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Abstract

Machine learning-based approaches demonstrate a significant potential in radar quantitative precipitation estimation (QPE) applications. In contrast to conventional methods that depend on local raindrop size distributions, deep learning (DL) can establish an effective mapping from three-dimensional radar observations to ground rain rates. However, the lack of transparency in DL models poses challenges toward understanding the underlying physical mechanisms that drive their outcomes. This study aims to develop a DL-based QPE system and provide a physical explanation of radar precipitation estimation process. This research is designed by employing a deep neural network consisting of two modules. The first module is a quantitative precipitation estimation network that has the capability to learn precipitation patterns and spatial distribution from multidimensional polarimetric radar observations. The second module introduces a quantitative precipitation estimation shapley additive explanations method to quantify the influence of each radar observable on the model estimate across various precipitation intensities.

Abstract

Eruptive dynamics is influenced by gas escape from the ascending magma. Gas pathways form in the magma via bubble coalescence, leading to gas channeling. Magmatic crystals play a key role in gas channel formation. This work constrains experimentally decompression-induced coalescence in high-crystallinity silicic magmas without external deformation, focusing on low gas content and bimodal crystal size (microlites and phenocrysts). All percolating samples have permeabilities of 10−14 m2 at bulk porosities of 7–10 vol% and bulk crystallinities up to 75 vol%. Our results demonstrate the possibility of coalescence-related outgassing at high pressure (120–350 MPa) and without external strain, which corresponds to magma stagnating deep in a volcanic conduit. Channeling at such low gas content implies that bimodal crystallinity favors effusive over explosive volcanic behavior. It may also be the missing physical mechanism explaining gas transfer across magmatic systems despite high melt viscosity and low or absent magma extrusion.

Abstract

Composite analyses of NOAA satellite-based outgoing longwave radiation data and ERA5 reanalysis data for nearly six solar maximum periods support the existence of a response of tropical convection and precipitation to short-term (∼27-day) solar ultraviolet variations. Following solar UV peaks, the response consists of an increase in average convection and precipitation in the equatorial Indian Ocean and a decrease in the western and central tropical Pacific, with maximum amplitude at a lag of 4 to 8 days. The opposite occurs following short-term solar UV minima. The observed responses are most detectable when the Madden-Julian oscillation (MJO) is active and appear to be related to a reduced ability of the MJO to propagate across the Maritime Continent barrier following solar UV peaks relative to UV minima. A similar behavior has previously been found when the stratospheric quasi-biennial oscillation is in its westerly phase relative to its easterly phase.

Abstract

The hydrological and biogeochemical properties of the hyporheic zone in stream and riverine ecosystems have been extensively studied over the past two decades. Although it is widely acknowledged that sediment heterogeneity can influence biogeochemical reactions, little effort has been made to understand the role of heterogeneity on the spatiotemporal variability of riverbed redox conditions under changing flow dynamics at different timescales. Here we integrate a mechanistic model and field data to demonstrate that heterogeneity in permeability plays a vital role in modulating sediment redox conditions at both seasonal (annual) and event (daily-to-weekly) timescales, whereas heterogeneity in particulate organic carbon (POC) content only has a comparable influence on redox conditions at the seasonal timescale. These findings underscore the importance of accurately characterizing sediment heterogeneity, in terms of permeability and POC content, in quantifying biogeochemical dynamics in the riverbed and hyporheic zones of riverine ecosystems.

Abstract

Since volcanic activity was first discovered on Io from Voyager images in 1979, changes on Io's surface have been monitored from both spacecraft and ground-based telescopes. Here, we present the highest spatial resolution images of Io ever obtained from a ground-based telescope. These images, acquired by the SHARK-VIS instrument on the Large Binocular Telescope, show evidence of a major resurfacing event on Io's trailing hemisphere. When compared to the most recent spacecraft images, the SHARK-VIS images show that a plume deposit from a powerful eruption at Pillan Patera has covered part of the long-lived Pele plume deposit. Although this type of resurfacing event may be common on Io, few have been detected due to the rarity of spacecraft visits and the previously low spatial resolution available from Earth-based telescopes. The SHARK-VIS instrument ushers in a new era of high resolution imaging of Io's surface using adaptive optics at visible wavelengths.

