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During this work, we adapted the BOSS microphysics scheme, originally formulated for rain-only, to include all warm-phase microphysical processes of cloud and rain, in particular the processes that mediate between these two categories, for example autoconversion and accretion.
The California-Nevada 1997 New Year’s flood was an atmospheric river (AR)-driven rain-on-snow (RoS) event and remains the costliest in their history. The joint occurrence of saturated soils, rainfall, and snowmelt generated inundation throughout northern California-Nevada. Although AR RoS events are projected to occur more frequently with climate change, the warming sensitivity of their flood drivers across scales remains understudied. We leverage the regionally refined mesh capabilities of the Energy Exascale Earth System Model (RRM-E3SM) to recreate the 1997 New Year’s flood with horizontal grid spacings of 3.5 km across California, with forecast lead times of up to 4 days, and across six warming levels ranging from pre-industrial conditions to +3.5° C. We describe the sensitivity of the flood drivers to warming including AR duration and intensity, precipitation phase, intensity and efficiency, snowpack mass and energy changes, and runoff efficiency. Our findings indicate current levels of climate change negligibly influence the flood drivers. At warming levels ≥ 1.7° C, AR hazard potential increases, snowpack nonlinearly decreases, antecedent soil moisture decreases (except where the snowline retreats), and runoff decreases (except in the southern Sierra Nevada where antecedent snowpack persists). Storm total precipitation increases, but at rates below warming-induced increases in saturation-specific humidity. Warming intensifies short-duration, high-intensity rainfall, particularly where snowfall-to-rainfall transitions occur. This study highlights the nonlinear tradeoffs in 21st-century RoS flood hazards with warming and provides water management and infrastructure investment adaptation considerations.
The severe impacts of rain‐on‐snow (ROS) extreme events have been widely recognized and studied. However, unlike hydrological processes, flood inundation dynamics and the relative contribution of rainfall and snowmelt during ROS events remain under‐investigated. We diagnosed and documented sub‐kilometer spatio‐temporal dynamics of flood inundation during the 2017 California ROS events, simulated by a 2‐dimensional hydrodynamic model, River Dynamical Core (RDycore). RDycore shows good performance in capturing fine‐scale flood inundation dynamics against gauge measurements (with a median correlation coefficient of 0.81) and satellite observations. On top of rainfall, snowmelt not only increases the mean maximum inundation depth (12.0%–25.1%), but also expands the total flooded area (19.9%–31.9%) and prolongs the mean flood duration (3.4%–7.1%) across the events. Our findings offer an explicit and accurate picture of when and where ROS flooding could occur and how snowmelt increases flood hazard, valuable for risk assessment and infrastructure planning.
Accurate flood estimation in snow-dominated and high-latitude regions remains challenging due to complex interactions among rainfall, snowmelt, and rain-on-snow (ROS) processes, which are not captured in conventional precipitation-based intensity-duration-frequency (PREC-IDF) curves. This study evaluates the Next-Generation IDF (NG-IDF) framework, an extension of PREC-IDF that incorporates total water available for runoff (precipitation minus changes in snow water equivalent), in two contrasting Alaskan watersheds of Department of Defense (DoD) significance: the Little Chena River Basin (LCRB) in interior Alaska, a tributary of the Chena River that flows through Fort Wainwright and near Eielson Air Force Base, and the Upper Ship Creek Basin (USCB), which drains Joint Base Elmendorf-Richardson near Anchorage. Using long-term SNOTEL observations and event-based rainfall-runoff modeling, NG-IDF and PREC-IDF flood estimates were compared against observation-based flood frequency analyses. Results show that NG-IDF consistently reduces flood-estimation bias by 15–20% relative to PREC-IDF, particularly for snowmelt- and ROS-dominated events. In the interior LCRB, permafrost conditions can substantially amplify flood responses during snowmelt events, an effect not explicitly represented in standard design tools. These findings demonstrate that NG-IDF provides a more physically consistent and transferable framework for flood estimation in cold regions, with potential relevance to mission-critical DoD installation resilience. Projected increases in ROS frequency and permafrost degradation across Alaska further emphasize the need to integrate physics-based hydrologic models that explicitly represent snow and permafrost processes to enhance design resilience and operational readiness for military and other critical infrastructure.
The Micro Rain Radar Pro (MRR-PRO) is a vertically pointing Ka-band Doppler radar designed to capture the fine-scale structure and evolution of precipitation. By recording the full Doppler spectrum at high temporal and spatial resolution, the MRR-PRO provides insight into both hydrometeor fall velocities and precipitation microphysics. From these spectra, key moments—reflectivity, mean Doppler velocity, spectral width, and rainfall rate—are derived and stored alongside the raw spectral data in CF/Radial 1.4-compliant files. Deployed at the Argonne Testbed for Multiscale Observational Studies (ATMOS) since November 2024, the MRR-PRO delivers vertical profiles at 70 m range resolution extending up to 4.5 km above ground level. These observations enable detailed analyses of precipitation type, intensity, and vertical structure, supporting process-level studies of cloud and precipitation dynamics in diverse weather regimes.
