A New Equatorial Plasma Bubble Prediction Capability

Transcription

1 A New Equatorial Plasma Bubble Prediction Capability Brett A. Carter Institute for Scientific Research, Boston College, USA, RMIT University, Australia,

2 Executive Summary: Study 1 Increased Kp Intensified plasma convection at high latitudes a) n e Increased Joule heating Thermospheric wind perturbations propagate towards equator Decrease in zonal wind at equator Decrease in upward plasma drift (Vp) Decreased R-T growth rate (no EPBs or scintillation) New ability to accurately forecast post-sunset EPB occurrence!! 2

3 Outline Introduction to Equatorial Plasma Bubbles (EPBs): What are they, and why are they important? EPB occurrence: Climatology and daily variability Global ionosphere-thermosphere modeling and EPB observations Analysis of GPS data from: Southeast Asia American, African, Asian and Western Pacific EPB prediction trial Unanswered questions Summary and conclusions Study 2: Geomagnetically induced currents at the equator 3

4 Equatorial Plasma Bubbles Ground-based radar measurements of EPBs All-sky cameras and numerical modelling Kelley et al. (2006) Generalised Rayleigh-Taylor instability: Gravity (Gentile et al., 2006) Upward plasma drift - prereversal enhancement after sunset Retterer [2008a,b] 4

5 Day-to-day EPB variability Abdu Retterer al. et (2009) al., 2006 AGU The strength of the pre-reversal enhancement has been a good candidate for the daily EPB variability: Highly variable during quiet times Strongly influenced by changes in storm and substorm activity (via undershielding and over-shielding electric fields) Has been shown as a good parameter to feed into EPB prediction models (e.g. Retterer) Scherliess and Fejer (1999) 5

6 Vanimo Ionospheric Scintillation Observations Vanimo: Burke et al. (2004) Ionosonde vertical incidence sounding radar GPS Ionospheric Scintillation Monitor (ISM) ISM data: Amplitude scintillation index, S4 α B S I I ) / ( I 2 (the square root of the normalised variance of the signal Day intensity, I, over 1 Night minute) α Occurrence of post-sunset (after ~10 UT) scintillation events is mostly largest during equinox and smallest during solstices. 6

7 GPS scintillation observations ISM Carter et al., 2014 [JGR] Ionosphere - thermosphere observations along the entire flux tube, as required by the Rayleigh-Taylor linear instability growth rate expression, are not possible/feasible (e.g. Gentile et al., 2006) Unknown Directly Measured/known Pederson conductivities Upward neutral wind Gradient scale length Upward plasma drift Gravity Ion-neutral collision frequency Recombination rate Therefore, some form of ionosphere-thermosphere modelling is required 7

8 TIEGCM The Thermosphere Ionosphere Electrodynamics General Circulation Model (TIEGCM) is a timedependent 3D physics-based (i.e. not empirical) numerical simulation of the Earth s thermosphere and ionosphere. Inputs: Solar activity (F10.7 cm flux) Geomagnetic activity (Kp index) Outputs: Electron density F layer height 3D plasma drift Thermospheric density 3D neutral winds Basically, everything that we need 8

9 GPS observations versus TIEGCM The TIEGCM was used to calculate the R-T growth rate every 15 mins The R-T growth rate (calculated purely from TIEGCM output) is 1σ higher on EPBs compared to non- EPB days A breakdown of the terms gives a clear indication which parameters are exhibiting the observed daily variability in the GPS data The upward drift term is causing higher growth rates on EPB days Carter et al., 2014a [JGR] 9

10 Modelled TIEGCM: EPB variability Daily maximum average S4 shows good correlation with TIEGCM growth rate EPB modelling vs observations: Observed EPBs Yes No Yes 17 3 No 5 31 Heidke skill score = Accuracy (17+31)/56 = 85.7% TIEGCM runs that varied Kp closely followed the observed daily variability Kp is dominant source of TIEGCM variability during quiet period Carter et al., 2014a [JGR] 10

