We report our analysis of multi-diagnostic ionospheric observations over Indonesia following the 15 January 2022 Tonga volcano eruption. Observation data from the Indonesian GNSS CORS network, ionosondes, and GISTM receivers, in conjunction with the Himawari-8 satellite imagery, were used in the analysis. The Lamb waves from the eruption, traveling at ~310 m/s, reached eastern part of Indonesia (~5,000 km from Tonga) approximately 4 hours after the eruption. The Lamb waves traversed the Indonesian region for 4 hours and 40 minutes, around sunset period. As a result, some unseasonal equatorial plasma bubbles (EPBs) occurred over this longitude sector, with an earlier-than-usual onset time. There was a directional split in the zonal drift velocity of these EPBs, where some EPBs drifted eastward with a velocity of 138.0 ± 6.9 m/s and others westward with a velocity of 39.6 ± 2.0 m/s. At the same time, traveling ionospheric disturbances (TIDs) from the Tonga eruption also propagated over the Indonesian region with a velocity of 434.6 ± 21.7 m/s. In the total electron content (TEC) data, interactions between EPBs and TIDs were observed over the region. There were enhancements in the rate-of-TEC index (ROTI) and S4 scintillation index, indicating the presence of ionospheric density irregularities. A turbulent ionospheric F-layer, due to these EPBs and TIDs, caused either spread-F echoes or a loss of F-region traces in the ionograms. An intensification of sporadic-E layer, lasting for a few hours, was also observed in the ionograms.

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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.

We report the results of our investigations on ionospheric effects potentially caused by the 15 February 2013 Chelyabinsk meteor explosion. We used the observation data from a number of digisonde stations located in Europe and Russia to detect the traveling ionospheric disturbances (TIDs) likely to have been caused by the meteor explosion. We found that certain characteristic signatures of the TIDs can be identified in individual ionogram records, mostly in the form of Y‐forking/splitting of the ionogram traces. Based on the arrival times of the disturbances, we have inferred the overall propagation speed of the TIDs from Chelyabinsk to be 171 ± 14 m/s. In addition to the natural fulfillment of scientific endeavors, this work also highlights the importance of maintaining the mastery of ionosondes as ionospheric diagnostic instruments (in terms of operation, data analysis, representation, and interpretation) for many generations of space researchers to come.

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.

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.