Earth and Environmental Sciences

Abstract Connectivity within river networks governs the transport and aggregation of fluxes such as water, sediment, and nutrients. Here we focus on dynamic connectivity , defined by the time‐evolving connectivity that emerges as fluxes propagate and mix through the network, characterized by minimal‐flow connectivity and maximal‐flow connectivity . Using 100 natural basins across the United States, we disentangle the roles of network geometry (link‐lengths) and topology (branching structure) in controlling the times to achieve and . We find that link‐lengths decrease with stream order, and this geometric hierarchy primarily controls the time to , whereas the time to is mainly governed by network topology. Placed in a climatic context, our results show that humid basins, characterized by stronger link‐length hierarchy and enhanced side branching, exhibit slower flux aggregation. This study provides new insights into how climate influences channel network geometry and topology, thereby controlling basin‐scale flux aggregation rates.

Abstract. Balloon-borne radiosonde observations constitute a crucial component of meteorological sounding, which conducts a ground to upper-air “ascent phase” sounding. This paper introduces the Ascent-Drift-Descent Radiosonde System (ADDRS), an innovative system characterized by three observation phases – “Ascent-Drift-Descent” (ADD) – in which all three phases of sounding observation are executed through single balloon launch. Several key technologies were successfully developed, including the carrier (dual-mode balloon), the payload (System-on-Chip (SoC) module for radiosonde), Ground to upper-air data reception and control command transmission and data processing framework based on “Internet cloud + Instrument terminal” was established. Data quality control methods and data assimilation techniques of ADDRS were also developed. An interactive experiment encompassing observations and forecasting was conducted to evaluate the quality of experimental data at each phase of ADDRS. Numerical assimilation and forecasting experiments showed a positive (albeit not yet statistically significant) impact on forecast quality for both general cases and for Tropical cyclone cases. The pre-operational launching and assimilation of more than 100 such radiosondes started on 1 July 2024 and provided data over 1 year, and the number of stations continues to grow.

Abstract Satellite precipitation records are increasingly used to assess whether urbanisation modifies local rainfall. However, it remains unclear whether reported urban signals reflect physical processes or artifacts from evolving satellite observing systems. The Integrated Multi-satellitE Retrievals for GPM (IMERG) merges observations from a growing constellation of microwave and infrared sensors, meaning that long-term trends derived from this product may partly reflect changes in the observing system rather than true precipitation changes. Here we analyse rainfall frequency and intensity over 15 major global cities spanning diverse climate regimes using IMERG Version 07B and show that urban areas exhibit a consistent increase in rainfall event frequency with a weaker enhancement in intensity relative to surrounding rural areas. Both signals are dominated by microwave-based retrievals, while infrared-dominated periods largely suppress the urban hotspot pattern, highlighting the sensitivity of detected urban signals to the underlying retrieval type. To isolate physical rainfall changes from observational artifacts, we develop a synthetic time series approach that quantifies the contribution of systematically increasing microwave sampling frequency to apparent long-term trends. We find that sampling artifacts explain up to 20% of observed long-term trends, with locally higher contributions in some cities. After accounting for these effects, the urban rainfall enhancement persists across cities, demonstrating that IMERG captures a robust and physically consistent urban signal in both rainfall frequency and intensity. These findings have direct implications for the reliability of satellite-based urban climate assessments, the interpretation of long-term precipitation trends, and the design of future observing systems.

