East Antarctica (EA) experienced an unprecedented atmospheric river (AR)-driven heatwave in March 2022. Using high-resolution model simulations, this study investigates how atmospheric moisture (Q) and sea surface temperature (SST) influence AR development and associated surface impacts through latent heat exchange and circulation evolutions. A 20% increase in Q and an ~1.3°C increase in SST over the target region enhanced area-averaged precipitation over EA by up to 10.25% and 17.23%, respectively, while amplifying Dome C warming by up to 5.38°C and 3.95°C. However, the responses are nonlinear, with AR strength and precipitation responding more strongly to reduced Q and enhanced SST. Increased Q accelerates blocking development and AR intensification, shifting the AR eastward and enhancing oceanic precipitation. In contrast, SST warming enhances evaporation and moisture retention, producing a delayed but stronger precipitation response. Temperature changes show strong spatial variability among sensitivity experiments, likely due to uncertainties in cloud radiative effects.

We present a generative modeling approach for nowcasting infrared (IR) brightness temperatures (Tb) from geostationary satellite observations (~ 10.8 μm) that couples a denoising diffusion probabilistic model with a 3D U-Net backbone. Using SEVIRI observations, the model ingests six hours of IR history and produces six-hour nowcasts at 15-min resolution. Deterministic evaluation is performed on an independent July–September 2022 test set and benchmarked against 3D U-Net, ConvLSTM, and Optical Flow extrapolation baselines. The diffusion model enhances prediction accuracy, yielding lower errors and higher correlation than all baselines across most forecast lead times, with a persistent advantage through the 2-hour lead time and reduced but still evident gains at longer leads. We complement the statistical assessments with a perceptual and a probabilistic diagnostic; diffusion achieves the highest SSIM at all leads and the lowest CRPS among all models (CRPS generates MAE for deterministic baselines). RAPSD analysis shows improved retention of high-frequency variance relative to deep learning baselines while avoiding the texture-only limitations of Optical Flow. Spatial maps demonstrate that our diffusion model significantly outperforms the baselines, indicating smaller errors over the study region. Two case studies show coherent structures in Tb forecasts, sharper gradients, and improved localization of evolving cold features. Overall, coupling diffusion with 3D U-Net delivers more structurally faithful satellite nowcasts than traditional extrapolation and deep learning baselines, particularly at short to intermediate leads.

Atmospheric rivers are narrow bands of water vapor transport and serve as key drivers of water supplies and flood hazards in mid-latitude regions. The atmospheric river scale supports early-warning communication by ranking events from potentially beneficial to hazardous based on atmospheric forcing represented by water vapor transport magnitude and duration. However, the scale does not consider land-surface conditions that can influence how precipitation derived from water vapor translates into streamflow and flood hazards. Analyzing atmospheric river landfalls across catchments in California and central Chile, we show that divergences between atmospheric river rank and flood response are primarily explained by pre-existing soil moisture conditions. Based on this insight, we develop a simple modification to the atmospheric river scale that nearly doubles the scale's correspondence with peak streamflow and increases the number of flood-generating atmospheric rivers classified as hazardous by more than 30%. These findings demonstrate that incorporating land-surface conditions can enhance early-warning hazard classification tools.

Heavy precipitation in Colorado (CO) is key to water resources, and the presence or absence of a few strong storms can make or break the yearly snowpack that delivers water to four major river basins. However, predicting precipitation in CO is challenging because it has high spatial and temporal variability. Atmospheric rivers (ARs) are one type of storm that results in a large fraction of extreme precipitation in the western U.S. and lends itself to improved forecasts over the region. Extensive knowledge of AR frequency, intensity, impacts, and key meteorological processes has been developed for U.S. West Coast landfalling ARs; however, relatively limited research has examined AR characteristics further inland, particularly for Colorado (CO), where high and complex topography, as well as the distance from the coast, complicate attempts to track ARs, AR-derived moisture, and AR-related impacts. Previous research efforts attributing precipitation to ARs based on their spatial footprint have yielded less than 30% of cool-season precipitation in CO as related to ARs. However, a large volume of anecdotal evidence suggests that ARs play a larger role in CO precipitation. To quantify this, we used trajectory-based methods to quantify the contribution of landfalling ARs to top-decile precipitation in subbasins throughout CO. Moisture sourced from landfalling ARs penetrates inland along relatively low-elevation corridors through the Interior West, and exhibits substantial geographic and interannual variability. Using the backward trajectory approach, we found that landfalling ARs contribute 21–78% of western CO’s top-decile cool season precipitation. Most of the AR-related precipitation across western CO during the cool-season is sourced from landfalling ARs near Southern California, the Baja Peninsula, and the Pacific Northwest. These results indicate a larger role for ARs in CO weather and hydroclimate than previous research suggests and highlight the importance of AR representation in forecast models to improve predictability of precipitation in CO.

