A MLP neural network as an investigator of TEC time series to detect seismo-ionospheric anomalies

Anomaly detection is extremely important for earthquake parameters estimation. In this paper, an application of Artificial Neural Networks (ANNs) in the earthquake precursor’s domain has been developed. This study is concerned with investigating the Total Electron Content (TEC) time series by using a Multi-Layer Perceptron (MLP) neural network to detect seismo-ionospheric anomalous variations induced by the powerful Tohoku earthquake of March 11, 2011.     

Spatial distribution of TEC across India in 2005: Seasonal asymmetries and IRI prediction

Total electron content measured simultaneously at 10 locations over India during the low solar activity year 2005 is used to examine the temporal and spatial asymmetries and also to assess the predictability of the International Reference Ionosphere in respect of the observed asymmetrical distribution. The stations are distributed in latitude along 77°E and in longitude along 23°N forming a meridional and a zonal chain respectively. A longitudinal gradient positive towards east was observed in the daytime hours of equinox and summer. Equinoctial asymmetry was prevalent across India during this year. Within the crest and equator, winter anomaly has been observed. It is found that IRI 2012 (with Ne Quick option, URSI coefficients) is unable to fully capture the temporal variation and spatial gradients of the ionization density in the Indian sector during 2005. The amount of offset between the model and measurement varies with local time and location.     

Earthquake induced dynamics at the ionosphere in presence of magnetic storm

The modifications induced in the dynamics of the ionosphere by the major Japan earthquake (EQ) of March 11, 2011 (epicenter at 38.322°N, 142.369°E, M = 8.9) in presence of a magnetic storm are examined by mapping latitudinal variations of F-layer ionization density (NmF2) from 22 stations covering the epicenter zone. The changes forced into the Total Electron Content (TEC) by the major EQ in the magnetic storm ambiance are also examined from the GPS data collected at Guwahati (26° 10′ N, 91° 45’ E), situated in the major fault system of East Asia. The contributions of pre-seismic electric field as well as of magnetic storm time electric field in the observed density variations are brought into the ambit of discussion. The influence of lower atmosphere in shaping TEC features during the study case is highlighted. The effects of solar activity on density variations during such complex ambiances are also addressed.     

On the uncertainties in the measurement of absolute (true) TEC over Indian equatorial and low latitude sectors

The Indian sector encompasses the equatorial and low latitude regions where the ionosphere is highly dynamic and is characterized by the equatorial ionization anomaly (EIA) resulting in large latitudinal electron density gradients causing errors and uncertainties in the estimation of range delays in satellite based navigation systems. The diurnal and seasonal variations of standard deviations in the TEC data measured during the low sunspot period 2004–2005 at 10 different Indian stations located from equator to the anomaly crest region and beyond are examined and presented. The day-to-day variability in TEC is found to be lowest at the equatorial station and increases with latitude up to the crest region of EIA and decreases beyond.     

POBI interpolation algorithm for CYGNSS near real time flood detection research: A case study of extreme precipitation events in Henan,China in 2021

In late 2016, NASA launched the first constellation of the global navigation satellite system reflectometry (GNSS-R) small satellites called the Cyclone Global Navigation Satellite System (CYGNSS). The stable data quality and continuous free availability of CYGNSS scientific data provided a new method for flood monitoring. However, owing to the pseudorandom distribution of CYGNSS data, researchers must always choose between high temporal resolution and high spatial resolution during the performance of flood monitoring based on CYGNSS data. For floods caused by extreme precipitation with sudden and short durations, the current flood mapping based on CYGNSS data cannot be updated in near real time. However, the near real time update of the flood distribution range is meaningful for postdisaster emergency response and rapid rescue. This study aimed to address this problem using a newly proposed spatial interpolation method based on previously observed behaviour (POBI). First, a method for calculating the surface reflectivity of the CYGNSS was introduced, followed by the principle of the POBI spatial interpolation method. The applicability of the POBI method in Henan Province, China, was then analysed, and by using the flood in Henan Province, China, in July 2021 as an example, the feasibility of CYGNSS near real time flood mapping based on the POBI method was evaluated. Based on the results, near real time and 3 km flood distribution monitoring results can be obtained using the proposed new method. The results were evaluated using MODIS (Moderate Resolution Imaging Spectroradiometer) images and compared with the observations of SMAP (Soil Moisture Active Passive) and GPM (Global Precipitation Measurement) in the same period. The results show that the flooded areas obtained by CYGNSS correspond to the inundated areas in MODIS images and are also in high agreement with the SMAP. In addition, CYGNSS allows for finer mapping and quantification of inundation areas and flood duration. Moreover, we also discussed the potential of CYGNSS to detect floods in shorter periods of time (a few hours) and did a preliminary evaluation using precipitation data from meteorological stations. The results are also highly consistent.