Brett Anthony Carter

and 12 more

The Hunga Tonga Volcano eruption launched a myriad of atmospheric waves that have been observed to travel around the world several times. These waves generated Traveling Ionospheric Disturbances (TIDs) in the ionosphere, which are known to adversely impact radio applications such as Global Navigation Satellite Systems (GNSS). One such GNSS application is Precise Point Positioning (PPP), which can achieve cm-level accuracy using a single receiver, following a typical convergence time of 30 mins to 1 hour. A network of ionosondes located throughout the Australian region were used in combination with GNSS receivers to explore the impacts of the Hunga-Tonga Volcano eruption on the ionosphere and what subsequent impacts they had on PPP. It is shown that PPP accuracy was not significantly impacted by the arrival of the TIDs and Spread-F, provided that PPP convergence had already been achieved. However, when the PPP algorithm was initiated from a cold start either shortly before or after the TID arrivals, the convergence times were significantly longer. GNSS stations in northeastern Australia experienced increases in convergence time of more than 5 hours. Further analysis reveals increased convergence times to be caused by a super equatorial plasma bubble (EPB), the largest observed over Australia to date. The EPB structure was found to be ~42 TECU deep and ~300 km across, traveling eastwards at 30 m/s. The Hunga Tonga Volcano eruption serves as an excellent example of how ionospheric variability can impact real-world applications and the challenges associated with modeling the ionosphere to support GNSS.
This paper presents a statistical analysis to investigate the day-to-day variability of field-aligned irregularities (FAI) occurrence in nighttime F-region ionosphere over the Equatorial Atmosphere Radar (EAR), West Sumatra, Indonesia. FAI echoes were identified based on signal intensity of backscatter radar observations. We analyzed nighttime F-region FAI during 3 years starting in January 2011 to December 2013. For the first time, a combinatorics analysis was applied to examine the statistical likelihood of various day-to-day FAI occurrence patterns. The empirical day-to-day combinatorics analysis was performed based on binary classification of EAR observation data into either FAI occurrence (+) or absence (-) for each calendar date. Permutations of various day-to-day occurrence patterns, from 1-day to 6-day patterns, were sorted into histograms. The combinatorics analysis was performed in 4 separate time intervals to account for seasonal variation: two equinoxes (March and September) and two solstices (June and December). EAR data show that FAI occurrence probability is maximum for the two equinoxes, and that it is minimum for the two solstices. Our analysis shows that certain day-to-day patterns are more likely to occur than others, and such “combinatorics fingerprints” depend on season. During the solstices, persistent absence of FAI over several consecutive days far outweighed persistent FAI occurrence over an equivalent grouping of days with the same length. Meanwhile, during the equinoxes, we found a generally more equitable distribution between persistent day-to-day FAI occurrence and persistent day-to-day FAI absence. These findings may open new ways to help forecast FAI occurrence on a regional basis.

Rezy Pradipta

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We report our cross-validation of equatorial plasma bubble (EPB) observations based on 2-D ΔTEC data maps over the South American sector, against equatorial spread-F (ESF) observations based on digisonde measurements at several locations. The 2-D ΔTEC data maps were derived using a GPS TEC data detrending procedure [Pradipta et al., 2015] that is inherently capable of distinguishing between wavelike fluctuations associated with traveling ionospheric disturbances (TIDs) and deep depletions associated with EPBs. The data detrending was performed for TEC signals along individual ionospheric piercing point (IPP) trajectories from individual stations, before spatially interpolating the ΔTEC values into a fine 0.2 deg x 0.2 deg geographic latitude/longitude grid. We validated the EPB/depletion observations from these 2-D ΔTEC data maps against digisonde observations of ESF occurrences at Jicamarca (JI91J), Cachoeira Paulista (CAJ2M), and Fortaleza (FZA0M) using data recorded in 2011. A general agreement was found between the EPB and ESF occurrences. Over Jicamarca: 55.1% fall within the EPB=YES & ESF=YES category, 20.6% fall within the EPB=NO & EPB=NO category, 24.4% fall within the EPB=NO & ESF=YES category, and 0% fall within the EPB=YES & ESF=NO category. Over Cachoeira Paulista: 48.5% fall within the EPB=YES & ESF=YES category, 37.4% fall within the EPB=NO & EPB=NO category, 13.2% fall within the EPB=NO & ESF=YES category, and 0.8% fall within the EPB=YES & ESF=NO category. Over Fortaleza: 68.8% fall within the EPB=YES & ESF=YES category, 10.4% fall within the EPB=NO & EPB=NO category, 20.2% fall within the EPB=NO & ESF=YES category, and 0.6% fall within the EPB=YES & ESF=NO category. The classification process of EPB/ESF occurrences (+’s) and no-occurrences (-’s) in this validation work also points at the possibility of performing combinatoric pattern analyses on EPB/ESF occurrence likelihood. This type of analysis may be useful in assessing the fundamental limit of EPB/ESF occurrence predictability that can be theoretically achieved.