Abstract

La Palma, Canary Islands, had its largest historical eruption in 2021. From January 2022 to May 2023 there were >2,100 seismic events, primarily at depths ≤20 km, prompting us to update the deformation and modeling study, using interferometric synthetic aperture radar observations and a last generation interpretation tool. We detect the evolution of the remaining magmatic body in the SW portion of the island, with arrival of new magma moving into the oceanic crust out to sea, and a pressurized zone in the central-eastern area, at regions of structural weakness. The current source characteristics have some similarities to the early stage dynamics prior to the 2021 eruption. Operational and multidisciplinary studies must continue to monitor either their stabilization or growth and destabilization. The ability to identify magma ascent using only deformation data over short time periods allows us to characterize unrest patterns and provide new insights into volcanic processes.

Abstract

The evolution of coastal wetlands is a complex process which is difficult to forecast, made more complicated by the addition of changing climatic conditions. Here, long term ecological and geomorphological data are coupled to geotechnical measurements at a coastal wetland in North Inlet estuary, South Carolina. The coupled methodology is presented and discussed in context of understanding coastal wetland system evolution in a changing climate. Specifically, the root shear strength of Spartina alterniflora across a range of elevations was investigated using a cone penetrometer test. Elevation, shear strength, and biomass are shown to be critically interconnected. Root strength was shown to decrease with increased inundation time and decreased elevation (i.e., mudflats). Conversely, the data set illustrates the importance of maintaining key elevation ranges in relation to sea-level to optimize wetland resilience.

Abstract

The climate benefit of blue carbon sequestered by mangrove forests can be partially offset by CH4 emission, but this offset is rarely assessed using multi-year high-frequency measurements. Here, four-year eddy covariance measurements were used to examine temporal patterns of CH4 flux and its blue carbon offset (i.e., reduced climate benefit) in a subtropical estuarine mangrove in China. We found both diel and seasonal CH4 fluxes were mainly driven by soil temperature and tidal activities, showing greater nighttime emission. On average, one-tenth of CO2 uptake was offset by CH4 emission using the sustained-flux global warming potential metric at a 20-year time horizon, while this offset could vary over an order of magnitude due to asynchronous fluxes of CH4 and CO2 across diel and seasonal cycles. These results highlight the significant contribution of nighttime emission to mangrove CH4 budget and the importance of asynchronous flux variations in assessing mangrove's climate benefit.

Abstract

Tornadoes, as highly destructive weather events, require accurate detection for effective decision-making. Traditional radar-based tornado detection algorithms (TDA) face challenges with limited tornado feature extraction capabilities, leading to high false alarm rates and low detection probabilities. This study introduces the Multi-Task Identification Network (MTI-Net), leveraging Doppler radar data to enhance tornado recognition. MTI-Net integrates tornado detection and estimation tasks to acquire comprehensive spatial and locational information. As part of MTI-Net, we introduce a novel backbone network of Multi-Head Convolutional Block (MHCB), which incorporates Spatial and Channel Attention Units (SAU and CAU). SAU optimizes local tornado feature extraction, while CAU reduces false alarms by enhancing dependencies among input variables. Experiments demonstrate the superiority of MTI-Net over TDA, with a decrease in false alarm rates from 0.94 to 0.46 and an increase in hit rates from 0.23 to 0.81, highlighting the effectiveness of MTI-Net in handling small-scale tornado events.

Abstract

The role of internal variability in generating an apparent link between autumn Barents-Kara sea (BKS) ice and the winter North Atlantic Oscillation (NAO) has been intensely debated. In particular, the robustness and causality of the link has been questioned by showing that BKS-NAO correlations exhibit nonstationarity in both reanalysis and climate model simulations. We show that the lack of ice observations means nonstationarity cannot be confidently assessed using reanalysis prior to 1961. Model simulations are used to corroborate an argument that forced nonstationarity could result from ice edge changes due to global warming. Consequently, the observed change in BKS-NAO correlations since 1960 might not be purely a result of internal variability and may also reflect that the ice edge has moved. The change could also reflect the availability of more accurate ice observations. We discuss potential implications for analysis based on coupled climate models, which exhibit large ice edge biases.