Abstract Flooding in tropical Southeast Asia often occurs under persistent cloud cover, which limits optical imagery and increases reliance on radar-based monitoring. This study presents a framework for estimating floodwater depth during the October 2020 floods in Quang Tri Province, Vietnam, using multi-temporal Sentinel-1 SAR data and supervised classification. Three classifiers—Random Forest (RFC), Maximum Likelihood (MLC), and Minimum Distance (MDC)—were trained on balanced water and non-water samples. RFC delivered the most consistent performance, with testing accuracies above 99% and RMSE values below 0.021. VH backscatter was the dominant feature, and omission and commission errors remained minimal across dates. Floodwater depth was estimated using the Floodwater Depth Estimation Tool (FwDET) with four DEMs: a locally corrected 30 m DEM and three global models (SRTM, NASADEM, MERIT). The depth maps are highly sensitive to DEM selection. The reference DEM produced shallow, coherent inundation patterns, whereas SRTM and NASADEM yielded larger, fragmented deepwater zones due to positive elevation bias and stepped surfaces. MERIT reduced these artefacts but lacked fine-scale detail. A slope-stratified analysis confirms that depth differences are greatest in low-slope terrain. Median depth biases exceed 0.25–0.35 m for slopes below 0.1° and fall below 0.1 m in steeper areas. The strong negative slope–depth correlation, consistent across two flood peaks, highlights the influence of DEM structure and floodplain morphology. The findings show that DEM quality is the dominant source of uncertainty in remote-sensing-based depth estimation in tropical lowlands. Integrating reliable SAR-based water classification with terrain-dependent diagnostics provides a clearer basis for interpreting FwDET results. These insights support improved flood hazard assessment in Quang Tri and are transferable to other flood-prone regions reliant on global DEMs.

Abstract Tropical cyclones (TCs) are highly destructive weather disasters. Prior studies of TCs and birth outcomes often examined single storms or single TC characteristics (e.g., maximum wind speed) and rarely assessed urban/rural differences. To better understand TC impacts on perinatal health, we evaluated associations between multiple TC exposure metrics and several adverse birth outcomes and considered urban-rural variations. We conducted a population-based time series analysis of 2,436,478 singleton births in Georgia from 2000–2018, in which individual-level state birth records were aggregated to the county–week level and linked to weekly, county-level TC data from the National Oceanic and Atmospheric Administration and the National Aeronautics and Space Administration. TC exposure metrics included maximum sustained wind speed (>17, 22, 25 m/s), cumulative rainfall (>125, 150, 175 mm), storm proximity (<5, 10, 20 km), and flooding events (yes/no). Generalized linear models with a Poisson distribution were used to estimate relative risks (RRs) and 95% confidence intervals (CIs) for weekly rates of preterm birth (PTB, <37 weeks), low birthweight (LBW, <2500 g), small-for-gestational-age (SGA, <10th percentile), and proportion of male births. Winds >22 m/s were associated with higher risk of PTB (RR=1.58 [95% CI: 1.27, 1.96]), LBW (RR=1.77 [95%CI: 1.40, 2.23]), and SGA (RR=1.38 [95%CI: 1.12, 1.70]). Rainfall >175 mm was associated with PTB (RR=1.44 [95%CI: 1.08, 1.93]) and LBW (RR=2.06 [95%CI: 1.50, 2.83]). Proximity <5 km was associated with PTB (RR=1.41 [95%CI: 1.04, 1.90]) and LBW (RR=2.11 [95%CI: 1.38, 3.23]). Flooding was associated with LBW (RR=1.11 [95%CI: 1.03, 1.21]) and SGA (RR=1.08 [95%CI: 1.01, 1.16]). Risk estimates were generally higher in rural versus metropolitan counties. Across multiple metrics, TC exposures were linked to increased PTB and fetal growth restriction, with stronger effects in rural counties. These findings bolster information on perinatal risks of TCs and inform more targeted disaster preparedness.

Introduction Understanding precipitation extremes is critical for effective water resource planning and agricultural management. Methods This study assessed historical and future precipitation extremes in the Southern Great Plains (SGP) of the United States. Multiple NEX-GDDP CMIP6 models were evaluated against PRISM data first to identify top performers. Three models, GFDL-CM4, GFDL-CM4-gr2, and INM-CM5-0, were selected, and their ensemble mean was used to analyze historical (1985–2014) and projected mid-century (2050s; 2041–2070) and late-century (2080s; 2071–2,100) conditions under SSP2-4.5 and SSP5-8.5 scenarios. Results and discussion Results show that precipitation extremes vary across space and time. Areal-average results show that, by the 2050s, eight extreme indices (excluding PRCPTOT and R10mm) increase under both scenarios relative to the historical period. By the 2080s, these indices continue to rise under SSP5-8.5, while all indices increase under SSP2-4.5. In terms of the areal average, all ten indices show decreasing trends in the historical period. In the 2050s, trends reverse to increases for most indices except CDD and CWD, which continue to decline. By the 2080s, trends diverge by scenario: under SSP2-4.5, most indices increase, whereas under SSP5-8.5, most indices decrease. Even though there is variation in the trends of precipitation extremes in the historical, 2050s and 2080s periods, there is no significant trend in any of the areal averages. At the grid level, the SGP region displays significant spatial heterogeneity where certain grids show a trend towards an increase in precipitation extremes, whereas others depict a decreasing trend. Although areal averages suggest weak or uncertain overall trends, significant local changes highlight the need for location-specific water management and agricultural planning.

