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Quantification of uncertainty in surface mass change signals derived from Global Positioning System (GPS) measurements poses challenges, especially when dealing with large datasets with continental or global coverage. We present a new GPS station displacement dataset that reflects surface mass load signals and their uncertainties. We assess the structure and quantify the uncertainty of vertical land displacement derived from 3045 GPS stations distributed across the continental US. Monthly means of daily positions are available for 15 years. We list the required corrections to isolate surface mass signals in GPS estimates and screen the data using GRACE(-FO) as external validation. Evaluation of GPS time series is a critical step, which identifies (a) corrections that were missed, (b) sites that contain non-elastic signals (e.g., close to aquifers), and (c) sites affected by background modeling errors (e.g., errors in the glacial isostatic model). Finally, we quantify uncertainty of GPS vertical displacement estimates through stochastic modeling and quantification of spatially correlated errors. Our aim is to assign weights to GPS estimates of vertical displacements, which will be used in a joint solution with GRACE(-FO). We prescribe white, colored, and spatially correlated noise. To quantify spatially correlated noise, we build on the common mode imaging approach by adding a geophysical constraint (i.e., surface hydrology) to derive an error estimate for the surface mass signal. We study the uncertainty of the GPS displacement time series and find an average noise level between 2 and 3 mm when white noise, flicker noise, and the root mean square (rms) of residuals about a seasonality and trend fit are used to describe uncertainty. Prescribing random walk noise increases the error level such that half of the stations have noise > 4 mm, which is systematic with the noise level derived through modeling of spatially correlated noise. The new dataset is available at 10.5281/zenodo.8184285 (Peidou et al., 2023) and is suitable for use in a future joint solution with GRACE(-FO)-like observations. [ABSTRACT FROM AUTHOR]

Quantification of uncertainty in surface mass change signals derived from GPS measurements poses challenges, especially when dealing with large datasets with continental or global coverage. This study recommends a method for preparing and processing GPS measurements for use in a future joint solution with GRACE(-FO)-like observations. We assess the structure and quantify the uncertainty of vertical land displacement derived from 3045 GPS stations distributed across the continental US. Monthly means of daily positions are available for 15 years. We list the required corrections to isolate surface mass signals in GPS estimates and screen the data using GRACE(-FO) as external validation. Evaluation of GPS timeseries is a critical step, which identifies a) corrections that were missed; b) sites that contain non-elastic signals (e.g., close to aquifers); and c) sites affected by background modelling errors (e.g., errors in the glacial isostatic model). Finally, we quantify uncertainty of GPS vertical land displacement (VLD) estimates through stochastic modeling and quantification of spatially correlated errors. Our aim is to assign weights to GPS estimates of VLD, which will be used in a joint solution with GRACE(-FO). We prescribe white, colored and spatially correlated noise. To quantify spatially correlated noise, we build on the common mode imaging approach adding a geophysical constraint (i.e., surface hydrology) to derive an error estimate for the surface mass signal. We study the uncertainty derived using each technique and find that three techniques exhibit an average noise level between 2-3 mm: white noise, flicker noise, and RMS of residuals about a seasonality and trend fit. Prescribing random walk noise increases the error level such that half of the stations have noise > 4 mm, which is systematic with the noise level derived through modeling of spatial correlated noise. The new data set is suitable for use in a future joint solution with GRACE(-FO)-like observations. [ABSTRACT FROM AUTHOR]

The intersatellite range measurements between the Gravity Recovery and Climate Experiment Follow‐On (GRACE‐FO) satellite pair enable mapping time variations in Earth's gravity field. The Laser Ranging Interferometer (LRI) and Microwave Instrument (MWI) provide independent range measurements. We compare gravity fields derived from the two instruments. The LRI captures time‐variable gravity field signals up to 21 mHz (165 km half‐wavelength) globally, while MWI reaches frequencies up to 15 mHz (230 km). Time‐variable mass variations free from annual cycles and trends are sensed up to 15 mHz (LRI) and 11 mHz (320 km) (MWI). Both instruments are shown to be excellent tools for detection and monitoring of sub‐monthly geophysical signals. Along‐track global maps of sub‐monthly signals are expected to shed light on the high‐frequency behavior of a large number of geophysical processes. Plain Language Summary: Mass change is a fundamental observable for climate studies. The Gravity Recovery and Climate Experiment (GRACE) and GRACE‐FO satellite gravimetry missions track mass changes on a monthly basis for nearly 20 years. We are interested in characterizing the capabilities of the GRACE‐FO measurement system to observe mass change signals mapped in the time‐variable gravity field, in space, time and frequency domains. Analysis of the gravity fields retrieved using two independent ranging instruments on GRACE‐FO, the Microwave Instrument (MWI) and a novel Laser Ranging Interferometer (LRI), shows that the LRI consistently detects time‐variable gravity signals in higher frequencies (finer spatial scales) than the MWI. Additionally, we show that both instruments are very efficient in measuring sub‐monthly gravity field variations, mainly driven by ocean dynamics, hydrosphere and tides. A refined function that translates range acceleration measurements to surface mass estimates is also introduced and used to convert measured range accelerations over known watersheds to surface mass estimates. Key Points: We quantify the ability of the GRACE‐FO measurement system to measure geophysical signals in space and timeThe GRACE‐FO measurement system can provide global maps of sub‐monthly gravity signals driven by hydrosphere, tides, and ocean dynamicsLaser ranging maps time‐variable gravity at finer spatial scales than microwave ranging [ABSTRACT FROM AUTHOR]