Abstract

West Africa continues to host a growing number of low and intermediate-magnitude earthquakes (M2-5) along its passive margins, and its continental interior. Earthquake activity in these regions raises the need to comprehend the causes and the tectonic controls of the seismicity. Unfortunately, such studies are rare. Here, we apply single-station inversion techniques to constrain fourteen focal mechanisms, computed after compiling a set of high-quality waveforms. We describe the connection between seismicity, the contemporary stress field, anthropogenic activity and Holocene fault scarps in the region. Our results indicate transpressive stresses acting on the inherited brittle structures in the passive margins. We also observe a compressive regime in the intracontinental failed rifts. We attribute the seismicity to the reactivation of “weak” faults in the Neoproterozoic and Mesozoic failed rifts, the passive transform structures, and the intracratonic Precambrian brittle shear zones.

Abstract

The Radio Frequency Sensor (RFS), a new radio frequency lightning detector, was launched into geosynchronous orbit in December 2021, and first collected data in January 2022. RFS is a specialized software-defined radio receiver that detects, records, and reports impulsive broadband radio-frequency (RF) signatures from lightning in the very high frequency (VHF; 30–300 MHz) range. Its vantage point from a Western hemisphere geosynchronous orbit provides unique opportunities to study evolution of RF lightning signatures over the durations of thunderstorms over the Americas and Pacific Ocean. Its overlapping view with the Geostationary Lightning Mappers (GOES-16 & 17) enables additional comparisons between the sources of optical emissions and associated VHF emissions that were not possible with previous sensors. We find that RFS preferentially detects bright VHF signals called transionospheric pulse pairs (trans-ionospheric pulse pairs (TIPPs)). It is estimated that more than 85% of the RFS-detected lightning events are TIPPs. This paper presents initial results from the first year and a half of on-orbit operation.

Abstract

Mass loss of the Greenland Ice Sheet (GrIS) plays a major role in the global sea level rise. The west coast of the GrIS has contributed 1,000 Gt of the 4,488 Gt GrIS mass loss between 2002 and 2021, making it a hotspot for GrIS mass loss. Surface melting is driven by changes in the radiative budget at the surface, which are modulated by clouds. Previous works have shown the impact of North Atlantic transport for influencing cloudiness over the GrIS. Here we used space-based lidar cloud profile observations to show that a polar low circulation promotes the presence of low clouds over the GrIS west coast that warm radiatively the GrIS surface during the melt season. Polar low circulation transports moisture and low clouds from the sea to the west of Greenland up over the GrIS west coast through the melt season. The concomitance of the increasing presence of low cloud in fall over the Baffin Sea due to seasonal sea-ice retreat and a maximum occurrence of Polar low circulation in September results in a maximum of low cloud fraction (∼14% at 2.5 km above sea level) over the GrIS west coast in September. These low clouds warm radiatively the GrIS west coast surface up to 80 W/m2 locally. This warming contributes to an average increase of 10 W/m2 of cloud surface warming in September compared to July on the GrIS west coast. Overall, this study suggests that regional atmospheric processes independent from North Atlantic transport may also influence the GrIS melt.

Abstract

The Multi-angle Imaging SpectroRadiometer (MISR) aboard NASA's Terra satellite observed the Hunga Tonga—Hunga Ha'apai (HTHH) 15 January eruption plume on eight occasions between 15 and 23 January 2022. From the MISR multi-angle, multi-spectral imagery we retrieve aerosol plume height geometrically, along with plume-level motion vectors, and derive radiometrically constraints on particle effective size, shape, and light-absorption properties. Parts of two downwind aerosol layers were observed in different places and times, one concentrated in the upper troposphere (11–18 km ASL), and a mid-stratosphere layer ∼23–30+ km ASL. After the initial day (1/15), the retrievals identified only spherical, non-light-absorbing particles, typical of volcanic sulfate/water particles. The near-tropopause plume particles show constant, medium-small (several tenths of a micron) effective size over 4 days. The mid-stratosphere particles were consistently smaller, but retrieved effective particle size increased between 1/17 and 1/23, though they might have decreased slightly on 1/22. As a vast amount of water was also injected into the stratosphere by this eruption, models predicted relatively rapid sulfate particle growth from the modest amounts of SO2 gas injected by the eruption to high altitudes along with the water (Zhu et al., 2022, https://doi.org/10.5194/acp-22-10267-2022). MISR observations up to 10 days after the eruption are consistent with these model predictions. The possible decrease in stratospheric particle size after initial growth was likely caused by evaporation, as the plume mixed with drier, ambient air. Particles in the lower-elevation plume observed on 1/15 were larger than all the downwind aerosols and contained significant non-spherical (likely ash) particles.