Abstract Boundary‐layer winds strongly impact heavy rainfall through low‐level convergence, moisture transport, and vertical wind shear. Using high‐resolution observations from the 356‐m Shenzhen Meteorological Tower, together with ERA5 reanalysis and semi‐idealized numerical experiments, this study investigates the vertical structure and diurnal variation of boundary‐layer winds during April–June 2018–2020. Observations reveal an anomalous counterclockwise (CCW) wind turning with time throughout all tower levels, contrasting with the clockwise (CW) inertial oscillations typical of the Northern Hemisphere. ERA5 analysis further shows that this CCW turning gradually transitions to CW turning within the 975–925 hPa layer, forming a distinct vertical CCW‐to‐CW mode. Numerical experiments demonstrate that turbulent vertical mixing governs the vertical scale of this mode. Consequently, spatial heterogeneity in urbanization and land‐use types leads to pronounced regional differences in the transition height. These findings provide new observational evidence of deep CCW wind turning in an urbanized coastal environment.

Abstract How the urban heat island effect (UHI) responds to surface heat flux is a central question in urban climate research. Previous studies have reported two distinct scaling relations: nighttime UHI scales with heat flux to the one‐third power, while daytime UHI scales with heat flux to the two‐thirds power. However, the physical origin of this nighttime‐daytime difference and how the scaling transitions between the regimes remains elusive. We reconcile these scaling laws through a set of large‐eddy simulations (LES) in which the atmosphere is initialized with a mixed layer of depth beneath a linear potential temperature profile. LES reveals that the power‐law scaling of the UHI depends on . When (nocturnal conditions), the scaling exponent approaches one‐third, while for large (daytime conditions), it approaches two‐thirds. This scaling transition is explained through a theoretical framework that accounts for the dependence of boundary‐layer height on surface heat flux.

Abstract Analysing multi-hazard climate risk is complex due to multiple interactions among risk factors, leading to cascading and interconnected impacts. While past climate-related disasters provide invaluable insights into underlying risk factors, the non-stationarity of meteoclimatic processes and evolving non-climatic risk drivers necessitate approaches that also consider present and future conditions; moreover, lessons from past events must be generalised to effectively inform risk governance.
A range of analytical approaches has been developed to capture this complexity, each shaped by distinct perspectives and values. Understanding how these approaches conceptualise risk is crucial for advancing disaster risk science. Although widely used, they are typically applied in isolation, with no guiding framework for integrating them into a cohesive, multi-method workflow.
This paper proposes such a framework, combining Forensic Disaster Analysis (FDA), Impact Chains (IC), and Risk Storylines (SL) as complementary approaches for multi-hazard risk assessment, designed to support strategic risk framing for researchers and practitioners in Disaster Risk Reduction and Climate Change Adaptation.
We argue that applying these methods jointly and in a structured manner provides a more comprehensive understanding of climate disaster risk across the full temporal spectrum—from forensic analysis of past events, to impact chains for present risk assessment, to risk storylines for exploring possible futures in terms of materialised impacts. To substantiate this argument, we exemplify the framework using the Vaia storm in Northern Italy, and comparatively evaluate the three approaches against seven characteristics identified by Hochrainer-Stigler et al. (2023) for comprehensive multi-hazard and multi-risk assessment.
By highlighting the individual limitations and complementary strengths of these analytical approaches through the lens of these seven criteria, this paper provides a pathway for moving from single-method approaches toward an integrated, temporally comprehensive framework that can enhance risk assessment and generate actionable insights for climate and disaster risk governance














