Abstract The InSight mission has collected a wealth of valuable seismic data from Mars. Analyses of these recordings have produced important new insights into the planet's interior structure and geological activity. However, the presence of numerous one‐sided transient signals, known as “glitches,” introduces difficulty and complexity into the processing of InSight seismic data. At present, no existing method is capable of perfectly detecting and removing all glitches, owing to their large number and varied origins. This study proposes a new glitch detection and removal approach by introducing a physically interpretable signal‐processing framework based on shock‐waveform decomposition (SWD) method. By exploiting the strong representational capability of the shock‐waveform, the proposed method decomposes raw seismic data into shock‐waveform components and identifies those associated with glitches and high‐frequency spikes for selective removal. Comparative analyses with established glitch detection and removal techniques demonstrate that the SWD‐based method achieves detection performance comparable to existing approaches, with intermediate detection sensitivity between that of the MPS method and those of the UCLA and GlitchNet methods and advantages of mathematical simplicity and a physical interpretation. In addition, the proposed approach effectively removes spikes occurring at glitch onset in the representative comparisons, resulting in cleaner processed signals in these cases.
Abstract NASA's Orbiting Carbon Observatory‐2 (OCO‐2) and OCO‐3 have been observing Earth's atmosphere from space since September 2014 and August 2019, respectively. The version 11 (v11) Atmospheric Carbon Observations from Space (ACOS) Level 2 full physics (L2FP) retrieval algorithm has been used to provide estimates of the column‐averaged dry‐air mole fraction of carbon dioxide () from OCO measurements of surface reflected near‐infrared solar radiation. In this work, updates to the ACOS v11 retrieval algorithm are discussed, and the quality filtering and bias correction of the products are described. estimates from OCO‐2 and OCO‐3 are compared to Total Carbon Column Observing Network (TCCON) observations and to atmospheric inversion models, which serve as truth‐proxies for . In addition, a direct comparison of collocated soundings between OCO‐2 and OCO‐3 is performed. Variations in v11 over small areas show improvement in agreement between actual and theoretical uncertainties, suggesting a reduction in systematic errors compared to v10, especially for OCO‐3. There is reduced variance in the new v11 product relative to truth‐proxies, including less divergence in the values with time against models. The collocations of observations between OCO‐2 and OCO‐3 indicate an overall mean difference of <0.2 ppm across the overlapping time range. Furthermore, OCO‐2 and OCO‐3 agree better with each other than they do with the validation sources, suggesting a well conditioned and unified set of concentrations that can be used concurrently for carbon cycle studies to provide a benchmark record spanning September 2014 through August 2026.
Abstract This study investigates the impact and mitigation of ionospheric and tropospheric delays on Sentinel‐1 interferograms in northwestern Argentina. The research focuses on the Santa María Basin in the south‐central Andes, a region characterized by moderate seismic deformation, monsoonal moisture supply, a steep east‐west rainfall gradient, and its location on the southern crest of the Equatorial Ionization Anomaly. We used ERA5 reanalysis data, validated with local Global Navigational Satellite Systems (GNSS) data, to estimate tropospheric delays. Ionospheric delays were estimated through split‐spectrum processing and interpolated GNSS data. Tropospheric delays are usually one order of magnitude larger than ionospheric delays in the descending track. In contrast, the ionospheric delay for the ascending (evening) track data is larger at the equinoxes than that of the tropospheric delay and requires correction. Furthermore, the ascending track showed a fivefold larger ionospheric delay variability than for the descending (morning) track data. This variability exhibits a strong positive correlation with the 11‐year solar cycle and displays a semi‐annual pattern. Ionospheric activity significantly affects burst alignment, leading to higher median absolute deviation of azimuth offsets for ascending pairs, especially during equinoxes, which can cause phase jumps between bursts of up to 2 mm. Accurate mitigation of both atmospheric delays is crucial for reliable deformation monitoring in the region. Failing to correct for these long‐wavelength atmospheric biases, especially from the ionosphere, could mask real, slow tectonic deformation signals in InSAR measurements. We provide a framework for correcting InSAR time series impacted by strong atmospheric delays.
Abstract NOAA's Environmental Modeling Center led a community‐wide effort to build a robust global coupled configuration within the Unified Forecast System (UFS) framework to upgrade operational systems. To reach a fully coupled configuration, the components and their interactions were iteratively refined, resulting in a more connected and comprehensive coupled modeling system. A significant milestone was delivering a mature coupled prototype (P8) as the baseline configuration for subsequent development toward operational systems. This is the first study to evaluate the spatial pattern of P8 week 3&4 ocean forecasts, with NOAA's current coupled operational system, CFSv2, serving as a comparison benchmark. Most notably, P8 demonstrates improved sea surface height (SSH) forecast skill, reduced upper ocean biases, and improved El Niño anomaly and its eastward propagation, albeit with room for further improvements. Given the numerous changes between CFSv2 and P8, isolating specific reasons for forecast improvements is difficult. Instead, a comparison to P8's predecessor, P7, suggests that iterative revisions to atmospheric physics have reduced the overly warm bias in general, and improved the wind‐driven upwelling near the equator and along the US West coast. Further improvements may be sought by refining atmospheric physics parameterization, including model uncertainty, and initializing from cycled coupled data assimilation. This study establishes a critical benchmark necessary to aid in assessing the efficacy of future upgrades, and in evaluating the relative roles of model physics versus initialization in resolving prominent ocean forecast biases.
Abstract Autonomous profiling floats have revolutionized global ocean observation, yet their passive drift limits their ability to remain within desired regions. Using observations and simulations with numerical ocean models, we quantify observed and potential residence time (RT) of profiling floats and explore station‐keeping strategies. Analysis of Argo float trajectories, performing fixed Argo‐style sampling, reveals strong spatial variability in RT, from <20 days in western boundary currents to >100 days in subtropical gyre interiors and marginal seas. To quantify RT for alternate sampling behaviors, trajectories are calculated using velocity fields from an assimilative ocean reanalysis model in the dynamically complex western Pacific. A simple selection criterion of choosing to park at the depth of minimum horizontal velocity increases the mean RT by tens to several hundreds of percent, compared with fixed parking at 1,000 m. More sophisticated selection strategies lengthen RT further—such as accounting for flow direction or changing the park depth multiple times during one dive. These control methods provide upper limits on the RT by assuming perfect knowledge of the ocean's velocity profile. These results demonstrate the potential of adaptive park‐depth selection for mission‐specific profiling floats and regional observing applications where enhanced RT is a primary objective. Such strategies are not intended for the core Argo array, for which consistent parking‐depth sampling is important for maintaining the long‐term middepth velocity record.
