Global Navigation Satellite System(GNSS)observations are critical for establishing high-precision terrestrial reference frames(TRF),but the environmental loading effects,particularly hydrological loading deformation(H...Global Navigation Satellite System(GNSS)observations are critical for establishing high-precision terrestrial reference frames(TRF),but the environmental loading effects,particularly hydrological loading deformation(HYLD),remain unaccounted in existing TRF like ITRF2020,limiting their accuracy.This study evaluates the performance of multiple HYLD datasets derived from GRACE(mascon and spherical harmonic(SH)products)and four hydrological models(LSDM,ERA5,GLDAS2,and MERRA2)in explaining seasonal and non-seasonal GNSS displacements globally using IGS Repro3 and Re pro 2datasets.Among these six HYLD datasets,we demonstrate that the GRACE mascon solution achieves superior performance in explaining the seasonal and non-seasonal GNSS displacements,by quantifying the amplitude reduction ratio(AMPR)and root mean square reduction ratio(RMSR)induced by HYLD corrections,respectively.The mascon-derived HYLD achieves better correction,particularly with the vertical median AMPR of 35.1%and RMSR of 4%.In contrast,hydrological models and SH product have relatively lower performance in explaining GNSS displacements,with ERA5 achieving only 24.7%for the ve rtical AMPR.The HYLDs of coastal stations generally exhibit worse perfo rmance with lower AMPR and more negative RMSR distributions,likely reflecting the influence of ocean loading and their limitations in accurately isolating the land water signal within land boundaries;whereas the mascon result shows minimal differences between inland and coastal stations,benefitting from the reduced leakage of land water into the oceans.Furthermore,the transition from Repro2 to the improved reprocessing strategy in Re pro3 enhances the overall consistency between HYLDs and GNSS displacements,specifically with a 7%improvement in the vertical AMPR with MERRA2.展开更多
The GRACE(Gravity Recovery and Climate Experiment)space mission recorded temporal variation characteristics of the global gravity field at decadal timescales.The gravity data have been shown to capture the dynamics of...The GRACE(Gravity Recovery and Climate Experiment)space mission recorded temporal variation characteristics of the global gravity field at decadal timescales.The gravity data have been shown to capture the dynamics of flows within the outer core and their effects on the core-mantle boundary.We first aim to remove global surface process gravity signals from the GRACE data.We then construct the global core magnetic field according to the CHAOS-7 model.Finally,we apply the blind source separation method to decompose the processed gravity signals and core magnetic signals and compute the power spectral density of the gravity and magnetic field signals by using the Lomb-Scargle periodogram approach.We have discovered a signal cycle(of~6 years)in the principal components of the core magnetic and gravity signals,potentially as a result of deep Earth processes.The main principal components of the core magnetic and gravity signals reveal that the variation trends in the second-order time derivative of the core magnetic field are similar to those in the gravity field.After 2014,the second-order time derivative of the core magnetic field exhibited linear and rapid change characteristics,which were the same as the change in the gravity field and are consistent with existing research results.展开更多
基金sponsored by the National Natural Science Foundation of China,China(Grant Nos.42174028,42474030,and 41774007)Major Science and Technology Program of Hubei Province,China(Grant No.JSCX202501188)the Natural Science Foundation of Wuhan,China(Grant No.2024040701010029)。
文摘Global Navigation Satellite System(GNSS)observations are critical for establishing high-precision terrestrial reference frames(TRF),but the environmental loading effects,particularly hydrological loading deformation(HYLD),remain unaccounted in existing TRF like ITRF2020,limiting their accuracy.This study evaluates the performance of multiple HYLD datasets derived from GRACE(mascon and spherical harmonic(SH)products)and four hydrological models(LSDM,ERA5,GLDAS2,and MERRA2)in explaining seasonal and non-seasonal GNSS displacements globally using IGS Repro3 and Re pro 2datasets.Among these six HYLD datasets,we demonstrate that the GRACE mascon solution achieves superior performance in explaining the seasonal and non-seasonal GNSS displacements,by quantifying the amplitude reduction ratio(AMPR)and root mean square reduction ratio(RMSR)induced by HYLD corrections,respectively.The mascon-derived HYLD achieves better correction,particularly with the vertical median AMPR of 35.1%and RMSR of 4%.In contrast,hydrological models and SH product have relatively lower performance in explaining GNSS displacements,with ERA5 achieving only 24.7%for the ve rtical AMPR.The HYLDs of coastal stations generally exhibit worse perfo rmance with lower AMPR and more negative RMSR distributions,likely reflecting the influence of ocean loading and their limitations in accurately isolating the land water signal within land boundaries;whereas the mascon result shows minimal differences between inland and coastal stations,benefitting from the reduced leakage of land water into the oceans.Furthermore,the transition from Repro2 to the improved reprocessing strategy in Re pro3 enhances the overall consistency between HYLDs and GNSS displacements,specifically with a 7%improvement in the vertical AMPR with MERRA2.
基金the National Natural Science Foundation of China(Grant Nos.42274003,41974007,and 41774019).
文摘The GRACE(Gravity Recovery and Climate Experiment)space mission recorded temporal variation characteristics of the global gravity field at decadal timescales.The gravity data have been shown to capture the dynamics of flows within the outer core and their effects on the core-mantle boundary.We first aim to remove global surface process gravity signals from the GRACE data.We then construct the global core magnetic field according to the CHAOS-7 model.Finally,we apply the blind source separation method to decompose the processed gravity signals and core magnetic signals and compute the power spectral density of the gravity and magnetic field signals by using the Lomb-Scargle periodogram approach.We have discovered a signal cycle(of~6 years)in the principal components of the core magnetic and gravity signals,potentially as a result of deep Earth processes.The main principal components of the core magnetic and gravity signals reveal that the variation trends in the second-order time derivative of the core magnetic field are similar to those in the gravity field.After 2014,the second-order time derivative of the core magnetic field exhibited linear and rapid change characteristics,which were the same as the change in the gravity field and are consistent with existing research results.