Access to high-quality, high-resolution, near-real-time precipitation data is essential for hydrological and meteorological research and disaster response. Traditional tools such as rain gauges and radar networks, though effective, are limited by sparse coverage in remote areas and radar constraints such as beam blockage and increasing beam height with range, which reduce near-surface accuracy. Satellite observations address these challenges by providing global coverage with fine spatial and temporal resolution. Many precipitation products combine geosynchronous thermal infrared (IR) and passive microwave (PMW) data. PMW sensors offer detailed atmospheric profiles but are restricted to infrequent overpasses and increasing reliance on smaller satellites with higher-frequency channels, which are less sensitive to liquid precipitation. In contrast, IR sensors provide consistent, high-frequency global observations, making them valuable for near-real-time estimation. Recent advances in deep learning, particularly convolutional neural networks (CNNs), have further improved satellite precipitation retrievals. This study introduces Precipitation Estimation from Remotely Sensed Information Using Artificial Neural Networks (PERSIANN)-U-Net (PU-Net or PERSIANN V3), a quasi-global algorithm covering 60 degrees N-60 degrees S that combines IR data, monthly climatology, and the U-Net architecture to produce half-hourly precipitation estimates at 0.04 degrees resolution. The product is evaluated against Hydro Estimator (HE), Integrated Multi-satellitE Retrievals for Global Precipitation Measurement (IMERG), and PERSIANN Dynamic Infrared-Rain Rate (PDIR-Now) for 2022-23. Results show that PU-Net closely matches its training target, IMERG V07 Final, at the global scale, and its performance is further evaluated against Stage IV as a reference over contiguous United States (CONUS). Training PU-Net on IMERG (2016-21) leverages a high-quality, integrated PMW-IR-gauge precipitation product while developing an IR-based framework not reliant on PMW availability. By operating on a single global image, PU-Net avoids tile partitioning and blending steps, reducing edge discontinuities, and produces more spatially consistent precipitation fields across hemispheres.

Intermittent rivers and ephemeral streams (IRES) constitute a large fraction of global river networks, provide important ecosystem services, and are increasing in number with climate change. Yet, observing stage and calculating discharge in IRES can be technologically and methodologically challenging. To address this problem, we develop a method to classify relative stage categories from field camera imagery, creating a time series of categorical flow states without the need for direct stage measurements. Specifically, we employ a Logistic Regression model to classify conditions of no water, low water levels, or high water levels for an ephemeral stream located in the upper Russian River watershed of California (US). We trained our algorithm using hourly field camera images from 2017-2023, and validated the image classifications with 15 min continuous stage observations. We then used image classifications to perform quality control on the continuous stage time series, which allowed us to identify when the stream was dry and when the sensor malfunctioned. Next, we compared the image classifications to publicly accessible modeled discharge from the NOAA National Water Model CONUS Retrospective Dataset. We discuss how in-situ monitoring including field cameras and the classification of field camera imagery, combined with surface meteorology and soil moisture observations, provides detailed hydrologic information important for understanding how climate affects IRES. Because the image classification approach is transferable to other ephemeral stream sites equipped only with field cameras, this methodology provides a low-cost option for observing relative stage on sparsely-measured IRES that can augment existing hydrologic modeling used by water managers.

