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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. [ABSTRACT FROM AUTHOR]

Satellites provide the largest dataset for monitoring the earth system and constraining analyses in numerical weather prediction models. A significant challenge for utilizing satellite radiances is the accurate estimation of their biases. High-accuracy nonradiance data are commonly employed to anchor radiance bias corrections. However, aside from the impacts of radio occultation data in the stratosphere, the influence of other types of "anchor" observation data on radiance assimilation remains unclear. This study provides an assessment of impacts of dropsonde data collected during the Atmospheric River (AR) Reconnaissance program, which samples ARs over the northeast Pacific, on the radiance assimilation using the Global Forecast System (GFS) and Global Data Assimilation System at the National Centers for Environmental Prediction. The assimilation of this dropsonde dataset has proven crucial for providing enhanced anchoring for bias corrections and improving the model background, leading to an increase of ∼5%–10% in the number of assimilated microwave radiance in the lower troposphere/midtroposphere over the northeast Pacific and North America. The impact on tropospheric infrared radiance is not only small but also beneficial. Impacts of dropsondes on the use of stratospheric channels are minimal due to the absence of dropsonde observations at certain altitudes, such as aircraft flight levels (e.g., 150 hPa). Results in this study underscore the usefulness of dropsondes, along with other conventional data, in optimizing the assimilation of satellite radiance. This study reinforces the importance of a diverse observing network for accurate weather forecasting and highlights the specific benefits derived from integrating dropsonde data into radiance assimilation processes. Significance Statement: This study aims to evaluate the impact of aircraft reconnaissance dropsondes on the assimilation of satellite radiance data, utilizing observations from the 2020 Atmospheric River Reconnaissance program. The key findings reveal a substantial enhancement in the model first guess and improved estimates of radiance biases. Notably, there is a significant 5%–10% increase in microwave radiance observations over the northeastern Pacific and North America, with positive yet modest effects observed in tropospheric infrared radiance. These findings underscore the crucial role of atmospheric river reconnaissance dropsondes as anchor data, enhancing the assimilation of radiance observations. In essence, the inclusion of these dropsondes in routine networks is particularly valuable for optimizing data assimilation in regions with sparse observational data. [ABSTRACT FROM AUTHOR]

Atmospheric rivers (ARs) are the primary mechanism for transporting water vapor from low latitudes to polar regions, playing a significant role as drivers of extreme weather, such as heavy precipitation and heat waves 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 to quantify the strength and impact of these ARs for both scientific research and practical application. The AR scale introduced by Ralph et al. (2019) ranks ARs based on the duration of AR conditions and the intensity. However, the thresholds of integrated water vapor transport (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 the Antarctic and Greenland coasts. The polar AR scale better characterizes the strength and impacts of ARs in the Antarctic and Arctic regions, and has the potential to enhance communications across observation, research, and forecasts for polar regions. [ABSTRACT FROM AUTHOR]

Atmospheric rivers (ARs) are the primary mechanism for transporting water vapor from low latitudes to polar regions, playing a significant role as drivers of extreme weather, such as heavy precipitation and heat waves 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 to quantify the strength and impact of these ARs for both scientific research and practical application. The AR scale introduced by Ralph et al. (2019) ranks ARs based on the duration of AR conditions and the intensity. However, the thresholds of integrated water vapor transport (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 the Antarctic and Greenland coasts. The polar AR scale better characterizes the strength and impacts of ARs in the Antarctic and Arctic regions, and has the potential to enhance communications across observation, research, and forecasts for polar regions. [ABSTRACT FROM AUTHOR]