Abstract. The Universal Thermal Climate Index (UTCI) is a measure of thermal comfort that quantifies how humans experience environmental conditions. Due to its robustness and versatility as a bioclimatic indicator, it has been extensively employed across a wide range of studies in bioclimatology and is increasingly used as an operational measure of outdoor thermal comfort. At the same time, calculating the UTCI value from the relevant environmental parameters is nominally not straightforward, which is why using a 6th-degree polynomial approximation has become the standard way to calculate UTCI values. At the same time, although it is computationally efficient, the error of this polynomial approximation can be substantial. The goal of this study was to develop an improved version of the polynomial approximation – one that retains comparable computational efficiency but is more robust in terms of numerical stability and substantially more accurate, particularly in reducing the frequency of larger errors. This goal was successfully achieved using sparse orthogonal regression, namely sparse regression with an orthogonal polynomial basis, which not only substantially reduces the average errors (i.e., the mean error, the mean absolute error, and the root mean square error) but also drastically reduces the frequency of large errors. By leveraging Legendre polynomial bases, approximation models could be constructed that efficiently populate a Pareto front of accuracy versus complexity and exhibit stable, hierarchical coefficient structures across varying model capacities. Training the new approximation models over only 20 % of the data, with the testing performed over the remaining 80 %, highlights successful generalization, with the results also being robust under bootstrapping. The decomposition effectively approximates the UTCI as a Fourier-like expansion in an orthogonal basis, yielding results near the theoretical optimum in the L2 (least squares) sense.

Abstract Photovoltaic power plants (PVPPs) are expanding rapidly across arid and semi-arid regions, but their ecological consequences cannot be directly inferred from evidence drawn from multiple climatic zones, because ecosystem functioning in drylands is strongly constrained by water availability. We therefore conducted a dryland-focused meta-analysis of observational studies to quantify the effects of PVPPs on local microclimate, soil, vegetation, biodiversity, and greenhouse-gas-related variables. Our synthesis included 44 studies comprising 679 paired comparisons among under-panel, between-panel, and off-site control conditions. Using multilevel random-effects models, we estimated overall effect sizes and evaluated heterogeneity through subgroup analyses and meta-regression. Across studies, PVPPs were most consistently associated with reduced near-surface wind speed and air temperature, together with increased soil moisture and vegetation cover. In contrast, responses of aboveground biomass, annual net primary productivity, and total phosphorus were less robust and should not be interpreted as uniform ecological responses across dryland PVPPs. Heterogeneity was partly associated with experimental position, land use type, panel type, operating time, and background moisture limitation, with aridity index (AI) and mean annual precipitation emerging as the most consistently supported continuous moderators in exploratory univariate analyses. In general, PVPP-associated increases in moisture- and vegetation-related variables, particularly soil moisture and vegetation cover, were stronger under drier background conditions. These findings suggest that, within the currently available dryland evidence base, the strongest and most consistent PVPP-associated responses are concentrated in microclimate- and moisture-related variables, whereas broader ecological responses remain more heterogeneous and tend to be more positive under drier background conditions.