Abstract Recent decades have seen record-high temperatures on the Antarctic Peninsula (AP) due to combined atmospheric rivers (ARs) and föhn warming. While ARs frequently enhance föhn, not all events cause surface warming over the entire Larsen C Ice Shelf (LCIS). Using high-resolution Polar WRF simulations, we examine the relationship between ARs and föhn over the AP during austral summers and identify four distinct AR shapes associated with föhn-induced surface warming over the LCIS: zonal-perpendicular, zonal-like, convex, and concave. Zonal-like ARs associated with coupled low-high-pressure systems and convex ARs linked to blocking highs produce strong föhn warming across the entire LCIS, primarily affecting its northern and southern sectors, respectively. In contrast, zonal-perpendicular and concave ARs generate moderate-to-weak warming, owing to either weaker AR intensity or AR curvature. Although downward shortwave radiation dominates surface warming, enhanced moisture suppresses its increase from föhn-induced cloud clearance while enhancing downward longwave radiation near mountain gaps. Sensible heat flux also contributes substantially along the mountain foothills. As ARs intensify under climate change, their interaction with föhn over the AP can critically influence the future stability of coastal ice shelves.

Water systems across California and the western United States face intensifying pressure from increasing hydrologic variability, declining snowpack, groundwater regulation, and sustained urban, agricultural, and environmental demands. Forecast informed reservoir operations (FIRO) is an emerging national adaptation strategy that leverages advances in hydrometeorological forecasting to allow more flexible reservoir operations, enabling operators to safely retain additional water while maintaining or enhancing flood risk management. In California, the potential economic value of the urban and agricultural water-supply benefits of FIRO implementation is evaluated for 20 major reservoirs identified by the U.S. Army Corps of Engineers as having no prohibitive barriers to further viability assessment. Collectively, these reservoirs represent over 20.1 million acre-feet of capacity. Using historical storage records, we estimate the volume of water that could have been retained under FIRO, yielding an increase in water availability of approximately 315 000 acre-feet per year, up to ≈500 000 acre-feet in wet years. Under these assumptions, we project that FIRO could generate approximately $145 million per year in direct economic benefits to urban and agricultural water users. Estimated annual benefits are highest in hydrologically moderate water years ($206 million) and lower in dry and wet years ($100 million and $134 million, respectively). Estimates are supported by two separate valuation frameworks combined with a statistical analysis of the Nasdaq Veles NQH2O California Water Index. These results account only for direct urban and agricultural water-supply benefits and exclude potential gains from flood risk reduction, environmental flows, hydropower, and recreation, and therefore represent only one portion of FIRO’s total economic value.

In March 2019, a strong atmospheric river (AR) originating from the Gulf of Mexico transported abundant moisture inland and fueled a record-breaking bomb cyclone in Colorado, resulting in widespread winter weather hazards across several states. Experimental model simulations and trajectory analysis indicate that mid-tropospheric latent heat release (LHR) coincided with the strength of the warm conveyor belt and played a key role in the deepening of the cyclone. The LHR promoted the generation of a lower tropospheric positive potential vorticity (PV) anomaly and a stronger low-level cyclonic circulation, enhancing the cyclone, low-level jet stream, and associated water vapor transport. Additionally, it generated an upper tropospheric negative PV anomaly and strong upper-level anticyclonic circulation, influencing the structure of the trough-ridge couplet and the associated Rossby wave. Reductions in the initial intensity of the AR and disallowing LHR both weakened the cyclone. However, disallowing LHR significantly disturbed the synoptic-scale structure of the storm and embedded Rossby wave, resulting in a stronger impact. Thus, the reduction of diabatic PV generation, under the influence of AR activities, was crucial in the explosive intensification of the cyclone. Additionally, the reductions in AR intensity and the magnitude of cyclone weakening did not show a linear relationship. Few studies have explored interactions between ARs and continental cyclones, and this paper highlights the need for further research on AR-associated extreme weather events inland.

Atmospheric rivers (ARs) are filamentary structures within the atmosphere that account for a substantial portion of poleward moisture transport and play an important role in Earth's hydroclimate. However, there is no one quantitative definition for what constitutes an atmospheric river, leading to uncertainty in quantifying how these systems respond to global change. This study seeks to better understand how different AR detection tools (ARDTs) respond to changes in climate states utilizing single-forcing climate model experiments under the aegis of the Atmospheric River Tracking Method Intercomparison Project (ARTMIP). We compare a simulation with an early Holocene orbital configuration and another with CO2 levels of the Last Glacial Maximum to a preindustrial control simulation to test how the ARDTs respond to changes in seasonality and mean climate state, respectively. We find good agreement among the algorithms in the AR response to the changing orbital configuration, with a poleward shift in AR frequency that tracks seasonal poleward shifts in atmospheric water vapor and zonal winds. In the low CO2 simulation, the algorithms generally agree on the sign of AR changes, but there is substantial spread in their magnitude, indicating that mean-state changes lead to larger uncertainty. This disagreement likely arises primarily from differences between algorithms in their thresholds for water vapor and its transport used for identifying ARs. These findings warrant caution in ARDT selection for paleoclimate and climate change studies in which there is a change to the mean climate state, as ARDT selection contributes substantial uncertainty in such cases.

