Remote Sensing-derived Flood Inundation Maps (RS-FIM) are an attractive and commonly used source of evaluation benchmarks. Errors in model-predicted FIM (M-FIM) evaluation results due to biases in RS-FIM benchmarking are quantified by introducing a high-confidence benchmark FIM, which was manually delineated from ultra-resolution imagery, as Ground Truth. The evaluation results show considerable differences in M-FIM accuracy assessment when using lower-quality benchmarks. A RS-FIM enhancement (gap-filling) procedure is presented and its effect on FIM evaluation results is analyzed. The results show that the enhancement is insufficient for significantly improving the robustness of the evaluation. The impact of including/excluding Permanent Water Bodies (PWB) on FIM evaluation results is analyzed. The results show that including PWB in FIM evaluation can significantly inflate the model accuracy. A novel evaluation strategy is proposed and analyzed. The proposed evaluation strategy is based on excluding low-confidence grid cells and PWB from the M-FIM evaluation analysis. Low-confidence grid cells are those that were estimated to be flooded by the gap-filling procedure, but were not classified as such by the remote sensing analysis. The results show that the proposed evaluation strategy can dramatically improve the robustness of the evaluation, except when a considerable number of false positives exist in the RS-FIM. The analyses showcase the many challenges in FIM evaluation. We provide an in-depth discussion about the need for standards, user-centric evaluation, the use of secondary sources, and qualitative evaluation.

The North Atlantic Ocean circulation, fueled by winds and surface buoyancy fluxes, carries 1.25 PettaWatts of heat poleward in the subtropics, and helps in regulating global weather and climate patterns. Here, we assess the impacts of changes in winds and surface heat fluxes on the Atlantic Ocean circulation and heat transport using ocean simulations. We decompose the circulation and heat transport into warm and cold cells (resembling a subtropical gyre and the dense overturning circulation respectively), and a mixed cell capturing waters transitioning between warm and cold regions. Warm and mixed cells transport more heat poleward as wind stress increases; however, these anomalies are compensated by reductions in the cold cell’s heat transport. Warm and cold cells transport more heat poleward when we increase meridional heat flux gradients. Our findings underscore the distinct roles of winds and surface heat fluxes in controlling the Atlantic Ocean’s meridional heat transport.

The transport and delivery of low-abundance, bioactive trace elements to the surface ocean by aerosol mineral dust is a major planetary control over marine primary production and hence the global carbon cycle. Variations in the concentration of atmospheric dust have established links to global climate over geologic timescales and to regional biogeographic shifts over seasonal timescales. Constraining atmospheric dust variability is thus of high value to understanding oceanographic systems, especially vast, constitutively low-nutrient subtropical gyre ecosystems and high-nutrient/low-chlorophyll ecosystems where availability of the trace element iron is a dominant ecological control. Here we leverage the MERRA-2 reanalysis product to examine over four decades of surface-level atmospheric mineral dust concentrations in a domain of the subtropical North Pacific centered at ocean Station ALOHA. This study region has been sampled regularly since the mid-1980s and was the site of the Hawaii Aerosol Time-Series (HATS) project in 2022-2023. Two unequal semi-annual periods of elevated dust are evident in the long-term data are described and constrained. We look for evidence of shifts in total and seasonal atmospheric dust abundances and in the timing of the onset of the dominant spring/summer dusty period, finding year-to-year variations but little evidence for long-term trends. We observe significant but complex relationships between the Pacific Decadal Oscillation (PDO) index and both dust and precipitation. The data show that 2022 was among the dustiest years for the study domain in the preceding two decades and, by contrast, that 2023 exhibited a significant early-spring lull in dust.

Occupations in unstructured areas subject to flooding and landslides in Brazilian cities have little vegetation, soil sealing, and concentration of air and noise pollution, impacting human health and well-being. Urban density combined with paved soil in high areas of the city can cause the phenomenon of a heat island in extreme weather events, such as rainwater flooding the lower regions of the city. There is important identify areas that need to be rebuilt to mitigate urban heat and vulnerability. This work presents a methodology for monitoring meteorological data for constructing urban heat maps in Suzano, Brazil, assisting in developing urban risk mitigation strategies. The urban parameters considered in these analyses were land uses and occupation, urban configurations (height and width of buildings), vegetation, topography, and wetlands. Methods: a) survey of urban parameters in four different areas; b) collection of environmental data during three seasons. There were used the fixed station to collect data on air temperature and humidity for 20 days in 4 areas of cities and a mobile station to collect data on air temperature and globe temperatures, humidity, wind, and surface temperatures for 1 hour in the morning an 1 hour in the afternoon for each season); c) data analysis: correlation between mapping climate data and urban parameters; d) development of urban guidelines for mitigating urban heat. In the central area, it was observed that air temperatures were around 3o C higher than in other areas. It is influenced by materials used on façades and pavements and the lack of urban trees or pocket parks. In areas close to green areas, the humidity was 5% higher than in other areas. Actions to increase permeable and green areas on sidewalks, such as the implementation of rain gardens and incentives to reduce car circulation, are actions considered. Urban heat maps help architects, urban planners, engineers, and professionals interested in improving the environment and bringing sustainability to cities.

