Abstract
The International Reference Ionosphere (IRI) model is a widely used empirical model to describe ionospheric climatology. However, IRI represents the monthly averages of the ionospheric parameters, which makes it difficult to capture the local and short-term ionospheric variations. To overcome this limitation, we propose a data ingestion method using a combination of ground-based and space-borne observations. The ionospheric parameters from ground-based Global Navigation Satellite System (GNSS), ionosondes, space-borne GNSS radio occultation and satellite altimetry observations are ingested into the IRI-2020 model to improve its accuracy. The outputs of the ingested IRI (IRIinge) are assessed by case study and statistical analysis, with reference to independent ionosonde observations and global ionospheric maps. The case study shows that IRIinge expresses the diurnal and local variations of the ionosphere better than the standard IRI (IRIstan) in both high and low solar activity periods. The relative error of ionospheric electron density profiles from IRIinge is generally less than 10%, and the vertical total electron content from IRIinge has an accuracy improvement of 39.0% compared to that from IRIstan. The statistical analysis shows that IRIinge performs more stable than IRIstan, and its output generally has smaller REs and root-mean-square errors, especially in daytime and storm time. The proposed method significantly improves IRI-2020 on the accuracy of the output parameters and the ability to present the short-term variations of the ionosphere.
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Data availability
The COSMIC and COSMIC-2 RO data are available from the COSMIC Data Analysis and Archive Center (CDAAC) (https://cdaac-www.cosmic.ucar.edu). The ionosonde data are from NGDC (ftp://ftp.ngdc.noaa.gov/ionosonde/data/). The GNSS data are from the Crustal Dynamics Data Information System (CDDIS) (https://cddis.nasa.gov/archive/gnss/data/). The satellite altimetry data are from the National Centers for Environmental Information (NCEI) (https://www.ncei.noaa.gov/products/jason-satellite-products). The IRI-2020 source code is downloaded at http://irimodel.org/.
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Acknowledgements
This research is funded by the National Natural Science Foundation of China (Nos. 42074027, 42174017, 41774033, and 41774032).
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TH and XX designed the research. TH performed the experiments. TH, XX and JL analyzed the data. TH and XX wrote the draft of the manuscript. All authors reviewed and approved the final manuscript.
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Hu, T., Xu, X. & Luo, J. Multi-source data ingestion for IRI-2020 model: a combination of ground-based and space-borne observations. GPS Solut 28, 78 (2024). https://doi.org/10.1007/s10291-024-01620-y
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DOI: https://doi.org/10.1007/s10291-024-01620-y