The Southeastern United States (SEUS) region has lower extreme quantitative precipitation forecast (QPF) skill compared to the northeastern or western United States. Previous studies have reported that the extreme precipitation events (EPEs) with high integrated vapor transport (IVT) have higher QPF skill than those with low IVT in the SEUS. We hypothesize that this extreme QPF skill is influenced by different storm types, such as atmospheric rivers (ARs), mesoscale convective systems (MCSs), and tropical cyclones (TCs), occurring within various synoptic patterns. This study investigates pattern‐wise QPF skill and the contribution of storm types to EPEs in the SEUS. Six synoptic patterns associated with EPEs were identified from 2001 to 2019. These patterns exhibited a distinct seasonality: three occurred in the cool season, two in the warm season, and one in the transition season. Approximately 35% of the EPEs in the cool season, 24% in the transition season, and 29% in the warm season are associated with coincident ARs and MCSs. Pattern‐wise QPF skill derived from the GEFS reforecast dataset illustrated that the cool season pattern, characterized by high IVT and frequency of ARs, has higher QPF skill. In contrast, the warm season pattern with high convective available potential energy and integrated water vapor has lower QPF skill across multiple lead times. In addition, patterns with higher frequency of ARs or coincident ARs and MCSs have better predictability than those with isolated MCSs. These results provide insight into the contribution of storm types to EPEs and their predictability in the SEUS.

Atmospheric rivers (ARs) are responsible for much of the wintertime precipitation over the U.S. West Coast. Their associated precipitation can be beneficial, by replenishing water resources, and can be detrimental, by triggering flooding and landslides. Therefore, accurate forecasts of ARs are crucial for water management and hazard preparedness. To help bring about improvements in downstream precipitation forecasts in the western United States, the AR Reconnaissance (AR Recon) field campaign has been set up, which deploys research aircraft to sample essential upstream atmospheric structures using dropsondes and airborne instruments. This study examines the impacts of AR Recon dropsonde data on precipitation forecasts over the western United States in the European Centre for Medium-Range Weather Forecasts (ECMWF) Integrated Forecasting System (IFS), for the AR Recon field seasons of 2022/23 and 2023/24. Observing-system experiments demonstrate that dropsonde observations bring moderate, yet statistically significant improvements at various lead times. Over California, the additional observations enhance precipitation forecasts at lead times of 24-36 hours and 72-96 hours, which is likely linked to aircraft operating from two bases, that is, the U.S. West Coast and Hawaii. For the Pacific Northwest region, dropsonde observations mainly improve forecast skill at lead times of 48-72 hours. The timing differences in improvements between the two regions in 2022/23 are associated with the southward propagation of error reductions in the large-scale flow. The smaller dropsonde impacts in 2023/24 compared to 2022/23 can be partly explained by the long data assimilation window used in 2023/24. The dropsonde impacts in the Global Forecast System (GFS) are consistent with those in IFS, although the impacts are larger in the GFS. Despite the overall benefits of dropsonde assimilation, slight degradation is also observed at certain lead times, such as 60 hours over California for two seasons combined, warranting further investigation.

