# Retrospective Study on Seismic Ionospheric Anomalies Based on Five-Year Observations from CSES
## 4 Conclusions and Discussion
For the plasma parameters, the vertical ExB drift velocity will also push the anomalies westward due to the overall addition of spatial and seismogenic electric fields in the western area of the conductivity anomaly. For the electromagnetic emissions, the propagation direction is impacted by the variations in conductivity and the ambient magnetic field [16; 17; 44]. These features indicate that different physical parameters with different propagation mechanisms may cause anomalies to appear in different directions. Thus, it is challenging for this retrospective study to compare multiple types of physical parameters in a standard way. It should be noted that the anomaly recognition rates of all payloads in this work are preliminary. There are several reasons leading to the different recognition rates of the payloads. Firstly, the sample size of EQs for each payload is different and also not sufficient to make a quantitative analysis. Secondly, the working performance of payloads varies. For example, the inherent issues within the PAP itself result in inaccurate observations and, thus, a low anomaly recognition rate. Thirdly, due to the different working principles (or working mechanisms) of payloads for detecting different physical values, we need to adopt different data processing methods for different types of physical values to extract abnormal signals. This leads to differences in the anomaly extraction results. Lastly, the correlation between different parameters and earthquakes varies. For example, some parameters are more easily affected by seismogenic processes. As our knowledge accumulates through increasing observations and experience in the future, it is anticipated that data processing methods will evolve, and the accuracy and objectivity of the anomaly detection rates from the payloads will improve. Although CSES-01 can record abundant ionospheric anomalies during strong and shallow EQs, and the common characteristics of these anomalies have been summarized and confirmed by numerous previous studies [3; 8; 19; 45], there is still a significant gap between seismic monitoring and effective forecasting at present due to high rates of missed detections and false alarms. This situation can be attributed to several reasons. (1) The lack of high-resolution observation capability  
The recursive period of CSES-01 is 5 days, meaning it passes over a fixed location once every 5 days, and the minimum distance between two orbits is approximately 5\({}^{\circ}\) in the longitude direction (i.e., 500 km at the equator). However, both the time resolution of 5 days and the spatial resolution of 5\({}^{\circ}\) are inadequate for capturing all the seismic anomalies in the ionosphere. There is a possibility that the satellite may completely miss flying over the areas where seismic anomalies appear, or just fly past the edge of the disturbance area [23]. After the launch of CSES-02 in the future, which will operate concurrently with CSES-01,the revisiting orbit period will be reduced to 2-3 days, which effectively improves the spatiotemporal resolution of CSES observations. However, it is still not enough for the accurate real-time acquisition of seismic anomaly signals. Therefore, by combining the long-term observation data from the ground-based equipment and similar satellites in orbit at the same time, stereoscopic observation could be constructed, and more data from the lithosphere-atmosphere-ionosphere can be obtained to compensate for the lack of spatial and temporal resolution of existing satellite observations. 2. The challenges due to the different data processing methods lead to different anomaly extraction criteria  
As mentioned in Section 2, we employed efficient processing methods according to the data characteristics for different parameters; therefore, the magnitude of abnormal changes in different parameters may vary significantly, making it challenging to establish a quantitative relationship among different parameters.