Regional ionospheric parameter estimation by assimilating the LSTM
trained results into the SAMI2 model
Abstract
This paper presents a study on the possibility of predicting the
regional ionosphere at mid-latitude by assimilating the predicted
ionospheric parameters from a neural network (NN) model into the SAMI2
model. The NN model was constructed from the dataset of Jeju ionosonde
(33.43˚N, 126.30˚E) for the period of 1 Jan 2011 to 31 Dec 2015 by using
the long-short term memory (LSTM) algorithm. The NN model provides
24-hour prediction of the peak density (NmF2) and peak height (hmF2) of
the F2 layer over Jeju. The predicted NmF2 and hmF2 were used to compute
two ionospheric drivers (total ion density and effective neutral
meridional wind), which were assimilated into the SAMI2 model. The
SAMI2-LSTM model estimates the ionospheric conditions over the
mid-latitude region around Jeju on the same geomagnetic meridional
plane. We evaluate the performance of the SAMI2-LSTM by comparing
predicted NmF2 and hmF2 values with measured values during the
geomagnetic quiet and storm periods. The root mean square error values
of NmF2 (hmF2) from Jeju ionosonde measurements are lower by 31%, 22%,
and 29% (35%, 24%, and 17%) than those of the SAMI2, TIEGCM, and
IRI-2016 models during the geomagnetic quiet periods. However, during
the geomagnetic storm periods, the performance of SAMI2-LSTM, like all
other models, is significantly worse than during the quiet periods. We
discuss the advantage and possible improving strategy on the
predictability of the regional mid-latitude ionosphere by utilizing
data-assimilated physics models and deep-learning techniques together.