The soil-borne disease Verticillium wilt causes significant yield losses in cotton. Developing resistant cotton varieties is a long-term solution to manage the disease. The phytopathological parameter used to assess variety resistance is the presence or absence of infection, where transverse sectioning of infected stems is discoloured/brown. The traditional method of selecting resistant varieties involves manual scoring of visual stem discolouration after cutting. However, this process is associated with high labour costs, delayed processing time and cognitive biases. Therefore, automatic detection of resistant varieties with scalable, cost-effective, and rapid phenotyping tools is needed in cotton breeding.

The polar caps of Mars, primarily composed of water ice with minor fractions of Martian dust, serve as a record of the planet’s past dynamics. Distinct dust-rich layers have been observed in exposed outcrops of both the North and South Polar Layered Deposits (NPLD and SPLD). Some of these layers correlate with bright, parallel reflectors detected by the orbital sounder SHAllow RADar (SHARAD) in the subsurface of both Martian polar caps. Some of the radargrams from high-latitude regions of the SPLD also include a pervasive elevated radar background either filling the spaces between layers or obscuring them entirely, usually referred to as “fog”. While most common explanations include off-nadir roughness or volume scattering, existing literature does not include quantitative attribution of this phenomenon. To address this, we measured backscattering and attenuation of fog from a few SHARAD tracks and compared these to the signatures of plausible subsurface interfaces. While volume scattering is too weak to explain fog, we find that specular reflections from dusty layers, when sufficiently thin, fragmented, or low in dust content, can account for both the observed backscattering and low attenuation of fog. This suggests that fog regions of the SPLD may host densely spaced weak reflectors resulting from the same kind of dust layers as the brighter slabs detected in the NPLD. As a result, the contrasting appearance of the northern and southern polar caps in SHARAD radargrams could be attributable to differing stratigraphic profiles rather than distinct scattering mechanisms.

Accurate estimates of snow water equivalent (SWE) are essential for understanding hydrological processes and managing effective management of water resources, particularly in snow-dominated regions. Many methods for estimating SWE rely on in-situ measurements and/or numerical models. In-situ measurements, such as those provided by the USDA Snotel network, have the advantage of being direct observations of SWE but are only sparsely available and suffer from challenges of representativity. At the same time, numerical models embed knowledge of the physical processes underlying the snowpack accumulation and ablation but can be computationally expensive to run over large areas. In this study, we investigate applying deep learning techniques to predict the spatiotemporal distribution of SWE from a combination of atmospheric forcings derived from the Weather Research and Forecasting (WRF) model, geographic parameters related to topography and land cover that influence snow persistence, and historical observations of snow presence/absence from remote sensing data. By leveraging static variables and dynamic atmospheric forcings from WRF  as input features, we train a convolutional long short-term memory (ConvLSTM) network to predict SWE. Our proposed deep learning model aims to accelerate the prediction of spatially distributed SWE compared to traditional methods and can complement process-based land surface models often used to predict SWE. The computational savings associated with training and forward integration of machine learning based models open the door to high-resolution ensemble forecasting of SWE and assimilation of observations for real-time SWE estimation.

A document by Shenyue Jia. Click on the document to view its contents.

A document by Jack Sheehan. Click on the document to view its contents.

Internal tides and NLIW produce the strongest currents on the Aquitaine shelf. • Coastal wind-driven upwelling alters the cross-shelf evolution of the NLIW. • First field observations of co-existing mode 1 NLIW of opposite polarity under doublepycnocline conditions.

A document by Kyle Benjamin Heyblom. Click on the document to view its contents.

Electromagnetic ion cyclotron (EMIC) waves, driven by ring current ion temperature anisotropy in the Earth’s magnetosphere, play a key role in accelerating and precipitating relativistic electrons in the radiation belts. Their excitation and saturation are significantly affected by the surrounding cold plasma. Previous studies have shown that background cold helium ions can influence the growth and saturation of EMIC waves, yet the role of cold oxygen ions in wave saturation remains less understood. In this paper, we use linear theory and nonlinear hybrid simulation to investigate the effect of cold oxygen ions in the EMIC wave growth and saturation in a homogeneous plasma containing hot and cold protons, cold helium and cold oxygen ions. Our findings reveal that increasing the cold oxygen ion concentration decreases the EMIC wave growth rate and broadens the spectral width of stop bands near the helium and oxygen gyrofrequencies. Furthermore, an increasing oxygen ion concentration notably reduces the saturation amplitude of EMIC waves in cases where the helium band is dominant, while cases with a dominant hydrogen band remain unaffected. Cold ions are heated during wave excitation, with cold helium ions heating more significantly than cold protons and oxygen ions. However, an increasing cold oxygen ion concentration reduces the heating efficiency for cold helium ions. These results offer insights into how cold plasma modifies the spectral properties and amplitudes of EMIC waves, shedding light on energy transfer from hot protons to cold plasma through EMIC waves.

