Trapped Proton Models: TPM-1 and Beyond

1. Trapped Proton Models:TPM-1 and Beyond Stuart L. Huston Science Applications International Corporation Working Group Meeting on New Standard Radiation Belt and Space Plasma Models for Spacecraft Engineering College Park, MD 2004 October 5�8

2. Outline NOAAPRO/LATRM TPM-1 Recent Work Implications/Needs/etc.

3. The Basic Question Q: Why spend ?$ to get data and develop a radiation environment model? A: Uncertainty in the environment can increase total life cycle costs by many G$ Trading system lifetime, reliability, replenishment, performance, mass, numbers of s/c, etc. Risk management/mitigation Overdesign vs. underdesign Uncertainty in the environment may limit or prohibit certain technologies and/or missions Without good environment models, effects models are useless of limited value

4. NOAAPRO/LATRM Objective: Develop solar-cycle dependent model of low altitude (<1000 km) protons Use NOAA/POES MEPED data (>16, > 36, > 80 MeV) Implementation: Empirical curve fit for solar cycle variation Input is latitude/longitude/altitude/date Model calculates magnetic coordinates Output is simple integral flux

5. Proton Flux is Controlled by Atmospheric Density/Solar Activity This slide just basically shows the variation of the proton flux (at the magnetic equator) over the solar cycle. This was really the first long-term observation of the variation. The dashed line is the solar F10.7 flux (on the right axis). You can also see some transient events, but these aren�t included in the model. The could be important, however, for understanding some of the loss mechanisms or unusual source mechanisms.This slide just basically shows the variation of the proton flux (at the magnetic equator) over the solar cycle. This was really the first long-term observation of the variation. The dashed line is the solar F10.7 flux (on the right axis). You can also see some transient events, but these aren�t included in the model. The could be important, however, for understanding some of the loss mechanisms or unusual source mechanisms.

6. Introduction of Phase Lag Gives Good Correlation with Data

7. Model/Data Comparison � Magnetic Equator This is a comparison of the NOAAPRO model predictions with the actual data. (This is the same data as shown in the original slide.)This is a comparison of the NOAAPRO model predictions with the actual data. (This is the same data as shown in the original slide.)

8. TPM-1 Problems w/NOAAPRO: Poor energy resolution Limited spatial coverage Objective: Combine CRRESPRO and NOAAPRO Model valid from low-altitude to near GEO Challenges: Poor spatial resolution/biasing in CRRESPRO at low altitudes Intercalibration of very different detectors Combining integral flux data (POES) with differential data (CRRES) Approach: Convert CRRESPRO from bins to grids Scale spectra based on NOAAPRO Use NOAAPRO solar cycle variation

9. CRRESPRO 16.9 MeV Quiet

10. TPM-0 16.9 MeV Quiet

11. Calibration of POES Detectors

12. Electron Contamination(calculations by T. Cayton, LANL)

13. Comparison � Low Altitude Models

14. TPM-1 Spectra (High-L)

15. Flux vs. AltitudeTPM-1 Quiet Model

16. Flux vs. AltitudeTPM-1 Quiet Model vs. AP-8

17. TPM-1 Summary � As Delivered Energy Range: 1 � 81.5 MeV Regional coverage: 300 - 36,000 km 1.2 < L < 5.5 0 < l < 50� Inputs: Latitude, Longitude, Altitude (internal field model calculates magnetic coordinates Can perform orbital integration Date (i.e., phase of solar cycle) F10.7 history Time coverage: Data cover 1978 � 1995 (with special attention to 1990-1991) Valid for any point in solar cycle Time scale (vs. resolution) ~1 month

18. TPM-1 Summary (concluded) Calibration/Intercalibration Detector calibration issues as discussed Very limited validation/characterization performed Comparison with other models Absolute magnitude within about a factor of 5 of AP-8 (depending on energy/region) Spectra generally harder than AP-8 Radial profiles show that flux peaks at lower altitudes than AP-8 Low altitude fluxes are lower than CRRESPRO, higher than AP-8 Additional comments: Another solar cycle of POES data for incorporation into model High altitude portion (CRRESPRO) based on a snapshot at solar max Quiet and active models � what do we do about active periods?