Abstract

Rising Arctic temperatures pose a threat to the large carbon stores trapped in Arctic permafrost. To assess methane emissions in high-Arctic regions, we analyzed atmospheric data from Alaska and Siberia using two methods: (a) a wind sector approach to calculate emission changes based on concentration enhancements using wind direction, and (b) an inversion method utilizing a high-resolution atmospheric transport model. Incorporating data after 2015, we observed a significant rise in methane emissions (0.018 ± 0.005 Tg yr−2 from 2000 to 2021) from Alaska's North Slope, indicating a shift from previous analyses. We find 34%–50% of yearly emissions occurred in the late season (September–December) consistently across multiple years and regions, which is historically underestimated in models and inventories. Our findings reveal significant changes occurring in the Arctic, highlighting the crucial role of long-term atmospheric measurements in monitoring the region, especially during the cold season.

Assessing the impact of climate change on landslides near Vejle, Denmark, using public data
Kristian Svennevig, Julian Koch, Marie Keiding, and Gregor Luetzenburg
Nat. Hazards Earth Syst. Sci., 24, 1897–1911, https://doi.org/10.5194/nhess-24-1897-2024, 2024
In our study, we analysed publicly available data in order to investigate the impact of climate change on landslides in Denmark. Our research indicates that the rising groundwater table due to climate change will result in an increase in landslide activity. Previous incidents of extremely wet winters have caused damage to infrastructure and buildings due to landslides. This study is the first of its kind to exclusively rely on public data and examine landslides in Denmark.

A Holocene alpine seismic chronicle from Lake Aiguebelette (NW French Alps)
Mathilde Banjan, Christian Crouzet, Hervé Jomard, Pierre Sabatier, David Marsan, and Erwan Messager
Nat. Hazards Earth Syst. Sci. Discuss., https//doi.org/10.5194/nhess-2024-83,2024
Preprint under review for NHESS (discussion: open, 0 comments)
This research shows how lake sediments reveal seismic activity history over extended periods, surpassing historical records. Sediment analysis from Lake Aiguebelette in the Western Alps found 32 layers likely caused by earthquakes over the Holocene. Robust dating methods correlated these layers with known historical earthquakes. Results suggest Lake Aiguebelette's sediment records mainly reflect local seismic events, enhancing understanding of earthquake recurrence and regional seismic history.

Can remote sensing combustion phase improve estimates of landscape fire smoke emission rate and composition?
Farrer Owsley-Brown, Martin J. Wooster, Mark J. Grosvenor, and Yanan Liu
Atmos. Meas. Tech. Discuss., https://doi.org/10.5194/amt-2024-73,2024
Preprint under review for AMT (discussion: open, 0 comments)
Landscape fires produce vast amounts of smoke, affecting the atmosphere locally and globally. Whether a fire is flaming or smoldering strongly impacts the rate at which smoke is produced as well as its composition. This study tested two methods to determine these combustion phases in laboratory fires and compared them to the smoke emitted. One of these methods improved estimates of smoke emission significantly. This suggests potential for improvement in global emission estimates.

Innovative aerosol hygroscopic growth study from Mie–Raman–fluorescence lidar and microwave radiometer synergy
Robin Miri, Olivier Pujol, Qiaoyun Hu, Philippe Goloub, Igor Veselovskii, Thierry Podvin, and Fabrice Ducos
Atmos. Meas. Tech., 17, 3367–3375, https://doi.org/10.5194/amt-17-3367-2024, 2024
This paper focuses on the use of fluorescence to study aerosols with lidar. An innovative method for aerosol hygroscopic growth study using fluorescence is presented. The paper presents case studies to showcase the effectiveness and potential of the proposed approach. These advancements will contribute to better understanding the interactions between aerosols and water vapor, with future work expected to be dedicated to aerosol–cloud interaction.

Dual adaptive differential threshold method for automated detection of faint and strong echo features in radar observations of winter storms
Laura M. Tomkins, Sandra E. Yuter, and Matthew A. Miller
Atmos. Meas. Tech., 17, 3377–3399, https://doi.org/10.5194/amt-17-3377-2024, 2024
We have created a new method to better identify enhanced features in radar data from winter storms. Unlike the clear-cut features seen in warm-season storms, features in winter storms are often fuzzier with softer edges. Our technique is unique because it uses two adaptive thresholds that change based on the background radar values. It can identify both strong and subtle features in the radar data and takes into account uncertainties in the detection process.