Abstract Understanding how forest tree species respond to past climate variability is essential for reconstructing the past climate , particularly in climate-sensitive mountain ecosystems like the Western Himalayan region. In this study, a new composite regional chronology (CRC) was developed from tree-ring samples of Abies pindrow collected from three different sites in the Western Himalayan region of Pakistan. The results of the correlation analysis revealed a significant negative relationship of CRC with pre-monsoon minimum, average, and maximum temperature, as well as a significant positive correlation with pre-monsoon rainfall. The CRC of Abies pindrow also showed a significant positive relationship with rainfall during the previous year’s July, and November, and the current year April to September. In contrast, a significant negative correlation was noted with previous year’s July temperature, while a significant positive correlation was found with August and September temperatures. Furthermore, a significant positive correlation of the CRC with January and February temperatures was observed. A marked negative correlation of the CRC was observed with the current year’s May (-0.57), and June (-0.49) temperatures. Based on these correlations, we reconstructed a 166-year record of mean maximum pre-monsoon (May-June) temperature from the Western Himalayan region of Pakistan. The reconstruction accounted for 40.8% of the variance in the observed mean maximum temperature during the instrumental period (1956-2020 CE). The spatial correlation indicates a broad regional teleconnection across the Himalayan regions covering Pakistan, India, and Nepal. The interannual bands (2.43–3.57 years) of MTM spectral analysis and wavelet power spectrum revealed possible linkage with the Atlantic Multidecadal Oscillation (AMO). The Superposed Epoch Analysis (SEA) showed a substantial connection between the reconstructed temperature and the recorded volcanic eruptions. This study serves as a valuable resource for researchers exploring regional climate change dynamics, providing comprehensive insights into the intricate relationships between climate variations and tree growth

Abstract. Interference from very low frequency (VLF, 3–30 kHz) and low frequency (LF, 30–300 kHz) radio stations is a ubiquitous and challenging noise source in transient electromagnetic (TEM) data. It can be difficult to suppress interfering radio signals with the commonly applied methods of gating and stacking. However, the characteristics of VLF and LF radio signals encoded with minimum-shift keying methods allow for a better solution where the noise is modeled and subtracted. This approach has previously been shown to give good results for continuous streams of TEM data. Recently proposed new use cases for TEM instrumentation, such as time-lapse measurements of fluctuating groundwater levels and dynamic groundwater-saltwater interfaces produce discontinuous streams of TEM data with regular gaps between individual transients. We show that under mild constraints of data availability, radio signals can still be modeled in this case. We further show that the addition of an adaptive filter can fine-tune the radio model and improve the signal-to-noise ratio. The performance is analyzed on a synthetic noise data set and on a real field noise data set. For this field noise data set, we find that the standard errors of early time TEM data are reduced by about a factor of two.

Background Climate change significantly impacts health, particularly for individuals aged 65 and older, increasing heat-related illnesses and mortality. Understanding this burden is vital for public health planning. Methods This study analyzes data from the Global Burden of Disease (GBD) Study 2021, focusing on deaths, disability-adjusted life years (DALYs), and age-standardized ratios related to high-temperature exposure in individuals aged 65+ across 204 nations and territories. It examines trends from 1990 to 2021, assesses health inequities using measures like the Inequality Slope Index and Concentration Index, and projects future trends using a Bayesian Age-Period-Cohort (BAPC) model. Results In 2021, there were 247,098 heat-related deaths globally among those aged 65+, with an age-standardized mortality rate (ASMR) increase from 26.3 to 27.1 per 100,000 (annual average percentage change (AAPC): 0.22). Aggregate DALYs reached 3,986,215, with the age-standardized DALYs rate (ASDR) rising from 419.6 to 526.7 per 100,000. South Asia and East Asia experienced higher burdens, while Oceania and Western Australia had lower rates. Lower-middle Socio-demographic Index (SDI) regions faced greater burdens, with absolute inequality increasing and relative inequality slightly declining. Non-communicable diseases (NCDs), such as ischemic heart disease and stroke, accounted for a significant proportion of heat-related deaths and DALYs, with upward trends. Projections indicate continued increases in deaths and DALYs by 2050. Conclusions This study highlights the growing global burden of high-temperature exposure on older adults, emphasizing regional disparities and the dominance of NCDs. These findings underscore the need for targeted public health strategies to address climate-related health risks in vulnerable populations.

