Fred Martin Ralph

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.

Landfalling Atmospheric Rivers (ARs) are crucial for the water resources on the U.S. West Coast, but can also cause hazardous weather and water events there. Yet, accurately predicting AR impacts on precipitation remains a challenge. This study investigates the influence of dropsonde data from AR Reconnaissance (AR Recon) on improving weather forecasts in winter 2022, specifically AR‐related precipitation forecasts for the U.S. West Coast. Using operational Global Forecast System version 16 (GFSv16) simulations with and without assimilated dropsonde observations, we evaluated the impact of dropsondes on forecasting key meteorological fields like geopotential height, horizontal wind and water vapor transport, and precipitation. The results demonstrate that assimilating dropsonde data can improve the forecast accuracy of landfalling AR characteristics and associated precipitation, although mixed results were obtained in water vapor transport forecasts. Forecast improvement was achieved for medium to strong AR events, especially when dropsondes targeted the AR cores, showing a large error reduction in integrated vapor transport (approximately 20 kg m ⁻¹ s ⁻¹ ) and precipitation (by >5 mm d ⁻¹ or ∼20%–40%) for strong AR conditions. The analysis indicates that the effectiveness of dropsonde data is influenced by AR characteristics, such as intensity and track, as well as the complex terrain of the U.S. West Coast. These findings highlight the value of targeted field campaigns in improving AR precipitation forecasts and underscore the need for continued research to address remaining uncertainties, ultimately enhancing water resource management and extreme weather preparedness on the West Coast.

Atmospheric rivers (ARs) are elongated structures in the atmosphere that transport water vapor from the tropics to mid‐ and high‐latitudes in highly episodic events. ARs are a major source of precipitation in the western United States and many other regions of the world and play a role in air‐sea exchanges of heat and freshwater in the subtropics. For the first time, an experiment was designed to observe (open) ocean changes during AR precipitation, using profiling floats in the northeast subtropical Pacific. Results show upper ocean freshening and cooling with AR precipitation (lasting several hours), especially when surface winds are low ( m/s). ARs contribute 70%–90 of the total winter precipitation in the subtropical northeast Pacific: a salt budget analysis (using ECCO) shows that precipitation over winter is the main driver of ocean freshening in the top 25–30 m and contributes by to the integrated changes in the top m.

Atmospheric rivers (ARs) have long been recognized as a major precipitation deliverer in western North America. Meanwhile, ARs in inland and east coast areas and their impacts beyond precipitation are starting to get recognized. The GOES‐16 satellite, with its longitudinal location, provides an opportunity for improved observational understanding of ARs across the Americas. Using lightning observations from the satellite and an AR database based on the MERRA‐2 reanalysis, this study investigates the influence of ARs on lightning and its seasonal and geographical variations. Compared to the annual AR frequency of 30% during lightning days in a number of locations. Compared to all wet days, mean flash density during AR wet days can be higher by a factor of >∼2 depending on the locations. Although long‐lived and traveling ARs have the strongest moisture transport, short‐lived ARs are associated with the highest flash density.

This study investigated four top‐decile precipitation regimes in Colorado (CO) using a k‐means clustering approach. Two cool‐season regimes featured northwesterly and southwesterly integrated vapor transport (IVT) impacting western CO, characterized by a high frequency of anomalous landfalling West Coast Atmospheric Rivers (ARs) that penetrated inland facilitated by an upstream trough over the Intermountain West. Another cool‐season regime with southeasterly IVT impacting eastern CO was characterized by anomalous Gulf of America ARs facilitated by a trough and cut‐off low over the Great Plains. In contrast, a warm‐season regime is influenced by IVT from Gulf of America ARs facilitated by the North Atlantic subtropical high and the North American Monsoon circulation, impacting southwestern and southeastern CO. Representative storm examples from each regime are highlighted to examine the processes and impacts of top‐decile precipitation. These regimes provide further opportunity to assess predictability associated with AR and non‐AR related precipitation extremes in CO.

