Near real-time LOD prediction using ConvLSTM model through integrating IGS rapid LOD and effective angular momentum
Length of day (LOD), a critical component of Earth orientation parameters (EOP), represents variations in Earth’s rotation rate. It is very difficult to predict accurately due to the effects of atmosphere, ocean, hydrology, the Earth’s internal interactions and so on. The international Earth rotatio...
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| Format: | Article |
| Language: | English |
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Taylor & Francis Group
2025-03-01
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| Series: | Geo-spatial Information Science |
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| Online Access: | https://www.tandfonline.com/doi/10.1080/10095020.2025.2471432 |
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| author | Kehao Yu Xiaoya Wang Zhao Li Jian Wang Lihua Li Weiping Jiang Kai Yang Jingwen Long Yunzhao Li |
| author_facet | Kehao Yu Xiaoya Wang Zhao Li Jian Wang Lihua Li Weiping Jiang Kai Yang Jingwen Long Yunzhao Li |
| author_sort | Kehao Yu |
| collection | DOAJ |
| description | Length of day (LOD), a critical component of Earth orientation parameters (EOP), represents variations in Earth’s rotation rate. It is very difficult to predict accurately due to the effects of atmosphere, ocean, hydrology, the Earth’s internal interactions and so on. The international Earth rotation and reference systems service (IERS) EOP C04 series, derived from four space geodetic observations, could offer high accuracy and smooth EOP product. However, this product typically has a latency of about 30 days. It is not adequate for fields requiring strict real-time data processing and applications, such as precise tracking and navigation of interplanetary spacecraft, global navigation satellite system (GNSS) meteorology, real-time precision orbit determination of artificial satellites, real-time kinematic (RTK) positioning and so on. To address the aforementioned issues, we propose an algorithm for predicting LOD that adopts a convolutional long-short-term memory (ConvLSTM) method with different base sequence lengths based on the LOD series from the IERS EOP C04, effective angular momentum (EAM) datasets and GNSS near-real-time (NRT) LOD data from the International GNSS Services (IGS) Rapid Products. Compared to the most accurate models used by participants in the Second Earth Orientation Parameters Prediction Comparison Campaign (2nd EOP PCC), when GNSS NRT data is not used, the proposed model improves LOD ultra-short-term (1–10 days) prediction accuracy by 29.72% and medium- to long-term (60–360 days) prediction accuracy by 11.86%. After incorporating GNSS NRT data, the short-term (10–30 days) LOD prediction accuracy improves by 55.07%. It is shown that the ConvLSTM model, integrated with GNSS NRT data and EAM datasets, could significantly enhance the forecast accuracy of LOD across various time spans. This advancement enriches the Earth’s rotation prediction models and holds potential benefits for real time applications such as real-time satellite orbit determination, extreme weather analysis, RTK technology and so on. |
| format | Article |
| id | doaj-art-e02e313143d44e5281a7f6cc5c6e3615 |
| institution | DOAJ |
| issn | 1009-5020 1993-5153 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Geo-spatial Information Science |
| spelling | doaj-art-e02e313143d44e5281a7f6cc5c6e36152025-08-20T03:12:19ZengTaylor & Francis GroupGeo-spatial Information Science1009-50201993-51532025-03-0111510.1080/10095020.2025.2471432Near real-time LOD prediction using ConvLSTM model through integrating IGS rapid LOD and effective angular momentumKehao Yu0Xiaoya Wang1Zhao Li2Jian Wang3Lihua Li4Weiping Jiang5Kai Yang6Jingwen Long7Yunzhao Li8GNSS Research Center, Hubei Luojia Laboratory, Wuhan University, Wuhan, ChinaShanghai Astronomical Observatory, Chinese Academy of Sciences, Shanghai, ChinaGNSS Research Center, Hubei Luojia Laboratory, Wuhan University, Wuhan, ChinaGNSS Research Center, Hubei Luojia Laboratory, Wuhan University, Wuhan, ChinaSchool of Land Science and Technology, China University of Geosciences (Beijing), Beijing, ChinaGNSS Research Center, Hubei Luojia Laboratory, Wuhan University, Wuhan, ChinaSchool of Land Science and Technology, China University of Geosciences (Beijing), Beijing, ChinaGNSS Research Center, Hubei Luojia Laboratory, Wuhan University, Wuhan, ChinaGNSS Research Center, Hubei Luojia Laboratory, Wuhan University, Wuhan, ChinaLength of day (LOD), a critical component of Earth orientation parameters (EOP), represents variations in Earth’s rotation rate. It is very difficult to predict accurately due to the effects of atmosphere, ocean, hydrology, the Earth’s internal interactions and so on. The international Earth rotation and reference systems service (IERS) EOP C04 series, derived from four space geodetic observations, could offer high accuracy and smooth EOP product. However, this product typically has a latency of about 30 days. It is not adequate for fields requiring strict real-time data processing and applications, such as precise tracking and navigation of interplanetary spacecraft, global navigation satellite system (GNSS) meteorology, real-time precision orbit determination of artificial satellites, real-time kinematic (RTK) positioning and so on. To address the aforementioned issues, we propose an algorithm for predicting LOD that adopts a convolutional long-short-term memory (ConvLSTM) method with different base sequence lengths based on the LOD series from the IERS EOP C04, effective angular momentum (EAM) datasets and GNSS near-real-time (NRT) LOD data from the International GNSS Services (IGS) Rapid Products. Compared to the most accurate models used by participants in the Second Earth Orientation Parameters Prediction Comparison Campaign (2nd EOP PCC), when GNSS NRT data is not used, the proposed model improves LOD ultra-short-term (1–10 days) prediction accuracy by 29.72% and medium- to long-term (60–360 days) prediction accuracy by 11.86%. After incorporating GNSS NRT data, the short-term (10–30 days) LOD prediction accuracy improves by 55.07%. It is shown that the ConvLSTM model, integrated with GNSS NRT data and EAM datasets, could significantly enhance the forecast accuracy of LOD across various time spans. This advancement enriches the Earth’s rotation prediction models and holds potential benefits for real time applications such as real-time satellite orbit determination, extreme weather analysis, RTK technology and so on.https://www.tandfonline.com/doi/10.1080/10095020.2025.2471432ConvLSTMLOD predictioneffective angular momentumIGS rapid LODEarth orientation parameters |
| spellingShingle | Kehao Yu Xiaoya Wang Zhao Li Jian Wang Lihua Li Weiping Jiang Kai Yang Jingwen Long Yunzhao Li Near real-time LOD prediction using ConvLSTM model through integrating IGS rapid LOD and effective angular momentum Geo-spatial Information Science ConvLSTM LOD prediction effective angular momentum IGS rapid LOD Earth orientation parameters |
| title | Near real-time LOD prediction using ConvLSTM model through integrating IGS rapid LOD and effective angular momentum |
| title_full | Near real-time LOD prediction using ConvLSTM model through integrating IGS rapid LOD and effective angular momentum |
| title_fullStr | Near real-time LOD prediction using ConvLSTM model through integrating IGS rapid LOD and effective angular momentum |
| title_full_unstemmed | Near real-time LOD prediction using ConvLSTM model through integrating IGS rapid LOD and effective angular momentum |
| title_short | Near real-time LOD prediction using ConvLSTM model through integrating IGS rapid LOD and effective angular momentum |
| title_sort | near real time lod prediction using convlstm model through integrating igs rapid lod and effective angular momentum |
| topic | ConvLSTM LOD prediction effective angular momentum IGS rapid LOD Earth orientation parameters |
| url | https://www.tandfonline.com/doi/10.1080/10095020.2025.2471432 |
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