Abstract Air pollution is a global health crisis, but evidence on public perceptions of pollution exposure and mitigation demands remains fragmented. We first map "what research covers" by reviewing the survey-based literature on public views of air pollution, coding 456 studies through a transparent, LLM-assisted screening and classification workflow. Then, we map "what surveys offer" by cataloging 112 air-quality survey items in cross-national survey programs, coding them manually using the same classification framework. We find persistent geographic and exposure-level biases: survey-based research focuses on high-income countries, China and India, while cross-national item coverage is more clustered in Europe. Thematically, both domains emphasize perceptions, with gaps in behavior, policy preferences, and health—particularly in high-exposure regions. These findings highlight that where mitigation is most needed, research coverage is most limited and accessible survey data most scarce. Methodologically, the paper develops a validated LLM-assisted workflow to facilitate future large-scale literature mapping and classification.

Abstract Stratospheric aerosol injection programs are generally understood to be technologically straightforward and inexpensive relative to other climate interventions or remedies. This gives rise to the sensible question of whether uninvolved downstream states are at risk of having their climates covertly manipulated without their knowledge by other actors. This article seeks to put that fear to rest. We first survey the wide range of SAI experiments and deployments that are possible to clarify the deployed mass requirements necessary to create discernable transboundary impacts in uninvolved states. We then explore two methods that could be reliably used by civilian uninvolved parties to detect such deployments well before they reached the scale required to precipitate transboundary climatic impacts. Those involve detecting the plumes of sulfate precursors shortly after their injection by instruments already employed on satellites to monitor point source sulfur emissions and sighting the aircraft fleets that would be required to loft those precursors high in the atmosphere. While small process experiments with negligible surface impacts could easily be conducted covertly, we demonstrate that deployments of the scale required to create transboundary climatic impacts would be discernible by uninvolved parties long before any climatic impacts would occur. 

Abstract Methane (CH4) is a potent greenhouse gas with large emissions from oil and gas infrastructure, including producing and non-producing wells. Non-producing oil and gas wells in Canada may contribute up to 13% of fugitive methane emissions from the oil and gas sector, yet there are challenges to cost-effective, fast, and reliable monitoring of the 425,000 non-producing wells in the country. This challenge is not unique to Canada, as there are ~4 million non-producing wells in the United States and millions more globally. Therefore, we evaluated methane screening measurements using a handheld device against direct emission rates, quantified using the static chamber method, at 200 wells in Alberta and Saskatchewan, Canada, and compared screening capabilities of two sensors at different price points in a controlled release setting. Screening was conducted using a portable methane detector that satisfies key elements of the United States (U.S.) Environmental Protection Agency Method 21. We found screening concentrations and emission rates were strongly correlated in Alberta and weakly correlated in Saskatchewan. Using a 10-ppm threshold, screening reliably detected medium (1,000-10,000 mg/h) and high (>10,000 mg/h) emitters with a 92% and 100% success rates, respectively. However, low emitters (<1,000 mg/h) were often missed, with only 24% detected at this threshold. Average daily wind speed showed limited influence on screening measurements at the 1% and 5% significance level, while maximum wind gust showed no effect on screening measurements. Both the higher-cost infrared absorption sensor and the lower-cost semiconductor metal oxide sensor effectively detected methane with screening concentrations above 10 ppm across flow rates ranging from 250 to 3,000 mg/h. Our findings indicate that handheld screening tools can serve as an effective screening approach to flagging high-emitting wells, which can be useful in environmental risk characterization and well plugging prioritization frameworks for state regulators and other stakeholders.

Abstract Serpentinization alters the strength, fracture behavior, and rheology of ultramafic rocks, yet the coupled evolution of mineral reactions, cracking, and mechanical softening remains poorly constrained. We experimentally simulated serpentinization in dunite at 220°C and 15 MPa for 14 and 30 days and compared the reacted specimens with dry heat‐treated controls. Poromechanical tests and petrophysical measurements showed that serpentinization reduced the drained and unjacketed bulk moduli by up to 50% after 30 days. X‐ray diffraction (XRD) confirmed progressive olivine‐to‐serpentine replacement associated with solid‐volume expansion, while micro‐CT imaging captured reaction‐driven microcrack initiation and propagation. Crack‐density analysis showed that serpentinization generates microcracks but also partially seals larger features through secondary mineral growth, consistent with mercury intrusion porosimetry results. These results reveal a coupled process in which serpentinization simultaneously produces and infills fractures, reshaping the rock's mechanical framework. The findings clarify mechanisms governing seismic velocity reductions and hydromechanical weakening in serpentinizing mantle environments.