This data package contains the data and code to use and run the Water Tracer enabled version of the Weather Research and Forecasting Hydrologic model (WT-WRF-Hydro) with the Sequential Precipitation Input Tagging (SPIT) framework. It is associated with the publication “Shifts in Rain-Snow Partitioning Drive Faster Water Transit Times in the US Pacific Northwest” published in Scientific Reports (Butler et al., 2026; https://doi.org/10.1038/s41598-026-46539-1). We use the Continental U.S. (CONUSII; Rasmussen et al., 2021) dataset to force the model with an historical climate (2006–2013) and a future climate (2086–2093) with a representative carbon pathway (RCP) 8.5 scenario. We use the model to calculate water transit times in five headwater catchments within the U.S. Pacific Northwest. We also show key hydrologic and environmental variables that affect water transit times and changes in the future. Finally, we use observed data to validate the model such as stream water isotopes, snowpack characteristics, and stream discharge. For details on how to navigate data packages generated by this project, see https://data.ess-dive.lbl.gov/portals/PNNLRiverCorridorSFA/About. In addition to a readme, this data package also includes a file-level metadata (FLMD) file that describes each file and a data dictionary (DD) that describes all column/row headers and variable definitions. The data package consists of 11 folders: (1) "Figures" contains the exported figures used in the manuscript; (2) "Model_Isotope_Date" contains the WT-WRF-Hydro isotope date used in model validation; (3) “Model_Outputs_Future” contains the WT-WRF-Hydro future climate outputs; (4) “Model_Outputs_Historical” contains the WT-WRF-Hydro historical climate outputs; (5) “Model_Outputs_Weights_Areas” contains the WT-WRF-Hydro weights per catchment used to calculate water transit times and isotopes in stream water; (6) “MODIS_data_scripts” contains data used to validate snow conditions in the study area; (7) “Observed_Flow_Data” contains the observed streamflow data used in model validation; (8) “Observed_Isotope_Data” contains the observed stream water isotope data used in model validation; (9) “Scripts” contains the Python scripts used to general results and the figures; (10) “Statistic_Outputs” contains the water transit time statistical outputs reported in this manuscript; (11) “Validation_SNOTEL” contains the SNOTEL data used in model validation. The files in this data package have the following file extensions: .tif, .txt, .csv, .pdf, .py, .jpg, and .png.
This dataset contains the Micro Rain Radar data that have been reviewed by the mentor and calibrated against the Parsivel on the barge.
Abstract. Rain-on-snow (RoS) events in regions of ephemeral snowpack – such as the northeastern United States – can be key drivers of cool-season flooding. We describe an automated algorithm for detecting basin-scale RoS events in gridded climate data by generating an area-averaged time series and then searching for periods of concurrent precipitation, surface runoff, and snowmelt exceeding predefined thresholds. When evaluated using historical data over the Susquehanna River basin (SRB), the technique credibly finds RoS events in published literature and flags events that are followed by anomalously high streamflow as measured by gauge data along the river. When comparing four different datasets representing the same 21-year period, we find large differences in RoS event magnitude and frequency, primarily driven by differences in estimated surface runoff and snowmelt. Using dataset-specific thresholds improves agreement between datasets but does not account for all discrepancies. We show that factors such as meteorological forcing and coupling frequency, as well as choice of land surface model, play roles in how data products capture these compound extremes and suggest care is to be taken when climate datasets are used by stakeholders for operational decision-making.
1-minute merged tipping bucket rain gauge and barometer measurements at tower base
Anthropogenic climate change leads to increased precipitation intensity and exacerbated droughts in California, challenging the reliability and drought resiliency of water supply. Storing floodwater underground via managed aquifer recharge can mitigate these effects through direct infiltration or streambed infiltration. Seasonally dry streams (arroyos) already play an important part in managing groundwater recharge to the Livermore basin (CA). Understanding how, when and where stormwater and arroyo water infiltrate is critical to effectively utilise this strategy. To track water from recent storms (water year 2022–2023, WY23) into the Livermore Valley Groundwater Basin, we analysed stable water isotopes (δ 18 O and δ 2 H) in combination with naturally occurring radioactive isotopic tracers, sulphur-35 ( 35 S, t ½ = 87 days) and tritium ( 3 H, t ½ = 12.3 years). By comparing measurements of δ 18 O, 35 S and 3 H in arroyos to precipitation and groundwater, we classified the relative age and identified source of recharge to 16 wells near two arroyos. Two wells contained water with recent recharge (from WY23) from local precipitation. One well had recent recharge from variable (precipitation and imported water) sources. One well contained imported water recharge. Three wells contained water from mixed recent and older (pre-WY23) waters, from local precipitation sources. Two wells contained recent recharge from local mine settling ponds. Seven wells had older recharge from local precipitation sources. This combination of isotopes allows us to delineate where local and imported water recharges in this highly managed basin and identify locations where managed aquifer recharge is contributing to rapid groundwater infiltration. Our combined interpretation of isotopic water ages and sources in the context of land use shows that local infiltration of precipitation in open spaces is an important recharge mechanism, in addition to the managed arroyo recharge. Finally, a broader familiarity with 35 S will enable more extensive research on the infiltration of urban floodwaters.