In spite of forecasts for anomalous dryness based on the canonical La Niña signal, Water Years 2011, 2017, and 2023 brought copious precipitation to California and the Southwestern United States (SWUS). Although El Niño–Southern Oscillation (ENSO) is the main source of seasonal precipitation predictability for the region, outstanding Atmospheric River (AR) activity produced the unexpected regional wetness in each of these heretical water years (WYs). We define heretical WYs as those that result in precipitation anomalies that oppose those expected based on ENSO canon. We assess the contribution of ARs and other storms to these WYs, finding that heretical La Niña/El Niño WYs were characterized by anomalously robust/deficient AR activity. In California, precipitation accumulation during the heretical La Niña WYs was comparable to or even exceeded that observed during the exceedingly wet WY1998—the textbook canonical El Niño year. Our findings indicate a weaker/stronger relationship between ENSO and AR/non-AR precipitation, primarily driven by storm frequency. Although ARs can disrupt the ENSO-precipitation signal, ENSO still influences the frequency of AR precipitation in the southwestern U.S. desert, the region influenced by ARs that make landfall in Baja California, Mexico. These results highlight the complexity of ENSO's impact on precipitation in the Western US and underscore the need for a nuanced understanding of ENSO’s influence on ARs to improve seasonal precipitation prediction.

California experienced a historic run of nine consecutive landfalling atmospheric rivers (ARs) in three weeks' time during winter 2022/23. Following three years of drought from 2020 to 2022, intense landfalling ARs across California in December 2022-January 2023 were responsible for bringing reservoirs back to historical averages and producing damaging floods and debris flows. In recent years, the Center for Western Weather and Water Extremes and collaborating institutions have developed and routinely provided to end users peer-reviewed experimental seasonal (1-6 month lead time) and subseasonal (2-6 week lead time) prediction tools for western U.S. ARs, circulation regimes, and precipitation. Here, we evaluate the performance of experimental seasonal precipitation forecasts for winter 2022/23, along with experimental subseasonal AR activity and circulation forecasts during the December 2022 regime shift from dry conditions to persistent troughing and record AR-driven wetness over the western United States. Experimental seasonal precipitation forecasts were too dry across Southern California (likely due to their overreliance on La Nina), and the observed above-normal precipitation across Northern and Central California was underpredicted. However, experimental subseasonal forecasts skillfully captured the regime shift from dry to wet conditions in late December 2022 at 2-3 week lead time. During this time, an active MJO shift from phases 4 and 5 to 6 and 7 occurred, which historically tilts the odds toward increased AR activity over California. New experimental seasonal and subseasonal synthesis forecast products, designed to aggregate information across institutions and methods, are introduced in the context of this historic winter to provide situational awareness guidance to western U.S. water managers.

Atmospheric rivers (ARs) are the primary mechanism for transporting water vapor from low latitudes to polar regions, playing a significant role in extreme weather in both the Arctic and Antarctica. With the rapidly growing interest in polar ARs during the past decade, it is imperative to establish an objective framework quantifying the strength and impact of these ARs for both scientific research and practical applications. The AR scale introduced by Ralph et al. (2019) ranks ARs based on the duration of AR conditions and the intensity of integrated water vapor transport (IVT). However, the thresholds of IVT used to rank ARs are selected based on the IVT climatology at middle latitudes. These thresholds are insufficient for polar regions due to the substantially lower temperature and moisture content. In this study, we analyze the IVT climatology in polar regions, focusing on the coasts of Antarctica and Greenland. Then we introduce an extended version of the AR scale tuned to polar regions by adding lower IVT thresholds of 100, 150, and 200 kg m-1 s-1 to the standard AR scale, which starts at 250 kg m-1 s-1. The polar AR scale is utilized to examine AR frequency, seasonality, trends, and associated precipitation and surface melt over Antarctica and Greenland. Our results show that the polar AR scale better characterizes the strength and impacts of ARs in the Antarctic and Arctic regions than the original AR scale and has the potential to enhance communication across observational, research, and forecasting communities in polar regions.

