7th Asia-Pacific Conference on Plasma Physics, 12-17 Nov, 2023 at Port Messe Nagoya

Relative Factors of Ionospheric Plasma Irregularities Corresponding toDifferent Spread F Types over Hainan

Zheng Wang1,3*, Jinhui Cai2, Meiyi Zhan2, Pengdong Gao2*, Guojun Wang1,3, Jiankui Shi1,3,4, Xiao
Wang1,3,Chu Qiu2, Quan Qi2
1
State Key Laboratory of Space Weather, National Space Science Center, Chinese Academy ofSciences, Beijing, China
2State Key Laboratory of Media Convergence and Communication, Communication University ofChina, Beijing, China.
3Hainan National Field Science Observation and Research Observatory for Space Weather.
4University of Chinese Academy of Sciences, Chinae-mail (speaker): zwang@swl.ac.cn

The ionosonde is an ionospheric radar that uses
high-frequency (HF) radio waves to remotely sense theionosphere. It measures the local ionospheric plasma
density profile by sending radio wave pulses of different
frequencies (several MHz) and receiving the echoes. As
radio waves of a specific frequency are reflected from acertain height in the ionosphere with a particular plasma
density, these echoes create smooth, continuous tracesthat correspond to the plasma density profile of theionospheric E, F1, and F2 regions.
The radio wave echoes by Ionospheric plasma
disturbances/irregularities could show theircharacteristics in the plasma processes, corresponding todifferent Spread F (SF) types such as Frequency SF(FSF), Range SF (RSF), Branch SF (BSF), Mixed SF(MSF), Strong Range SF (SSF), and so on [1,2].
In the frequency-height figure called “ionogram”
obtained by the ionosonde, the image features areassociated with ionospheric features, corresponding todifferent physical mechanisms. As a result, researchers
always conduct statistical studies on the different SFtypes. However, based on studies in the past, it was clearthat the occurrences of different SF types had different
variation features with local time, season, solar/magnetic
activities, and other factors. By now, we don’t have awell-accepted standard to describe the SF amplitude anddetailed information. Recent advancements in machinelearning have enabled the automatic identification ofcertain ionogram features [3].
In our work, we manually classified the SF types overHainan (19.5°N, 109.1°E) from 2002-2016, building a
new image set of 469,227 human-recorded ionograms
including 38,226 Spread-F samples from one station overone solar cycle. On this basis we made a classification
model. To validate our assumption, some models, such as
VGG, ResNet, EfficientNet, and ViT, were utilized to
execute classification on a picked image dataset contains
20,000 samples from each SF types and no SF(total
100,000 samples). In the dataset we random split 80%
for training, 15% for validation and the rest 5% for test.
After training, we found ResNet, EffientNet, ConvNet

and MobileNet all have good performance (accuracy
higher than 90%).
The classification model helps us to extract the features.
Then we made a short-impending prediction model for
the SF, and extracted parameters for the different types inthe figure. Our ionogram generation method is proposedby combining a spatio-temporal ConvGRU model with asuper-resolution EDSR model, based on image feature
only. We use our classification model to check thepredicted ionograms, such as with/without SF, SFfeatures, and the ionospheric trace for backgroundplasma density. We find the average accuracy forwith/without SF (corresponding to plasma irregularities)
is also over 90%
To learn more about the forming and developing
processes of Ionospheric plasma irregularities, we alsostudied the relative factors such as the variations of
Ionosphere bottom height as well as solar/magnetic
activities with the data from 2002-2019. However, they
don’t show simple correlation, and we believe there are
deep relationships inside our prediction processes.
References[1] Piggott, W. R., & K. Rawer. (1972). URSI handbookof ionogram interpretation and reduction, Report
UAG-23, 68-73, World Data Center A for Solar—
Terrestrial Physics.
https://www.researchgate.net/publication/252233278_U
RSI_Handbook_of_Ionogram_Interpretation_and_Reduction[2] Booker, H. G., & Wells, H. W.. (1938). Scattering ofradio waves in the F region of the ionosphere. Journal ofGeophysical Research Atmospheres,43(3):249-256. doi:
10.1029/TE043i003p00249.
[3] Rao, Venkateswara, T., Sridhar, M., & Venkata
Ratnam, D.. (2022). Auto-detection of sporadic E andSpread-F events from the digital ionograms. Advances inSpace Research, 70, 1142-1152.doi:
10.1016/j.asr.2022.05.046.