The fate of peroxy radicals (HO2 + RO2), in particular the competition between propagation and termination pathways, is critical for understanding the atmospheric oxidative capacity. A new methodology was developed to characterize an important RO2 termination pathway by measuring the total organic nitrate yields (YRONO2) in the reactions of RO2 with NO. This is based on the use of a chemical amplifier where YRONO2 is derived by measuring the fraction of RO2 that propagates to HO2. This offers an alternative to the common approach of determining YRONO2 from the formation of an organic nitrate oxidation product. This approach circumvents the need for precise calibration with nitrate products. Applications to ethane-, cyclohexane-, and isoprene-RO2 at room temperature and atmospheric pressure yield YRONO2 values (mean ± 1σ) of (4.1 ± 2.9)%, (30.3 ± 0.8)%, and (12.1 ± 1.9)%, respectively. A comparison to the Master Chemical Mechanism indicates a good agreement for isoprene-RO2 but higher experimental yields for ethane- and cyclohexane-RO2. The disagreement observed for ethane-RO2 is attributed to the formation of organic nitrite (RONO) within this chemical system, while the discrepancy observed for cyclohexane-RO2 is likely due to an underestimation of YRONO2 in the MCM mechanism. No dependence of YRONO2 with humidity was observed for water mixing ratios ranging from 0.2 to 1.0%.

Abstract Extreme heat is an increasing public health and environmental threat shaped by both physical and social factors. This study assesses the vulnerability of mobile homes and their residents at three mobile home parks in Boulder, Colorado. We examined indoor temperature, housing conditions, and social and personal risk factors of the households. We monitored indoor and outdoor temperatures using 27 indoor and 5 outdoor temperature data loggers, comparing mobile homes with air conditioning, swamp coolers, and passive cooling designs. This work provides a detailed analysis of indoor temperature patterns and demonstrates how evaluating trends yields insights into the performance and effectiveness of different cooling systems. Homes with swamp coolers and air conditioning had average indoor temperatures of 72.7°F and 74.2°F, respectively, compared to 76.4°F in homes without cooling. A passively cooled home averaged 74.8°F and showed the longest thermal lag between outdoor and indoor peak temperatures (about 5 hours), indicating improved efficiency and heat resistance. This home was 2.1°F cooler than a neighboring uncooled unit, supported by a 17% reduction in solar radiation due to passive cooling design. Differences in cooling type influenced the duration and intensity of indoor heat exposure. To assess overall risk for residents, we combined temperature data with resident characteristics, including age, income, living alone, medical concerns, and English language fluency. Results indicate elevated risk among mobile home residents, particularly due to high night-time temperatures and limited access to effective cooling. Residents without cooling experienced consistently higher indoor temperatures, increasing cumulative heat exposure. Our findings highlight the importance of energy-efficient cooling solutions, passive design strategies, and urban forestry to reduce heat exposure in mobile home communities. Supporting design and planning interventions to improve resilience in mobile homes is essential to addressing public health concerns, particularly in regions and communities facing growing, compounding heat risks.

Abstract Lightning strikes generate broadband electromagnetic signals. At Extremely Low Frequencies (ELF), these signals partly leak into the ionosphere and produce so‐called Lightning Generated (LG) whistlers that the Absolute Scalar Magnetometers on board the ESA Swarm satellites can detect. The dispersion of LG whistlers has been empirically described by Eckersley (1935, https://doi.org/10.1038/135104a0 ): , where is the group delay of a wave packet, its frequency and is called the dispersion of the whistler. Although we find that ELF LG whistlers detected by Swarm generally follow this law, this is not the case for those detected close to the magnetic equator. Modeling ELF LG whistlers with ray‐tracing technique successfully predicts the observed dispersions and allows us to decipher what makes ELF LG equatorial whistlers different. For such whistlers, low frequencies follow rays with shorter paths than high frequencies, partly compensating for the fact that high frequencies travel faster.