Direct detection is a powerful means of searching for particle physics evidence of dark matter (DM) heavier than about a GeV with o(kiloton) volume, low-threshold detectors. In many scenarios, some fraction of the DM may be boosted to large velocities enhancing and generally modifying possible detection signatures. We investigate the scenario where 100% of the DM is boosted at the Earth due to new attractive long-range forces. This leads to two main improvements in detection capabilities: (1) the large boost allows for detectable signatures of DM well below a GeV at large-volume neutrino detectors, such as DUNE, Super-K, Hyper-K, and JUNO, as possible DM detectors, and (2) the flux at the Earth’s surface is enhanced by a focusing effect. In addition, the model leads to a significant anisotropy in the signal with the DM flowing dominantly vertically at the Earth’s surface instead of the typical approximately isotropic DM signal. We develop the theory behind this model and also calculate realistic constraints using a detailed GENIE simulation of the signal inside detectors.
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Membraneless coacervate microdroplets have long been proposed as model protocells as they can grow, divide, and concentrate RNA by natural partitioning. However, the rapid exchange of RNA between these compartments, along with their rapid fusion, both within minutes, means that individual droplets would be unable to maintain their separate genetic identities. Hence, Darwinian evolution would not be possible, and the population would be vulnerable to collapse due to the rapid spread of parasitic RNAs. In this study, we show that distilled water, mimicking rain/freshwater, leads to the formation of electrostatic crosslinks on the interface of coacervate droplets that not only suppress droplet fusion indefinitely but also allow the spatiotemporal compartmentalization of RNA on a timescale of days depending on the length and structure of RNA. We suggest that these nonfusing membraneless droplets could potentially act as protocells with the capacity to evolve compartmentalized ribozymes in prebiotic environments.
Drizzle, a common feature of marine boundary layer clouds formed through collision coalescence, plays a key role in cloud microphysics and evolution. Yet, simultaneously retrieving cloud and drizzle properties from remote-sensing observations remains challenging because drizzle droplets often dominate radar signals, masking cloud contributions. The goal of the proposed research is to provide constraints for the process of autoconversion and accretion using ARM cloud measurements. Specifically, we provide concurrent retrievals of cloud and drizzle that allows users to derive corresponding autoconversion and accretion rates.
This dataset includes the reflectivity, estimated rainfall rate, liquid water content, and vertical velocity recorded by the MRR2 averaged over 30-second intervals.
This dataset includes the reflectivity, estimated rainfall rate, liquid water content, and vertical velocity recorded by the MRR2 averaged over 30 s intervals.
Red geysers are a population of massive (logM/M ⊙ ~ 10.5), quiescent galaxies with large-scale but weak, bisymmetric ionized gas outflows, interpreted as signatures of ongoing, low-level active galactic nucleus (AGN) feedback. We investigate the kinematics and prevalence of cool (T ∼ 100–1000 K) neutral gas traced by Na I D absorption, and its connection to galaxy environment and AGN activity. Using 140 red geyser galaxies from the Sloan Digital Sky Survey-IV Mapping Nearby Galaxies at Apache Point Observatory (or MaNGA), we measure spatially resolved velocities and dispersions via double-Gaussian fits to the Na I D doublet. We find that ∼70% of the cool gas is inflowing, with a median velocity of ∼47 km s −1 (∼10% of the expected freefall velocity), and that it also exhibits kinematically ordered motions with σ NaD /σ* ∼ 0.4. Additionally, the Na I D absorption is more prevalent in red geysers than in a matched control sample, with a higher detection fraction (63% versus 40%) and reservoir areas ∼1.6 times larger. Acceleration (∼1 Myr) and accretion (∼20 Myr) timescales indicate that the Na I D-absorbing clouds are likely young and short-lived. Another intriguing result is that radio-detected red geysers (30% of the sample) show inflowing gas reservoirs ∼7 times larger than in non-radio systems. Similarly, galaxies subject to environmental effects host inflowing gas reservoirs ∼2.7 times larger than isolated red geysers. We take this as evidence that galaxy environments play a key role in replenishing the cool gas reservoirs of red geysers, fueling central AGN activity, sustaining radio emission, and regulating long-term quiescence. These findings reveal that quiescent systems are governed by cycles of inflow, feedback, and regulation.