Log why each contact was rejected or selected

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If you want, I can also give you:

1. a Python package structure for this tool,
2. a sample MILP formulation, or
3. a working prototype script that schedules weather-aware contacts across multiple ground stations.