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1. Ground-based Observations of Temporal Variation of the Cosmic-Ray Spectrum during Forbush Decreases

   https://doi.org/10.3847/2041-8213/add7d1  

   Mitthumsiri, W; Ruffolo, D; Munakata, K; Kozai, M; Hayashi, Y; Kato, C; Muangha, P; Sáiz, A; Evenson, P; Mangeard, P-S; et al (June 2025, The Astrophysical Journal Letters)

   Abstract Observations of temporary Forbush decreases (FDs) in the Galactic cosmic-ray (GCR) flux due to the passage of solar storms are useful for space-weather studies and alerts. Here, we introduce techniques that use global networks of ground-based neutron monitors and muon detectors to measure variations of GCR rigidity spectra in space during FDs by (1) fitting count rate decreases for power-law rigidity spectra in space with anisotropy up to second order and (2) using the “leader fraction” derived from a single neutron monitor. We demonstrate that both provide consistent results for hourly spectral index variations for five major FDs, and they agree with daily space-based data when available from the Alpha Magnetic Spectrometer. We have also made the neutron monitor leader fraction publicly available in real time. This work verifies that ground-based observations can be used to precisely monitor GCR spectral variation over a wide range of rigidities during space-weather events, with results in real time or from short-term postanalysis. 

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2. High-resolution precipitation monitoring with a dense seismic nodal array

   https://doi.org/10.1038/s41598-023-38008-w  

   Hua, Junlin; Wu, Mengxi; Mulholland, Jake P.; Neelin, J. David; Tsai, Victor C.; Trugman, Daniel T. (December 2023, Scientific Reports)

   Abstract Accurate precipitation monitoring is crucial for understanding climate change and rainfall-driven hazards at a local scale. However, the current suite of monitoring approaches, including weather radar and rain gauges, have different insufficiencies such as low spatial and temporal resolution and difficulty in accurately detecting potentially destructive precipitation events such as hailstorms. In this study, we develop an array-based method to monitor rainfall with seismic nodal stations, offering both high spatial and temporal resolution. We analyze seismic records from 1825 densely spaced, high-frequency seismometers in Oklahoma, and identify signals from nine precipitation events that occurred during the one-month station deployment in 2016. After removing anthropogenic noise and Earth structure response, the obtained precipitation spatial pattern mimics the one from a nearby operational weather radar, while offering higher spatial (~ 300 m) and temporal (< 10 s) resolution. We further show the potential of this approach to monitor hail with joint analysis of seismic intensity and independent precipitation rate measurements, and advocate for coordinated seismological-meteorological field campaign design. 

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3. Estimating Fire Radiative Power Using Weather Radar Products for Wildfires

   https://doi.org/10.1029/2023GL104824  

   Saide, P. E.; Krishna, M.; Ye, X.; Thapa, L. H.; Turney, F.; Howes, C.; Schmidt, C. C. (November 2023, Geophysical Research Letters)

   Abstract Satellite‐based Fire radiative power (FRP) retrievals are used to track wildfire activity but are sometimes not possible or have large uncertainties. Here, we show that weather radar products including composite and base reflectivity and equivalent rainfall integrated in the vicinity of the fires show strong correlation with hourly FRP for multiple fires during 2019–2020. Correlation decreases when radar beams are blocked by topography and when there is significant ground clutter (GC) and anomalous propagation (AP). GC/AP can be effectively removed using a machine learning classifier trained with radar retrieved correlation coefficient, velocity, and spectrum width. We find a power‐law best describes the relationship between radar products and FRP for multiple fires combined (0.67–0.76 R²). Radar‐based FRP estimates can be used to fill gaps in satellite FRP created by cloud cover and show great potential to overcome satellite FRP biases occurring during extreme fire events. 

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4. Meta‐analytical evidence for frequency‐dependent selection across the tree of life

   https://doi.org/10.1111/ele.14477  

   Gómez‐Llano, Miguel; Bassar, Ronald D; Svensson, Erik I; Tye, Simon P; Siepielski, Adam M (August 2024, Ecology Letters)

   Abstract Explaining the maintenance of genetic variation in fitness‐related traits within populations is a fundamental challenge in ecology and evolutionary biology. Frequency‐dependent selection (FDS) is one mechanism that can maintain such variation, especially when selection favours rare variants (negative FDS). However, our general knowledge about the occurrence of FDS, its strength and direction remain fragmented, limiting general inferences about this important evolutionary process. We systematically reviewed the published literature on FDS and assembled a database of 747 effect sizes from 101 studies to analyse the occurrence, strength, and direction of FDS, and the factors that could explain heterogeneity in FDS. Using a meta‐analysis, we found that overall, FDS is more commonly negative, although not significantly when accounting for phylogeny. An analysis of absolute values of effect sizes, however, revealed the widespread occurrence of modest FDS. However, negative FDS was only significant in laboratory experiments and non‐significant in mesocosms and field‐based studies. Moreover, negative FDS was stronger in studies measuring fecundity and involving resource competition over studies using other fitness components or focused on other ecological interactions. Our study unveils key general patterns of FDS and points in future promising research directions that can help us understand a long‐standing fundamental problem in evolutionary biology and its consequences for demography and ecological dynamics. 

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5. Upscaling Soil Organic Carbon Measurements at the Continental Scale Using Multivariate Clustering Analysis and Machine Learning

   https://doi.org/10.1029/2023JG007702  

   Wang, Zhuonan; Kumar, Jitendra; Weintraub‐Leff, Samantha R.; Todd‐Brown, Katherine; Mishra, Umakant; Sihi, Debjani (February 2024, Journal of Geophysical Research: Biogeosciences)

   Abstract Estimates of soil organic carbon (SOC) stocks are essential for many environmental applications. However, significant inconsistencies exist in SOC stock estimates for the U.S. across current SOC maps. We propose a framework that combines unsupervised multivariate geographic clustering (MGC) and supervised Random Forests regression, improving SOC maps by capturing heterogeneous relationships with SOC drivers. We first used MGC to divide the U.S. into 20 SOC regions based on the similarity of covariates (soil biogeochemical, bioclimatic, biological, and physiographic variables). Subsequently, separate Random Forests models were trained for each SOC region, utilizing environmental covariates and SOC observations. Our estimated SOC stocks for the U.S. (52.6 ± 3.2 Pg for 0–30 cm and 108.3 ± 8.2 Pg for 0–100 cm depth) were within the range estimated by existing products like Harmonized World Soil Database, HWSD (46.7 Pg for 0–30 cm and 90.7 Pg for 0–100 cm depth) and SoilGrids 2.0 (45.7 Pg for 0–30 cm and 133.0 Pg for 0–100 cm depth). However, independent validation with soil profile data from the National Ecological Observatory Network showed that our approach (R² = 0.51) outperformed the estimates obtained from Harmonized World Soil Database (R² = 0.23) and SoilGrids 2.0 (R² = 0.39) for the topsoil (0–30 cm). Uncertainty analysis (e.g., low representativeness and high coefficients of variation) identified regions requiring more measurements, such as Alaska and the deserts of the U.S. Southwest. Our approach effectively captures the heterogeneous relationships between widely available predictors and the current SOC baseline across regions, offering reliable SOC estimates at 1 km resolution for benchmarking Earth system models. 

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