This study introduces a deep learning (DL) scheme to generate reliable and skillful probabilistic quantitative precipitation forecasts (PQPFs) in a postprocessing framework. Enhanced machine learning model architecture and training mechanisms are proposed to improve the reliability and skill of PQPFs while permitting computationally ef fi cient model fi tting using a short training dataset. The methodology is applied to postprocessing of 24-h accumulated PQPFs from an ensemble forecast system recently introduced by the Center for Western Weather and Water Extremes (CW3E) and for lead times from 1 to 6 days. The ensemble system was designed based on a high-resolution version of the Weather Research and Forecasting (WRF) Model, named West-WRF, to produce a 200-member ensemble in near - real time (NRT) over the western United States during the boreal cool seasons to support Forecast-Informdayed Reservoir Operations (FIRO) and studies of prediction of heavy-to-extreme events. Postprocessed PQPFs are compared with those from the raw West-WRF ensemble, the operational Global Ensemble Forecast System version 12 (GEFSv12), and the ensemble from the European Centre for Medium-Range Weather Forecasts (ECMWF). As an additional baseline, we provide PQPF veri fi cation metrics from a recently developed neural network postprocessing scheme. The results demonstrate that the skill of postprocessed forecasts signi fi cantly outperforms PQPFs and deterministic forecasts from raw ensembles and the recently developed algorithm. The resulting PQPFs broadly improve upon the reliability and skill of baselines in predicting heavy-to-extreme precipitation (e.g., .75 mm) across all lead times while maintaining the spatial structure of the high-resolution raw ensemble.

An important source of errors in predicting landfalling atmospheric rivers (ARs) and their associated extreme precipitation and streamflow are inaccuracies in model initial conditions in ARs offshore. These inaccuracies are particularly evident in the marine boundary layer (MBL) where the tendency of ARs to transport warm air poleward over progressively cooler sea surface temperatures (SSTs) generates a stable MBL (SMBL). The SMBL's vertical structure and key modulating processes are documented using >1000 dropsondes along 99 transects of ARs from the AR reconnaissance and CalWater field campaigns. The SMBL depth, modulated by sensible heat loss to the ocean, is typically 300-800 m in the AR core, with vertical wind shears ranging from 5 to 50 m s(-1) km(-1), representing a highly variable decoupling of the AR aloft from conditions at the ocean surface. Simulated backward air parcel trajectories originating from dropsonde locations within the AR core are used to calculate the 24-h change in SST experienced by an air parcel (DSST24) beneath each AR. The DSST24 varies from -13 degrees to +2 degrees C and is directly related to the strength of the AR and its orientation relative to the SST gradient. The DSST24, therefore, distinguishes weak and strong decoupling regimes (WDR and SDR). In SDR cases, relative to WDR cases, the SMBL is characterized by greater sensible heat loss to the ocean, as well as stronger static stability, vertical wind shear, low-level jet, and horizontal water vapor transport. In SDR cases, the SMBL is deeper in the core than in adjacent warm and cold sectors.

Between 15 and 19 March 2022, East Antarctica experienced an exceptional heat wave with widespread 30 degrees-40 degrees C temperature anomalies across the ice sheet. This record-shattering event saw numerous monthly temperature records being broken including a new all-time temperature record of -9.4 degrees C on 18 March at Concordia Station despite March typically being a transition month to the Antarctic coreless winter. The driver for these temperature extremes was an intense atmospheric river advecting subtropical/midlatitude heat and moisture deep into the Antarctic interior. The scope of the temperature records spurred a large, diverse collaborative effort to study the heat wave's meteorological drivers, impacts, and historical climate context. Here we focus on describing those temperature records along with the intricate meteorological drivers that led to the most intense atmospheric river observed over East Antarctica. These efforts describe the Rossby wave activity forced from intense tropical convection over the Indian Ocean. This led to an atmospheric river and warm conveyor belt intensification near the coastline, which reinforced atmospheric blocking deep into East Antarctica. The resulting moisture flux and upper-level warm-air advection eroded the typical surface temperature inversions over the ice sheet. At the peak of the heat wave, an area of 3.3 million km(2) in East Antarctica exceeded previous March monthly temperature records. Despite a temperature anomaly return time of about 100 years, a closer recurrence of such an event is possible under future climate projections. In Part II we describe the various impacts this extreme event had on the East Antarctic cryosphere. SIGNIFICANCE STATEMENT: In March 2022, a heat wave and atmospheric river caused some of the highest temperature anomalies ever observed globally and captured the attention of the Antarctic science community. Using our diverse collective expertise, we explored the causes of the event and have placed it within a historical climate context. One key takeaway is that Antarctic climate extremes are highly sensitive to perturbations in the midlatitudes and subtropics. This heat wave redefined our expectations of the Antarctic climate. Despite the rare chance of occurrence based on past climate, a future temperature extreme event of similar magnitude is possible, especially given anthropogenic climate change.