Space weather impacts and predictions: relevant spatial and - PowerPoint PPT Presentation

Space weather impacts and predictions: relevant spatial and temporal scales Pulkkinen, A. NASA Goddard Space Flight Center Heliophysics Science Division 1

Contents • Identification of end-user needs (and the spatiotemporal context). • Power transmission industry. • Human spaceflight. • Addressing the end-user needs (and dealing with the spatiotemporal complexity of the relevant space environmental phenomena). • Geomagnetic storm benchmarks. • Solar energetic particle (SEP) predictions. 2

Identification of end-user needs 3

End-user needs • If we acknowledge space weather as the societal dimension of heliophysics, understanding the impacts and associated end-user needs are the foundation of the field. • Our space weather work must be informed by those needs and strive toward generating information that is actionable. 4

End-user needs – power transmission industry • While also predictions are of interest, the main U.S. focus right now is on hazard assessments. • To enable hazard assessments, space weather extremes need to be communicated to the end-user in the form of benchmarks. 5

GMD benchmark requirements System size ~500 km Line lengths ~100 km • Science side needs to provide information about a physical parameter that is directly applicable/actionable to further engineering analyses. (geoelectric field) • We need to address the following key characteristics of the extreme geoelectric fields: (Element 1) i. Amplitude. (Element 2) ii. Spatial structure. (Element 3) iii. Temporal waveform. Pulkkinen et al. (2012) • Full 1-3 day storm characterized. Response scale • 1-10 s sampling to capture rapid enhancements that may ~5-10 min. compromise voltage stability. • Longer duration enhancements necessary for thermal heating- related problems. • Science analyses also need to characterize the occurrence rates of i-iii. Marti et al. (2013) 6

GMD benchmark requirements • The geomagnetic induction process that generates the geoelectric field is dependent on external and internal factors: iv. Many different near space electric currents systems contribute to driving of geomagnetic induction. The effect of the geomagnetic latitude, and possibly local time, needs to be taken into account. (Element 4) v. The local ground conductivity dictates the ground response. Local geology needs to be taken into account. (Element 5) 7

End-user needs – human spaceflight • While low-inclination LEO (ISS orbit) is fairly benign from the space radiation perspective, deep space environment experienced in the Artemis program poses a much more significant challenge. • The key problem is ionizing radiation: > 10 MeV ions for EVAs and > 100 MeV ions for the crew inside the vehicle. • Primary sources for energetic ions contributing to possible problems include galactic cosmic rays, SEPs and inner radiation belt – only the SEP component discussed here. 8

End-user needs – human spaceflight • Due to the SEP challenge, Artemis will have storm shelter as a part of the ops. The shelter needs to be deployed in 30 min from ( Townsend et al ., 2018) è Predictive capability plays a critical role in the ops. • We need to have information about elevated, likely mostly CME shock-driven, energetic ion fluxes at the location of the vehicle. 9

End-user needs – human spaceflight Pre-eruption Post-eruption Post-eruption SEP forecasts forecasts timeline forecasts SEP flux (> 10 MeV) Time All clear/ Flare onset Event over Not clear (~10-minute SEP Inform the crew about (1-day onset and peak flux predicted evolution of the forecast) forecast) event (~1-day forecast) 10

Addressing the end-user needs 11

GMD benchmark(s) – spatiotemporal representation per the NERC standard ! 𝐹 𝑦, 𝑧, 𝑢 depends on: 𝐹 𝑦, 𝑧, 𝑢 = 𝐹 ( (𝑦, 𝑧, 𝑢) External excitation • ! 𝐹 + (𝑦, 𝑧, 𝑢) ! 𝐶 .(4 𝑦, 𝑧, 𝑢 Ground response • dictated by 𝜏(𝑦, 𝑧, 𝑨) ≈ 𝐹 -./0 (𝑦, 𝑧) 𝑔 ( 𝑢 𝑕 ( (𝑦, 𝑧) 𝑔 + (𝑢)𝑕 + (𝑦, 𝑧) Assume spatially homogeneous field ≈ 𝐹 -./0 (𝑦, 𝑧) 𝑔 ( (𝑢) 7 1 𝑔 + (𝑢) Factorize & approximate the primary dependencies ≈ 𝐹 9 7 𝛽(𝑧) 7 𝛾(𝑦, 𝑧) 𝑔 ( (𝑢) 7 1 𝑔 + (𝑢) Latitude dependence Ground response dependence 12