Atmospheric River Reconnaissance has held field campaigns during cool seasons since 2016. These campaigns have provided thousands of dropsonde data profiles, which are assimilated into multiple global operational numerical weather prediction models. Data denial experiments, conducted by running a parallel set of forecasts that exclude the dropsonde information, allow testing of the impact of the dropsonde data on model analyses and the subsequent forecasts. Here, we investigate the differences in skill between the control forecasts (with dropsonde data assimilated) and denial forecasts (without dropsonde data assimilated) in terms of both precipitation and integrated vapor transport (IVT) at multiple thresholds. The differences are considered in the times and locations where there is a reasonable expectation of influence of an intensive observation period (IOP). Results for 2019 and 2020 from both the European Centre for Medium-Range Weather Forecasts (ECMWF) model and the National Centers for Environmental Prediction (NCEP) global model show improvements with the added information from the dropsondes. In particular, significant improvements in the control forecast IVT generally occur in both models, especially at higher values. Significant improvements in the control forecast precipitation also generally occur in both models, but the improvements vary depending on the lead time and metrics used. Significance Statement: Atmospheric River Reconnaissance is a program that uses targeted aircraft flights over the northeast Pacific to take measurements of meteorological fields. These data are then ingested into global weather models with the intent of improving the initial conditions and resulting forecasts along the U.S. West Coast. The impacts of these observations on two global numerical weather models were investigated to determine their influence on the forecasts. The integrated vapor transport, a measure of both wind and humidity, saw significant improvements in both models with the additional observations. Precipitation forecasts were also improved, but with differing results between the two models. [ABSTRACT FROM AUTHOR]

Narrow cold‐frontal rain bands (NCFR) often produce short‐duration and high‐intensity precipitation that can lead to flooding and debris flow in California (CA). On 27 January 2021, an atmospheric river (AR) associated with an intense surface cyclone made landfall over coastal northern CA, which featured a prominent NCFR. This study uses high‐resolution West Weather Research and Forecasting simulations to accurately resolve the gap and core structure of the NCFR and provide reliable precipitation estimations, compensating for limitations of radar and satellite observations. This NCFR was supported by robust synoptic‐scale quasi‐geostrophic (QG) forcing for ascent and frontogenesis. It propagated southward from Cape Mendocino to Big Sur in 12 hr before stalling and rotating counter‐clockwise in central/southern CA due to upstream Rossby wave breaking and an amplifying upper‐tropospheric trough. With the lower to middle tropospheric flow backed considerably to the south‐southwest over the NCFR, the increase of the vertical wind shear caused the transition from parallel to trailing stratiform precipitation. The stall and pivot of the AR and NCFR led to intense rainfall with a 2‐day precipitation accumulation greater than 300 mm over central CA. In addition, under the potential instability and frontogenesis, a moist absolutely unstable layer between 850 and 700 hPa was captured at the leading edge of the NCFR, which indicated slantwise deep layer lifting and high precipitation efficiency. This study reveals synoptic‐scale and mesoscale drivers of rainfall outside orographic lifting and reaffirms the importance of high‐resolution numerical modeling for the prediction of extreme precipitation and related natural hazards. Plain Language Summary: California often experiences short‐duration, high‐intensity rainfall associated with landfalling atmospheric rivers (ARs), which are long, thin corridors of moisture in the atmosphere. They can trigger post‐fire debris flows, shallow landslides, and flash flooding. This study examines characteristics of the high impact landfalling AR from January 2021 via high‐resolution model simulations. The landfalling AR, associated with an intense surface cyclone over the Northeast Pacific, moved southward through 27 January prior to stalling along the central California Coast on 28 January. Due to the stalling and pivoting of the AR, the coast of central and southern California experienced a long‐duration period of moderate precipitation, large‐scale forcing for ascent and short duration periods of intense precipitation. In addition, the intense precipitation along the cold front can be explained an effective dynamic lifting of a deep layer of atmosphere. This event caused a partial collapse of Highway 1 in Northern California and produced >375 mm of rainfall at Las Tablas, which led to a post‐fire debris flow ∼30 km south of Big Sur. High‐resolution weather modeling reveals the physical processes of precipitation and is necessary for the prediction of extreme precipitation and related natural hazards. Key Points: This atmospheric river caused sustained rainfall and short‐duration precipitation related to a narrow cold‐frontal rainbandThe narrow cold‐frontal rainband is mainly driven by synoptic‐scale quasi‐geostrophic forcing for ascent and frontogenesisHigh‐resolution modeling is necessary to improve the understanding and predictability of high‐intensity short‐duration precipitation [ABSTRACT FROM AUTHOR]

