| 07 Jan 2026
Bridging the gap between weather forecasting and tsunami forecasting: a background model of the open ocean in the tsunami spectrum
Abstract. A reference power law of ω -2, where ω is angular frequency, has been traditionally used to characterize the background open ocean tsunami spectrum (BOOTS) slope from a period of 10 mins to 120 mins and that its noise profile can be assumed to be normally distributed. However, this characterization is based on data from temporary deployments of bottom pressure sensors that lasted from several weeks to 11 months and only in scattered areas of the Pacific. Here we measure the BOOTS noise profile using 1–15 years of bottom pressure recorder data sampled at 15 s from the Deep-ocean Assessment and Reporting of Tsunamis (DART) stations. We utilize probabilistic power spectral density plots to create background noise models for 34 DART stations across the Pacific basin. We find that often a simple normal probability distribution does not correctly characterize the observed background noise spectrum at periods of 120 s, 250 s, 800 s, and 2700 s. We find deviations from the expected behavior follow a strong seasonality signal due to infragravity waves. We calculate infragravity wave heights and their mean values for December, January, and February and June, July, and August. We found that meteorologically induced infragravity wave events are the largest factors in seasonal variations of the BOOTS slope and intercept, especially in the east Pacific. We show the typical meteorological systems that drive these events, and we connected tropical systems from off the coast of Mexico to infragravity wave events in the east and central Pacific. And that these infragravity wave generating events potentially affect the probability distributions of the BOOTS. Finally, we convert plot the spatial distributions of the probability distribution functions.
The paper characterizes the background noise of the open ocean tsunami spectrum using a large dataset of DART stations. It challenges the traditional Gaussian noise assumption by demonstrating significant non-Gaussianity driven by seasonally variable infragravity waves (IGWs) linked to weather systems. This is an interesting work. However, the study would benefit from a more detailed discussion on the potential implications for forecasting models to fully support the proposed connection. Additionally, further clarification on the data selection criteria and the interpretation of the observed spectral features would strengthen the manuscript's robustness. Therefore, I recommend a Major Revision.
1. The title employs the broad phrase "Bridging the gap between weather forecasting and tsunami forecasting." The manuscript primarily characterizes the statistical properties of background noise and the correlation between weather systems and IGWs. It does not propose a concrete forecasting model or operational framework that truly "bridges" this gap. The current title overpromises; I suggest revising it to more accurately reflect the study's scope (e.g., focusing on the characterization of noise statistics).
2. Lines 120-121: The authors mention excluding only seismic tsunamis with Mω≥7.0, as well as landslide or volcanic tsunamis. Many earthquakes smaller than 7.0, meteotsunamis, or small-scale non-seismic events can still leave detectable signals in DART records. This could lead the authors to erroneously attribute these unremoved signals to the intrinsic non-Gaussian nature of the background noise.
3. Lines 275-278: The authors observe that noise at long periods (e.g., 2700s) also deviates from a normal distribution but state directly that they "cannot determine the cause." For a paper aiming to establish a background model, leaving the noise source of a key frequency band as an "unanswerable question" is insufficient. I suggest the authors provide a deeper discussion on potential physical causes (such as residual tides, atmospheric pressure coupling, or instrument drift) rather than merely describing the phenomenon.
4. Lines 279-285: The authors repeatedly emphasize that current Data Assimilation (DA) and Machine Learning (ML) algorithms may encounter difficulties in handling actual tsunami impacts due to the assumption of Gaussian distributions. The manuscript does not provide any actual examples or synthetic tests to quantify this impact. I recommend adding a simple DA experiment or synthetic test to substantiate this core argument.
5. Figures 7 & 8: The authors shows that weather systems drive changes in background noise primarily by visually comparing snapshots of specific meteorological events with DART noise spectra. A few case studies are insufficient to prove that these are the "largest factors." I suggest calculating the correlation coefficients between meteorological parameters (such as significant wave height or wind speed) and DART background noise parameters over the entire time series to provide statistical backing.
6. Figure 9: I suggest explicitly marking the time points of "Sensor Upgrade" or "Old Sensor / New Sensor" with arrows or text boxes directly in Figure 9.
7. Figure 11: The use of multiple shapes combined with various colors to distinguish different distributions appears visually cluttered, making it difficult to distinguish patterns, especially when the map is zoomed out. Consider simplifying the classification scheme or using more distinct color contrasts to improve readability.