D-SHIFT: Transferring High Spatial Information from GRACE Monthly TWSA Mascon to Daily Products Using Generative Adversarial Networks

The Gravity Recovery and Climate Experiment and GRACE Follow-On missions provide monthly terrestrial water storage anomaly estimates for monitoring large-scale water storage change. The new analysis still awaits peer review, but it already lays out the central claim clearly.

It matters because physics only takes a result seriously when the measurement chain remains robust under scrutiny. Experimental particle physics and precision metrology both operate in regimes where the signal sits far below the background noise, and where systematic uncertainties can mimic new physics if not controlled rigorously. The history of the field contains numerous anomalies that generated theoretical excitement before better data showed them to be artifacts, and it also contains genuine discoveries that were initially dismissed as noise. The difference is almost always resolved by independent replication with different instruments and different systematics. The Gravity Recovery and Climate Experiment (GRACE) and GRACE Follow-On missions provide monthly terrestrial water storage anomaly (TWSA) estimates for monitoring large-scale. The monthly temporal resolution of official products limits the analysis of high-frequency hydrological events, while existing daily GRACE products often have reduced spatial.

This study introduces D-SHIFT (Daily Spatial High-Resolution Inference via Feature Transformation), a deep learning-based framework for generating daily, high-resolution TWSA. The model is trained in the monthly domain by using low-resolution daily solutions and other auxiliary features as inputs, while targeting on monthly mascon products.

The model is then applied to daily SHC inputs to generate products with similar spatial resolution of monthly products. Monthly validation against mascon products gives a global mean root mean square error of about 2.3cm, with good correlation and explained variance agreement.

Daily analyses show that D-SHIFT produces spatially coherent day-to-day fields and improves basin-scale trend and seasonality estimates compared with low-resolution SHC. The basin-area double-difference analysis indicates that these gains are most relevant for spatially localized signals affected by smoothing and leakage.

The broader interest lies as much in the method as in the headline number, because a durable measurement procedure can travel farther than a single result. When experimental physicists develop a technique that achieves new sensitivity or controls a previously uncharacterized systematic, that methodological contribution persists even if the specific measurement is later revised. This is one reason why precision physics experiments often generate long-term value that is not immediately visible in the original publication.

In Greenland, D-SHIFT better reproduces coastal mass-loss patterns and gives a basin-mean trend of -10.5cm/yr, close to the CSR Monthly value of -12.0cm/yr. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy.

Because this is still a preprint, the result should be read with genuine interest and proportionate caution. Peer review is not a guarantee of correctness, but it is a process that forces authors to respond to technical criticism from specialists who have no stake in a particular outcome. Preprints that survive that process, often with substantive revisions, emerge with a stronger evidential base than the version that first appeared. Until that stage is complete, the responsible reading keeps uncertainty explicitly visible rather than treating the claims as established findings.

The next step is more measurement, tighter systematic control and scrutiny from groups whose experimental setups are genuinely independent. In experimental particle physics and precision metrology, the threshold for a discovery claim is a five-sigma excess surviving multiple analyses; an intriguing signal at lower significance is a reason to run more experiments, not a reason to revise the textbooks. Next-generation experiments currently under construction or commissioning will revisit several of the open questions that give the current result its context. Until peer review and independent follow-up address those open questions, skepticism is not a failure of appreciation for the work; it is part of how science decides what to keep.

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