Precipitable water vapor (PWV) retrieved from Global Navigation Satellite System (GNSS) has been successfully applied in rainfall forecast. This paper shifts to a new focus, aiming at nowcasting lightning, for its relatively scant GNSS-PWV investigation. Unlike previous studies that mainly explored statistical correlations between PWV and lightning, this approach integrates GNSS-PWV and other meteorological parameters with advanced automated machine-learning algorithms to accurately predict lightning occurrences with a lead time up to 30 minutes. In this paper, the relationship between lightning occurrences and PWV variations is firstly examined through comprehensive statistical analysis. Next, a machine-learning-based lightning nowcasting model is established in this study, with the input of GNSS-PWV and common meteorological parameters. The training and test datasets are sampled every 10 minutes from 7 collocated GNSS stations and automatic weather stations (AWS) along with the lightning location information during the period from 2018 to 2022 in Hong Kong. A comprehensive evaluation is conducted on the performance of the lightning nowcasting model. Results indicate that the proposed model possesses convincingly more excellent performances over four evaluation metrics: Probability of Detection (POD, 89%), False Alarm Ratio (FAR, 30%), Threat Score (TS, 0.64), and Heidke Skill Score (HSS, 0.77). It is also revealed that this model achieves impressive innovativeness, performance and competitive advantages, compared with other existing methods and previous lightning forecast models.

Spread-F (SF) is one of the most important types of the ionospheric irregularities as it causes ionospheric scintillation which can severely affect the performance and reliability of communication, navigation, and radar systems. The ionosonde provides the most effective and economical way to study the ionosphere and SF. However, the manual identification of SF from an ionogram is boring and hard work. To automatically identify SF on the ionogram and extend the study of SF into the middle and low latitudes of East Asia, this paper presents a statistical analysis of SF in this region, based on the naïve Bayesian classifier. The results showed that the accuracy of automatic identification reached up to 97% on both the validation datasets and test datasets composed of Mohe, I-Cheon, Jeju, Wuhan, and Sanya ionograms, suggesting that it is a promising way to automatically identify SF on ionograms. Based on the classification results, the statistical analysis shows that SF has a complicated morphology in the middle and low latitudes of East Asia. Specifically, there is a peak of occurrence of SF in the summer in I-Cheon, Jeju, Sanya, and Wuhan; however, the Mohe station has the highest occurrence rate of SF in December. The different seasonal variations of SF might be due to the different geographic local conditions, such as the inland-coastal differences and formation mechanism differences at these latitudes. Moreover, SF occurs more easily in the post-midnight hours when compared with the pre-midnight period in these stations, which is consistent with the previous results. Furthermore, this paper extracts the frequency SF (FSF) index and range SF (RSF) index to characterize the features of SF. The results shows that the most intense FSF/RSF appeared in the height range of 220–300 km/1–7 MHz in these stations, although there are different magnitude extensions on different season in these regions. In particular, strong spread-F (SSF) reached its maximum at the equinox at Sanya, confirming the frequent SSF occurrence at the equinox at the equator and low latitudes. These results would be helpful for understanding the characteristics of SF in East Asia.

Parametric decay instability (PDI) is an important process in ionospheric heating. This paper focuses on the frequency and wavevector matching condition in the initial PDI process, the subsequent cascade stage, and the generation of strong Langmuir turbulence. A more general numerical model is established based on Maxwell equations and plasma dynamic equations by coupling high-frequency electromagnetic waves to low-frequency waves via ponderomotive force. The primary PDI, cascade process, and strong Langmuir turbulence are excited in the simulation. The matching condition in the initial PDI stage and cascade process is verified. The result indicates that the cascade ion acoustic wave may induce or accelerate the formation of cavitons and lead to the wavenumber spectrum being more enhanced at 2k_L (where k_L is the primary Langmuir wavenumber). The wavenumber spectra develop from discrete to continuous spectra, which is attributed to the caviton collapse and strong Langmuir turbulence.

Plasma lines (PLs) observed by the European Incoherent Scatter Scientific Association (EISCAT) incoherent scatter radar (ISR) data set during the recent solar maximum 2014 are investigated. To statistically analyze the PLs in the EISCAT data set, we develop an algorithm for the automatic extraction of naturally enhanced PLs from the ISR spectra. Two typical types of auroral‐enhanced PLs at high latitude, namely, secondary electron and electron beam, are analyzed based on this PL extraction algorithm. Our results show that PL enhancements originating from secondary electrons and electron beams present evident magnetic local time dependence. Moreover, the statistical study indicates that the PL enhancements are correlated with the geomagnetic and solar activities. These results can provide insights into solar wind‐magnetosphere‐ionosphere coupling mechanisms.

This paper is a response to the comment by Blagoveshchenskaya et al. (2017, https://doi.org/10.1002/2017JA023880) on the paper by Wang et al. (2016, https://doi.org/10.1002/2016JA023070). Although we disagree with Blagoveshchenskaya et al. (2017), nevertheless, we do thank them for their comments, since they have given us the opportunity to clarify some points.