11 TIEGCM: EPB variability Taking a closer look into the TIEGCM outputs: Increases in Kp coincide with increases (decreases) in the thermospheric temperature (upward plasma drift) This implies that perturbations in the F-region dynamo are causing the quiet-time variability, and not storm-associated penetration electric fields Carter et al., 2014a [JGR] 11

12 TIEGCM: Other longitudes The analysis was repeated for stations in the SCINDA network located different longitude sectors in 2011 Days that exhibited a drop in the TIEGCM R-T growth rate corresponded well to a lack of scintillation observations Once again, it is clear that periods of increased (and not necessarily high) Kp corresponded to a lack of scintillation Carter et al., 2014b [GRL] 12

13 COPEX: high and low latitude coupling COPEX observations were used to investigate timing of high-latitude and lowlatitude coupling a) n e (b) log e (N 2 ) Best anti-correlation observed between PRE and Kp from ~3.5 hours prior TIEGCM modelling independently reproduces this result Zonal neutral wind also shows strong anti-correlation with offset Kp Carter et al., 2014b [GRL] Heelis et al. (2012) Results hold for 2011 SCINDA data 13

14 Physical process: EPB suppression Increased Kp Intensified plasma convection at high latitudes a) n e Increased Joule heating Thermospheric wind perturbations propagate towards equator Decrease in zonal wind at equator Decrease in upward plasma drift (Vp) Decreased R-T growth rate (no EPBs or scintillation) 14

15 EPB Prediction Analysis has shown that the most important source of variability in the TIEGCM originates from the Kp index (geomagnetic activity) a) n e NASA (b) log e (N 2 ) NOAA Space Weather Prediction Center Can we reliably predict Kp? Yes, the ACE and WIND spacecraft are routinely used by the USAF to predict Kp with rather good accuracy. Are these predictions good enough? 15

16 Scintillation prediction trial: Mar-Jul hour Wing Kp predictions: TIEGCM generally performs best during peak EPB season, closely followed by WBMOD (up to 95% for KIS) During transition and off-peak seasons, either WBMOD or persistence forecast performs best Carter et al., 2014c [GRL] 16

17 Scintillation prediction trial: Mar-Jul hour Wing Kp predictions: Ranking of models is only slightly unchanged Using 4-hour predictions doesn t result in significant decrease in accuracy This is due to several hours delay between highlatitude changes (by Kp) and their effects at the equator via thermosphere wind perturbations Carter et al., 2014c [GRL] 17

18 Unresolved issues - DRUIDAE Detrimental, Rapid and Un-seasonal Ionospheric Disturbances Around the Equator ( DRUIDAE Latin for Druids ) This DRUIDAE event was observed across Asia Kototabang TIEGCM did not pick up increase in growth conditions No geomagnetic storms (no prompt penetration electric fields) Coupling with lower altitudes? Tides? AGWs?... 18

19 Summary and conclusions Equatorial Plasma Bubbles are known to adversely impact radio applications: Global Navigation Satellite Systems (GNSS), such as GPS, which is heavily relied upon by numerous industries, represent a key economic vulnerability UHF satellite communications are also vulnerable to EPBs Global observations of Equatorial Plasma Bubbles: Complicated daily variability in plasma bubbles/radio scintillation occurrence observed using ground-based GPS receivers TIEGCM exhibited daily variability in R-T growth rate that closely matched observations High-latitude convection found to control zonal neutral wind at the equator, and subsequently, the upward plasma drift speed, which influences R-T growth conditions EPB Prediction trial using Wing Kp predictions to drive TIEGCM was successful: During peak EPB periods, EPB suppression days were accurately forecast with overall EPB occurrence prediction accuracy of up to 95% TIEGCM and WBMOD failed to predict DRUIDAE (EPBs during off-peak season) It is suspected that lower atmospheric effects could be a prime driver of DRUIDAE (TIEGCM doesn t include effects from troposphere altitudes) 19