Belharra is a wave energy hotspot located on the French Basque coast. Here, unusually large waves occasionally break over a 15-meter deep seamount, producing world-class conditions for big wave surfing. This does not occur often, even though the region frequently encounters highly energetic swells, especially during winter. This work attempts to better understand the underlying processes leading to extreme waves at this location. A combination of spectral and phase-resolving wave models enables analysis of how the incident swell conditions and tide level affect the wave field at both regional and local scales. We carry out a detailed wave-by-wave investigation of the local conditions for a wide range of plausible scenarios. Qualitative comparisons are drawn with data from the trajectory of a surfer riding waves at Belharra during a big swell event. Regional and local features in the bathymetry appear to govern the concentration of swell energy at this spot. Long-period swells from directions around 300° - 315° generate the most energetic local conditions. Lower tide levels lead to greater breaking intensities, albeit with weaker wave amplifications.

A document by Oleksiy Dudnik. Click on the document to view its contents.

We present an analysis of the electrokinetic coupling equations for capillary flow developed in a previous work by the same authors where an extension to Pride's theory encompassing partially saturated porous rocks was derived. One of the main hypotheses of the model establishes that the non-wetting fluid phase must be connected across the averaging volume, which may not be the case near full wetting phase saturation. To overcome this limitation we develop new expressions for the model

Distributed acoustic sensing (DAS) on submarine fiber-optic cables is providing new observational insights into solid Earth processes and ocean dynamics. However, the availability of offshore dark fibers for long-term deployment remains limited. Simultaneous telecommunication and DAS operating at different wavelengths in the same fiber, termed optical multiplexing, offers one solution. In May 2024, we collected a four-day DAS dataset utilizing an L-band DAS interrogator and multiplexing on the submarine cables of the Ocean Observatory Initiative's Regional Cabled Array offshore central Oregon. Our findings show that multiplexed DAS has no impact on communications and is unaffected by network traffic. Moreover, the quality of DAS data collected via multiplexing matches that of data obtained from dark fiber. With a machine-learning event detection workflow, we detect 31 T waves and the S wave of one regional earthquake, demonstrating the feasibility of continuous earthquake monitoring using the multiplexed offshore DAS. We also examine ocean waves and ocean-generated seismic noise. We note high-frequency seismic noise modulated by low-frequency ocean swell and hypothesize about its origins. The complete dataset is freely available.

Microearthquakes (M<2.5) recorded by borehole optical fibers with Distributed Acoustic Sensing (DAS) retain abundant high-frequency energy, allowing us to characterize the source of small events where surface stations often lack enough bandwidth to analyze. The spectral ratio method is advantageous for DAS because the method isolates the source term and removes other unwanted effects. For other approaches, we can use the densely spatially distributed DAS receiver channels to estimate the attenuation structure, which is necessary to obtain reliable source parameter measurements.

We propose a novel machine learning algorithm for automatically detecting and classifying aurora in all–sky images (ASI). Our algorithm is largely trained without requiring ground–truth labels. By including a small number of labeled images, we are able to automatically label all of the approximately 700 million images in the Time History of Events and Macroscale Interactions during Substorms (THEMIS) ASI dataset from 2008 to 2022. We use a two–stage approach. In the first stage, we adapt the Simple framework for Contrastive Learning of Representations (SimCLR) algorithm to learn latent representations of THEMIS all–sky images. We then finetune a classifier network on the latent representations our model learns of the manually labeled Oslo aurora THEMIS (OATH) dataset. We demonstrate that this two–stage approach achieves excellent classification results on data for which there is no current benchmark. The outcome of this work is a set of annotations for all existing THEMIS ASI from 2008–2022. A statistical analysis of the occurrence rates of auroral labels with respect to solar wind parameters, interplanetary magnetic field vector, and geomagnetic indices shows agreement with the literature. We further investigate the occurrence rates of auroral phenomena in the annotated data set and their geoeffectiveness by utilizing the co–located THEMIS ground magnetometer data set. These annotations will facilitate efficient information retrieval for researchers interested in specific categories of aurora and will enable large scale statistical studies and machine learning analyses of THEMIS all–sky images that have not previously been possible.