19. New Directions

20. Recent Work (Sponsored by SEE & LWS) Statistical solar cycle variation (Xapsos, 2002) Slot Dynamics Study (2003) Investigated statistics of slot region Long-Term Dynamics (2004 � 2005) Extend energy range by combining w/SAMPEX data (collaboration with BIRA) Extend high energy/low altitude data to equator with analytical model Salammb� (collaboration with ONERA) Develop statistical model for low-altitude solar variation (collaboration with GSFC)

21. Statistical Solar Cycle Variation

22. Statistical Solar Cycle Variation

23. Transient Belts: Problem The CRRES spacecraft observed the formation of a transient radiation belt which formed in March 1991. This observation led to the development of a �Quiet� and and �Active� model in the CRRESPRO model. As this chart shows, CRRESPRO predicts an enhancement of a factor of about 50 in the proton flux at around L~2.4. Unfortunately for spacecraft designers, there is no way of knowing how to interpret this data or use it in designing spacecraft to operate in the slot region. We know that the flux can be enhanced to these levels, but what is the probability of such an enhancement? The CRRES spacecraft observed the formation of a transient radiation belt which formed in March 1991. This observation led to the development of a �Quiet� and and �Active� model in the CRRESPRO model. As this chart shows, CRRESPRO predicts an enhancement of a factor of about 50 in the proton flux at around L~2.4. Unfortunately for spacecraft designers, there is no way of knowing how to interpret this data or use it in designing spacecraft to operate in the slot region. We know that the flux can be enhanced to these levels, but what is the probability of such an enhancement?

25. Variability vs. L This chart shows the variability of the > 36 MeV proton flux as a function of L. The left chart shows the flux as a function of confidence level. The curve labeled �10%� would be exceeded 10% of the time; this curve can also be considered to be the 90% confidence level. Also shown are the median and mean values. The chart on the right shows the ratio of the 10% flux to the 90% flux, the median, and the mean. Also shown in this chart is the ratio of the CRRESPRO active and quiet fluxes. This chart shows the variability of the > 36 MeV proton flux as a function of L. The left chart shows the flux as a function of confidence level. The curve labeled �10%� would be exceeded 10% of the time; this curve can also be considered to be the 90% confidence level. Also shown are the median and mean values. The chart on the right shows the ratio of the 10% flux to the 90% flux, the median, and the mean. Also shown in this chart is the ratio of the CRRESPRO active and quiet fluxes.

26. TPM-1 Summary #2 Attempt to provide successor to AP-8, but limited energy range limits usefulness Can be used to assess uncertainty in AP-8 Very limited validation performed No provision for maintenance/upgrades Work done since initial release has not been incorporated into TPM-1

27. Modeling/Software Issues ALL particles: plasma, trapped protons/electrons, solar (protons, heavies), GCR protons .01(?) < E < 400 MeV electrons 1 keV(?) < E < 10 MeV Average, �worst-case�, % of time above threshold, maximum time continuously above threshold Time scale of variations Estimate of uncertainty ? safety margins How much due to variability of environment � statistical model How much due to uncertainty in measurement, How much due to modeling assumptions/simplifications Verification/Validation What aspects of software implementation need to be considered in determining model architecture? Elliptical orbits: different regions of space with different time scales, phasing, etc.

28. Data Issues Level of processing time averaging calibration, contamination, etc. � Intercalibration � different detectors, different orbits, different epochs Access to data � treatment of sensitive/proprietary data �operational/piggyback� detectors on commercial s/c classified s/c High fidelity vs. low fidelity detectors Is there any archival data worth using?

29. Programmatic Issues Long-term commitment to model development and maintenance e.g., IRI, original TREMP Understanding of financial benefit Current approach makes the job difficult new proposals every few years ? small chunks of the total based on anticipated funding levels development of ad hoc models for specific engineering applications need a global roadmap and commitment to follow it Inter-Agency, Inter-Program collaboration �Modeling is critical, but not a PI activity� (Vette, 1991) Need an �impartial� modeling center with access to data a la Aerospace/NSSDC activities Can engineering modeling needs impact scientific missions? Will there be an engineering mission to map/monitor the environment? Export restrictions collaboration, data sharing model distribution How to implement model E.g., GEOSpace, SPENVIS Stand-alone vs. web-based Commercial tools (e.g., Space Radiation�)