Junyang Gou
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View article: Assessment of ocean bottom pressure variations in CMIP6 HighResMIP simulations
Assessment of ocean bottom pressure variations in CMIP6 HighResMIP simulations Open
Ocean bottom pressure (pb) variations from high-resolution climate model simulations under the CMIP6 (Coupled Model Intercomparison Project Phase 6) HighResMIP protocol are potentially useful for oceanographic and space-geodetic research, …
View article: A Deep Ensemble Transformer Model for Global Ionosphere Prediction and Uncertainty Quantification
A Deep Ensemble Transformer Model for Global Ionosphere Prediction and Uncertainty Quantification Open
View article: DeepRec: Global Terrestrial Water Storage Reconstruction Since 1941 Using Spatiotemporal-Aware Deep Learning Model
DeepRec: Global Terrestrial Water Storage Reconstruction Since 1941 Using Spatiotemporal-Aware Deep Learning Model Open
View article: Supplementary material to "Assessment of Ocean Bottom Pressure Variations in CMIP6 HighResMIP Simulations"
Supplementary material to "Assessment of Ocean Bottom Pressure Variations in CMIP6 HighResMIP Simulations" Open
View article: Uncertainties of Satellite-based Essential Climate Variables from Deep Learning
Uncertainties of Satellite-based Essential Climate Variables from Deep Learning Open
Accurate uncertainty information associated with essential climate variables (ECVs) is crucial for reliable climate modeling and understanding the spatiotemporal evolution of the Earth system. In recent years, geoscience and climate scient…
View article: Downscaling GRACE-derived ocean bottom pressure anomalies using self-supervised data fusion
Downscaling GRACE-derived ocean bottom pressure anomalies using self-supervised data fusion Open
The gravimetry measurements from the Gravity Recovery and Climate Experiment (GRACE) and its follow-on (GRACE-FO) satellite mission provide an essential way to monitor changes in ocean bottom pressure ($p_b$), which is a critical variable …
View article: Improving the spatial resolution of global mass changes observed by GRACE(-FO) using deep learning — from terrestrial water to the ocean
Improving the spatial resolution of global mass changes observed by GRACE(-FO) using deep learning — from terrestrial water to the ocean Open
Gravity field solutions from the Gravity Recovery and Climate Experiment (GRACE) and its follow-on (GRACE-FO) satellite mission provide an essential way to monitor mass changes in the climate system, comprising terrestrial water storage (T…
View article: An Ionospheric Forecasting Model Based on Transfer Learning Using High-Resolution Global Ionospheric Maps
An Ionospheric Forecasting Model Based on Transfer Learning Using High-Resolution Global Ionospheric Maps Open
High-precision ionospheric prediction is essential for real-time applications of the Global Navigation Satellite System (GNSS), especially for single-frequency receivers. Various machine learning (ML) algorithms have been utilized for iono…
View article: Global high-resolution total water storage anomalies from self-supervised data assimilation using deep learning algorithms
Global high-resolution total water storage anomalies from self-supervised data assimilation using deep learning algorithms Open
Total water storage anomalies (TWSAs) describe the variations of the terrestrial water cycle, which is essential for understanding our climate system. This study proposes a self-supervised data assimilation model with a new loss function t…
View article: Operational Forecasting of Effective Angular Momentum Functions Fourteen Days Ahead
Operational Forecasting of Effective Angular Momentum Functions Fourteen Days Ahead Open
Forecasts of Earth’s Effective Angular Momentum functions (EAM) are used for different applications, including prediction of Earth Orientation Parameters (EOPs). Since May 2021, the Chair of Space Geodesy at ETH Zurich has been operational…
View article: Modeling the Differences between Ultra-Rapid and Final Orbit Products of GPS Satellites Using Machine-Learning Approaches
Modeling the Differences between Ultra-Rapid and Final Orbit Products of GPS Satellites Using Machine-Learning Approaches Open
The International GNSS Service analysis centers provide orbit products of GPS satellites with weekly, daily, and sub-daily latency. The most frequent ultra-rapid products, which include 24 h of orbits derived from observations and 24 h of …
View article: Global high-resolution total water storage anomalies from self-supervised data assimilation using deep learning algorithms
Global high-resolution total water storage anomalies from self-supervised data assimilation using deep learning algorithms Open
Total water storage anomalies (TWSAs) describe the variations of the terrestrial water cycle, which is essential for better understanding our climate system. This study proposes a self-supervised data assimilation model with a novel loss f…
View article: Modelling the differences between ultra-rapid and final orbit products of GPS satellites using machine learning approaches
Modelling the differences between ultra-rapid and final orbit products of GPS satellites using machine learning approaches Open
The International GNSS Service analysis centers provide orbit products of GPS satellites with weekly, daily, and sub-daily latency. The most frequent ultra-rapid products, which include one day of orbits derived from observations and one d…
View article: Ultra-short-term prediction of LOD using LSTM neural networks
Ultra-short-term prediction of LOD using LSTM neural networks Open
Earth orientation parameters (EOPs) are essential in geodesy, linking the terrestrial and celestial reference frames. Due to the time needed for data processing and combining different space geodetic techniques, EOPs of the highest quality…
View article: A machine learning approach to recover GRACE-B accelerometer data
A machine learning approach to recover GRACE-B accelerometer data Open
In gravimetry satellite missions GRACE (Gravity Recovery and Climate Experiment) and GRACE-FO (GRACE Follow-On), accelerometer measurements from both satellites are necessary for the gravity field recovery. The accelerometer provides accur…
View article: Global high-resolution total water storage anomalies from self-supervised data assimilation using deep learning algorithms
Global high-resolution total water storage anomalies from self-supervised data assimilation using deep learning algorithms Open
Total water storage anomalies (TWSAs) describe the variations of the terrestrial water cycle, which is essential for better understanding our climate system. Global TWSAs can be simulated by hydrological models with high spatial resolution…
View article: The New Geodetic Prediction Center at ETH Zurich
The New Geodetic Prediction Center at ETH Zurich Open
<p>Geodetic measurements allow the determination of a wide variety of parameters describing the Earth system, including its shape, gravity field, and orientation in space. The importance of such parameters to science and society is m…
View article: Improving the Accuracy of GNSS Orbit Predictions using Machine Learning Approaches
Improving the Accuracy of GNSS Orbit Predictions using Machine Learning Approaches Open
<p>Precise orbit determination is vital for the increasingly vast number of space objects around the Earth. Moreover, accurate orbit prediction of GNSS satellites is essential for many real-time geodetic applications, including real-…
View article: Deep quantum learning with long short-term memory for geodetic time series prediction: Application to length of day prediction
Deep quantum learning with long short-term memory for geodetic time series prediction: Application to length of day prediction Open
Earth and Space Science Open Archive This is a preprint and has not been peer reviewed. ESSOAr is a venue for early communication or feedback before peer review. Data may be preliminary.Learn more about preprints preprintOpen AccessYou are…
View article: Ultra-short-term prediction of LOD using LSTM neural networks
Ultra-short-term prediction of LOD using LSTM neural networks Open
<p>The Earth Orientation Parameters (EOP) are fundamentals of geodesy, connecting the terrestrial and celestial reference frames. The typical way to generate EOP of highest accuracy is combining different space geodetic techniques. D…
View article: Estimation of significant wave height using Sentinel-3 data
Estimation of significant wave height using Sentinel-3 data Open
Coastal area is one of the most important area for us. More than 600 million people (around 10% of the word’s population) live in coastal areas that are less than 10m above sea level. Nearly 2.4 billion people (about 40% of the world’s pop…