The Antarctica Peninsula (AP) has experienced more frequent and intense surface melting recently, jeopardizing the stability of ice shelves and ultimately leading to ice loss. Among the key phenomena that can initiate surface melting are atmospheric rivers (ARs) and leeside foehn; the combined impact of ARs and foehn led to moderate surface warming over the AP in December 2018 and record‐breaking surface melting in February 2022. Focusing on the more intense 2022 case, this study uses high‐resolution Polar WRF simulations with advanced model configurations, Reference Elevation Model of Antarctica topography, and observed surface albedo to better understand the relationship between ARs and foehn and their impacts on surface warming. With an intense AR (AR3) intrusion during the 2022 event, weak low‐level blocking and heavy orographic precipitation on the upwind side resulted in latent heat release, which led to a more deep‐foehn like case. On the leeside, sensible heat flux associated with the foehn magnitude was the major driver during the night and the secondary contributor during the day due to a stationary orographic gravity wave. Downward shortwave radiation was enhanced via cloud clearance and dominated surface melting during the daytime, especially after the peak of the AR/foehn events. However, due to the complex terrain of the AP, ARs can complicate the foehn event by transporting extra moisture to the leeside via gap flows. During the peak of the 2022 foehn warming, cloud formation on the leeside hampered the downward shortwave radiation and slightly increased the downward longwave radiation. Plain Language Summary: On the Antarctic Peninsula (AP), when ice shelves break up, glaciers flow faster from the land into the sea, leading to ice loss and increasing sea level rise. Surface warming is projected to double by 2050 over Antarctica and may have led to ice shelf collapse. Two phenomena that enhance surface warming are atmospheric rivers, (long corridors of moisture in the atmosphere) and leeside foehn effects (cooler and moist air advection on the upwind side that becomes warmer and drier when descending on the leeside). Here we study two combined atmospheric river and foehn events that led to surface warming on the AP, occurring in December 2018 and February 2022. The main warming mechanism in the northeastern AP during the nighttime was transfer of heat from the air to the ice surface (sensible heat flux), while the main mechanism during the daytime was intense sunlight, which was able to reach the surface because of clear skies on the opposite (lee) side caused by foehn. However, complicating the picture, there are gaps in the AP mountain range that let the atmospheric river through, allowing clouds to form on the other side, which then blocked some of the sunlight. Key Points: This study investigates the atmospheric river and foehn warming over the Antarctic Peninsula via observations and model simulationsAtmospheric rivers led to stronger precipitation on the upwind side and favored the foehn‐related sensible heat transfer on the leesideUnder the combined atmospheric rivers and foehn, shortwave radiation contributed the most to the ice surface warming, followed by sensible heat flux [ABSTRACT FROM AUTHOR]

Tracking atmospheric rivers (ARs) across their lifecycles is a field of recent interest with a multitude of emerging methodologies. The CONNected‐objECT (CONNECT) algorithm is adapted for the tracking of global midlatitude AR lifecycles and associated precipitation by implementing a seeded region growing segmentation algorithm, creating the AR‐CONNECT algorithm. To facilitate the permissiveness of the methodology, AR‐CONNECT is without hard‐coded geometric criteria yet is still shown to extract synoptic‐scale elongated objects >99.99% of the time. One of the consequences of the methodology is the ability to occasionally track atmospheric water vapor anomalies before evolving into AR geometries, effectively tracking AR genesis further back than other studies. With the aid of subdaily satellite‐derived rain data, we investigate the climatology, trends, and patterns of AR lifecycles from 1983–2016 and compare with other AR tracking studies. We find that AR frequency, genesis, and terminus locations are in generally good agreement with other AR tracking methodologies, though with key differences, and that AR frequencies in each hemisphere are determined by the number of AR hotspots. Furthermore, we uncover evidence that certain AR characteristics, such as frequency, areal extent, and duration, show evidence of increasing trends. Midlatitude precipitation uncovered by AR‐CONNECT shows contributions up to 50% over land and 65% over the ocean. Trend analysis of AR precipitation shows an increase in precipitation associated with ARs propagated by the Southern Jet Stream and ARs that traverse over the Sahara Desert, among others, but is determined not to be a driver of changes in global precipitation. Key Points: A region growing segmentation technique is used to track atmospheric rivers lifecycles by their higher‐intensity coresThe areal extent and duration of atmospheric river lifecycle are observed to be increasing over the study period of 1983–2016Precipitation from atmospheric rivers constitutes up to 50%/65% of the rainfall over the land/oceans and is shown to be increasing globally [ABSTRACT FROM AUTHOR]