GMD benchmark(s) – regional vs localized enhancements Pulkkinen et al. (2015) Geoelectric field distribution at 07:32:20 UT. Max. |E|: 4.41 V/km. Geoelectric field distribution at 16:49 UT. Max. |E|: 5.68 V/km. 75 o N 75 o N 400 km 400 km 70 o N 70 o N 144 o E 144 o E 300 300 200 200 72 o E E o 100 72 100 0 0 Geomanetic latitude [deg] Geomanetic latitude [deg] 65 o N 6 o 1 V/km 5 1 V/km N 60 o N 6 o 0 N 55 o N 55 o N E 126 o E 90 o E 108 o E 108 o E 1 o 2 90 6 o E Geomagnetic longitude [deg] Geomagnetic longitude [deg] Fig. 3 Same as Fig. 1 but for October 30, 2003 at 16:49 UT. A station in the blue group experiences the largest single station geoelectric field Fig. 1 Computed geoelectric field distribution on November 24, 2001 at 07:32 UT. The colored circles show the three station groups used in spatial magnitude of 5.7 V/km. The spatially averaged field magnitudes for blue , green , and red groups are 1.5, 0.6, and 0.1 V/km, respectively averaging: blue , green , and red groups. The green group generates the largest average geoelectric field magnitude of 2.8 V/km. Note that the maximum geoelectric field amplitude indicated in the top of the figure refers to a single station maximum, not to group average. Corrected geomagnetic coordinates and Oblique Mercator map projection are used 𝐹 9 quantified with a spatial average 𝐹 9 quantified with individual stations E-field applied regionally E-field applied locally 13

NERC GMD 8 benchmark white 10 ������������������� ������������������� GMD benchmark(s) paper ������������������� # of 10 s values per 100 years 6 10 10 4 10 8 E-field [V/km] 6 4 � 10 • Element 1: amplitude 𝐹 0 2 1 2 10 10 Return Level [Years] 0 • Element 2: spatial structure 10 �� �� �� 0 1 � 10 10 10 10 10 10 |E| [V/km] • Element 3: reference temporal waveform 𝑔 ( 𝑢 ) • Element 4: geomagnetic latitude dependence 𝛽 ( 𝑧 ) • Element 5: dependence on the local ground conductivity 𝛾 ( 𝑦 , 𝑧 ) Scaling factors from MT 2 10 surveys Scaling factors for different Scaling factor for the drop physiographic regions between 40-60 deg 1 Max. |E| [V/km] 10 0 10 − 1 10 − 50 0 50 14 Geomag. lat. [deg]

SEP prediction approaches Models available at iswa.gsfc.nasa.gov & ccmc.gsfc.nasa.gov All clear/pre-eruption forecasts Post-eruption timeline forecasts Post-eruption forecasts Mays et al. (2017) 15

Conclusions • From the space weather standpoint, end-user impacts and needs are the fundamental driver for identifying i) actionable physical parameters of interest, ii) relevant spatiotemporal scales. • “Unfortunately” it is often necessary to address a blend of global and local spatial scales and a wide range of temporal scales – space weather challenges our understanding of the heliophysics system. • Empirical, first-principles, handwaving etc. approaches all being used – the nature of the approach does not matter as long as it works. • It is not all about predictions: In some applications general characterization of extreme environments is currently of greater interest. 16

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Awarded May 23 rd