Clouds play an important role in the Southern Ocean and Antarctic surface energy balance via their radiative effects and in surface mass balance via precipitation formation. Here, we use measurements at Escudero Station (62.2°S, 58.97°W) on King George Island, north of the Antarctic Peninsula, to characterize clouds and their effects on the surface incoming radiation between 2017 and 2023. These measurements are unique providing 7 years of simultaneous cloud and radiation measurements, including year‐round observations. Cloud measurements using a mini micropulse lidar showed that clouds are present 96% of the time with persistent low‐level supercooled liquid‐containing clouds: 86% of the lowest cloud bases are within the first 1 km. Liquid was present about 80% of the time, and most liquid was supercooled: cloud‐base temperatures were below 0°C for 82% of atmospheric columns classified as liquid‐containing. Combining pyranometer and pyrgeometer measurements with clear‐sky radiative transfer modeling, we find that the downward cloud radiative forcing is negative during October–March and positive during April–September. For clouds with base temperatures below 260 K, downward longwave cloud forcing is found to be lower for ice‐only clouds than for liquid‐containing clouds; however, at warmer temperatures, both ice‐only and liquid‐containing clouds exhibited similar radiative forcing. During strong atmospheric river (AR) events, when long corridors of moisture bring heat and precipitation, surface temperatures are found to be positively correlated with downward shortwave (DSW) cloud forcing in summer, indicating that weaker DSW cloud forcing is linked to higher summertime surface temperatures.

Atmospheric motion vectors (AMVs) represent horizontal wind derived by tracking cloud or water vapor features on successive satellite images. The launch of the Geostationary Operational Environmental Satellite‐R Series (GOES‐R), including GOES‐16 (GOES‐East) and GOES‐17 (GOES‐West), has significantly enhanced AMV data volume and geographic coverage over the contiguous United States (U.S.) and adjacent oceans. AMVs from GOES‐16/17 products can augment wind data in data‐sparse oceanic areas such as those frequented by atmospheric rivers (ARs). However, AMVs exhibit biases and uncertainties, especially due to height assignment issues, and there are fewer conventional data (e.g., radiosondes) to assess GOES‐17 AMVs over oceans. The AR Reconnaissance (AR Recon) samples ARs to improve forecast skill over the U.S. West and provides a unique opportunity to compare GOES‐17 AMVs. This study quantifies biases and uncertainties in GOES‐17 AMVs in the northeast Pacific using dropsondes from AR Recon, and assesses Global Forecast System (GFS) model wind analyses and background fields during AR events. Results for four representative AR cases show that GOES‐R AMVs improved wind data distribution compared to cases prior to GOES‐R becoming operational, particularly in the upper and lower troposphere. A comparison with dropsondes reveals a small vector wind speed bias of −0.7 m s⁻¹. The uncertainty for AMVs is estimated at 5–6 m s⁻¹. Comparison of collocated GFS model background wind fields shows small biases. Data assimilation reduces root‐mean‐squared differences, but the small biases in operational AMVs need further attention as they are a predominant wind data source in the GFS over oceanic regions.

Atmospheric rivers (ARs) play an important role in weather and hydroclimate throughout the western U.S., and extensive knowledge of AR frequency, intensity, impacts, and key meteorological processes has been developed primarily focused on the U.S. West Coast landfalling ARs. 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, but this requires quantitative investigation. We used trajectory‐based methods to explore the role of landfalling ARs, their moisture pathways, and their contribution to top‐decile precipitation in sub‐basins throughout CO. Moisture sourced from landfalling ARs affects this area more frequently, penetrating inland along relatively low‐elevation corridors through the Interior West, and exhibit substantial geographic and interannual variability. The backward trajectory approach for attributing precipitation to landfalling ARs yields mostly higher AR contributions to top‐decile cool‐season precipitation (21%–78%), primarily affecting western CO. 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.

Accurate snow level forecasts are essential for predicting the precipitation phase in mountainous California watersheds, where small vertical errors can cause large discrepancies in modeled runoff and reservoir inflows. This study evaluates the vertical displacement between the freezing level and observed brightband heights from 29 snow level profilers across California. Freezing level forecasts support situational awareness ahead of approaching storms, while snow level profilers provide near real-time brightband height observations that can guide operational decision-making. At each site, the mean displacement and standard deviation are quantified to assess uncertainty and investigate potential physical drivers contributing to large displacements. Results show substantial variability across all profilers, and even between nearby sites, with uncertainty exceeding 200 m at several locations. A modified verification approach demonstrates that accounting for profiler-specific displacement and uncertainty significantly alters freezing level forecast skill scores. These results underscore the importance of resolving profiler-specific displacement uncertainty, especially for Forecast-Informed Reservoir Operations, where runoff generation and flood risk assessments are sensitive to the localized elevation of the rain--snow transition. This work emphasizes that freezing level forecast verification requires a framework that explicitly incorporates the physical characteristics and observational uncertainties between the brightband height and the freezing level. For operational applications, such physically grounded verification is critical for improving confidence in precipitation phase forecasts and refining predictive tools for water management.