Water management and flood control are major challenges in the western United States. They are heavily influenced by atmospheric river (AR) storms that produce both beneficial water supply and hazards; for example, 84% of all flood damages in the West (up to 99% in key areas) are associated with ARs. However, AR landfall forecast position errors can exceed 200 km at even 1-day lead time and yet many watersheds are <100 km across, which contributes to issues such as the 2017 Oroville Dam spillway incident and regularly to large flood forecast errors. Combined with the rise of wildfires and deadly post-wildfire debris flows, such as Montecito (2018), the need for better AR forecasts is urgent. Atmospheric River Reconnaissance (AR Recon) was developed as a research and operations partnership to address these needs. It combines new observations, modeling, data assimilation, and forecast verification methods to improve the science and predictions of landfalling ARs. ARs over the northeast Pacific are measured using dropsondes from up to three aircraft simultaneously. Additionally, airborne radio occultation is being tested, and drifting buoys with pressure sensors are deployed. AR targeting and data collection methods have been developed, assimilation and forecast impact experiments are ongoing, and better understanding of AR dynamics is emerging. AR Recon is led by the Center for Western Weather and Water Extremes and NWS/NCEP. The effort's core partners include the U.S. Navy, U.S. Air Force, NCAR, ECMWF, and multiple academic institutions. AR Recon is included in the "National Winter Season Operations Plan" to support improved outcomes for emergency preparedness and water management in the West. [ABSTRACT FROM AUTHOR]

Atmospheric rivers (ARs) are now widely known for their association with high‐impact weather events and long‐term water supply in many regions. Researchers within the scientific community have developed numerous methods to identify and track of ARs—a necessary step for analyses on gridded data sets, and objective attribution of impacts to ARs. These different methods have been developed to answer specific research questions and hence use different criteria (e.g., geometry, threshold values of key variables, and time dependence). Furthermore, these methods are often employed using different reanalysis data sets, time periods, and regions of interest. The goal of the Atmospheric River Tracking Method Intercomparison Project (ARTMIP) is to understand and quantify uncertainties in AR science that arise due to differences in these methods. This paper presents results for key AR‐related metrics based on 20+ different AR identification and tracking methods applied to Modern‐Era Retrospective Analysis for Research and Applications Version 2 reanalysis data from January 1980 through June 2017. We show that AR frequency, duration, and seasonality exhibit a wide range of results, while the meridional distribution of these metrics along selected coastal (but not interior) transects are quite similar across methods. Furthermore, methods are grouped into criteria‐based clusters, within which the range of results is reduced. AR case studies and an evaluation of individual method deviation from an all‐method mean highlight advantages/disadvantages of certain approaches. For example, methods with less (more) restrictive criteria identify more (less) ARs and AR‐related impacts. Finally, this paper concludes with a discussion and recommendations for those conducting AR‐related research to consider. Key Points: The large number of atmospheric river identification/tracking methods produces large uncertainty related to AR climatology and impactsUncertainty is quantified using the same data (MERRA v2), time period (1980–2017), region (global where possible), and common metricsThis study presents recommendations regarding the advantages/disadvantages of certain approaches based on science application [ABSTRACT FROM AUTHOR]

The Atmospheric River Tracking Method Intercomparison Project (ARTMIP) is an international collaborative effort to understand and quantify the uncertainties in atmospheric river (AR) science based on detection algorithm alone. Currently, there are many AR identification and tracking algorithms in the literature with a wide range of techniques and conclusions. ARTMIP strives to provide the community with information on different methodologies and provide guidance on the most appropriate algorithm for a given science question or region of interest. All ARTMIP participants will implement their detection algorithms on a specified common dataset for a defined period of time. The project is divided into two phases: Tier 1 will utilize the Modern-Era Retrospective analysis for Research and Applications, version 2 (MERRA-2) reanalysis from January 1980 to June 2017 and will be used as a baseline for all subsequent comparisons. Participation in Tier 1 is required. Tier 2 will be optional and include sensitivity studies designed around specific science questions, such as reanalysis uncertainty and climate change. High-resolution reanalysis and/or model output will be used wherever possible. Proposed metrics include AR frequency, duration, intensity, and precipitation attributable to ARs. Here, we present the ARTMIP experimental design, timeline, project requirements, and a brief description of the variety of methodologies in the current literature. We also present results from our 1-month "proof-of-concept" trial run designed to illustrate the utility and feasibility of the ARTMIP project. [ABSTRACT FROM AUTHOR]