1. Forecasting the Perfect Storm
Developing new space weather tools: Bridging between the fundamental science and operations. 15 Nov 2015.
DAVE PITCHFORD

2. Spacecraft Operator Need for Space Weather Forecasts / Predictions
Support operations
Scheduling of nominal operations
Scheduling of contingency recover operations
Scheduling of ground system maintenance / downtime
Post event analysis
Future spacecraft Requirements Specification

3. GOES-15 ELECTRON FLUXES

4. Space Weather Forecasting and Prediction – The Met Office Analogy
Observations are needed to feed the forecasting machinery!!
~270 Synoptic Stations
Provide initial conditions for the weather forecast models
MMS – Met Monitoring System
MetUM
UK MET OFFICE FORECAST

5. Space Weather Forecasting and Prediction – Cold Reality!
Observations are also needed to feed the forecasting machinery!!
Two GOES satellites and ACE are the only operational space based assets!
Need to provide initial conditions for the Space Weather forecast models
MMS – Met Monitoring System
MagnetosphereUM
Magnetosphere Forecast

6. The Magnetosphere Forecast Challenge!
What we need from the Magnetosphere Forecast:
Forecast of the radiation belt fluxes (electrons and protons BUT with the emphasis on the electron population;
Coverage of all satellite orbits – ie: LEO, MEO, GEO;
Need to capture the dynamic variation (storm / substorm) of the fluxes.
Physics or Empirical / Systems Science based models?
Physics Based Models (eg: BAS, VERB, IMPTAM) , built up from first principles, naturally result in increased and superior understanding of the physical processes involved in the magnetosphere. However building such a model for reliable forecasting clearly requires relatively complete understanding of the physics involved.
A clear advantage of the Physics Based Model approach is that they fundamentally cover all satellite orbits and the variations along the orbit.
Empirical / Systems Science based models are reliant on having available continuous measurements of the parameters being predicted.
The SNB3GEO provides quite accurate >800 keV / >2 MeV electron flux forecasts using the GOES-13 / 15 data.

7. Magnetosphere Forecast – Way Ahead
Research teams have a lot of work to meet the challenge:
Physics Based Models: hampered by incomplete knowledge of the underlying physics;
Empirical Models: hampered by very sparse in–orbit sensor coverage – which is vital to feed the forecasting machinery.
Clearly hybrid models, ie: coupling Physics Based Models with Empirical Models, should be investigated.
Validation of full magnetosphere coupled models requires additional in – orbit Space Environment Sensor assets to be available, at different orbits.

8. Drivers for new requirements…….

9. 1. New Orbits

10. Communications Satellite Orbits
Ra (km)
Rp (km) I
(Deg)
GEO
42164
GTO
~6600
6 - 28
O3B
14433
Earth Radius
~6371 km

11. 2. Electric Orbit Raising

12. Radiation Belt Data & Forecast

13. Traditional Orbit Raising Missions
In the ‘good old days’ the spacecraft was injected from GTO to GEO with a Hohmann transfer using a single firing of a solid propellant Apogee Boost Motor.
More recently the same effect has been achieved using several successive firings of a lower (but still significant) thrust Liquid Apogee Engine; leading to a total launch mission duration of say 6 – 10 days.

14. Electric Orbit Raising Missions
The advent of low cost / lower capability launch vehicles (eg: Falcon 9) has led to the emergence of low thrust Electric Orbit Raising missions;
The spacecraft carries much less chemical fuel and uses a (typically) Xe fuelled electric thruster (eg: Fakel SPT, Stationary Plasma Thruster), leading to a significantly lower Launch Vehicle cost at the expense of a much longer (~200 days) Transfer Orbit Mission.

15. TACSAT-4 PROTON ENVIRONMENT / SOLAR ARRAY DEGRADATION

16. Radiation Belt Data & Forecast

17. 3. Digital Transparent Processor Payloads

18. Traditional Communications Satellite Payload

19. Digital Transparent Transponder Payloads - 1
Angeletti et al, 2008.

20. Digital Transparent Transponder Payloads - 2
Angeletti et al, 2008.

21. Increasing Performance, Shrinking Feature Size………

22. Halloween Storms, Oct 2003 – GOES Protons

23. Halloween Storms, Oct 2003 – ACE SIS Heavy Ions

24. Any Questions?