Avalanche Fatalities during Atmospheric River Events in the Western United States

2017 ◽  
Vol 18 (5) ◽  
pp. 1359-1374 ◽  
Author(s):  
Benjamin J. Hatchett ◽  
Susan Burak ◽  
Jonathan J. Rutz ◽  
Nina S. Oakley ◽  
Edward H. Bair ◽  
...  
Keyword(s):  
United States ◽  
Water Vapor ◽  
Snow Water Equivalent ◽  
Vapor Transport ◽  
Western United States ◽  
Snow Avalanche ◽  
Water Vapor Transport ◽  
Atmospheric Rivers ◽  
Avalanche Forecasting ◽  
Snow Water

Abstract The occurrence of atmospheric rivers (ARs) in association with avalanche fatalities is evaluated in the conterminous western United States between 1998 and 2014 using archived avalanche reports, atmospheric reanalysis products, an existing AR catalog, and weather station observations. AR conditions were present during or preceding 105 unique avalanche incidents resulting in 123 fatalities, thus comprising 31% of western U.S. avalanche fatalities. Coastal snow avalanche climates had the highest percentage of avalanche fatalities coinciding with AR conditions (31%–65%), followed by intermountain (25%–46%) and continental snow avalanche climates (<25%). Ratios of avalanche deaths during AR conditions to total AR days increased with distance from the coast. Frequent heavy to extreme precipitation (85th–99th percentile) during ARs favored critical snowpack loading rates with mean snow water equivalent increases of 46 mm. Results demonstrate that there exists regional consistency between snow avalanche climates, derived AR contributions to cool season precipitation, and percentages of avalanche fatalities during ARs. The intensity of water vapor transport and topographic corridors favoring inland water vapor transport may be used to help identify periods of increased avalanche hazard in intermountain and continental snow avalanche climates prior to AR landfall. Several recently developed AR forecast tools applicable to avalanche forecasting are highlighted.

scholarly journals Understanding the Role of Atmospheric Rivers in Heavy Precipitation in the Southeast United States

2016 ◽  
Vol 144 (4) ◽  
pp. 1617-1632 ◽  
Author(s):  
Kelly Mahoney ◽  
Darren L. Jackson ◽  
Paul Neiman ◽  
Mimi Hughes ◽  
Lisa Darby ◽  
...  
Keyword(s):  
United States ◽  
Seasonal Variation ◽  
Water Vapor ◽  
Heavy Precipitation ◽  
Vapor Transport ◽  
Water Vapor Transport ◽  
Southeast United States ◽  
Atmospheric Rivers ◽  
Detection Tool ◽  
Match Rate

Abstract An analysis of atmospheric rivers (ARs) as defined by an automated AR detection tool based on integrated water vapor transport (IVT) and the connection to heavy precipitation in the southeast United States (SEUS) is performed. Climatological water vapor and water vapor transport fields are compared between the U.S. West Coast (WCUS) and the SEUS, highlighting stronger seasonal variation in integrated water vapor in the SEUS and stronger seasonal variation in IVT in the WCUS. The climatological analysis suggests that IVT values above ~500 kg m−1 s−1 (as incorporated into an objective identification tool such as the AR detection tool used here) may serve as a sensible threshold for defining ARs in the SEUS. Atmospheric river impacts on heavy precipitation in the SEUS are shown to vary on an annual cycle, and a connection between ARs and heavy precipitation during the nonsummer months is demonstrated. When identified ARs are matched to heavy precipitation days (>100 mm day−1), an average match rate of ~41% is found. Results suggest that some aspects of an AR identification framework in the SEUS may offer benefit in forecasting heavy precipitation, particularly at medium- to longer-range forecast lead times. However, the relatively high frequency of SEUS heavy precipitation cases in which an AR is not identified necessitates additional careful consideration and incorporation of other critical aspects of heavy precipitation environments such that significant predictive skill might eventually result.

Climatological Characteristics of Atmospheric Rivers and Their Inland Penetration over the Western United States

2014 ◽  
Vol 142 (2) ◽  
pp. 905-921 ◽  
Author(s):  
Jonathan J. Rutz ◽  
W. James Steenburgh ◽  
F. Martin Ralph
Keyword(s):  
United States ◽  
Water Vapor ◽  
Great Basin ◽  
Large Scale ◽  
The United States ◽  
Western United States ◽  
Water Vapor Transport ◽  
Cool Season ◽  
Atmospheric Rivers ◽  
Topographic Barriers

Abstract Narrow corridors of water vapor transport known as atmospheric rivers (ARs) contribute to extreme precipitation and flooding along the West Coast of the United States, but knowledge of their influence over the interior is limited. Here, the authors use Interim European Centre for Medium-Range Weather Forecasts (ECMWF) Re-Analysis (ERA-Interim) data, Climate Prediction Center (CPC) precipitation analyses, and Snowpack Telemetry (SNOTEL) observations to describe the characteristics of cool-season (November–April) ARs over the western United States. It is shown that AR frequency and duration exhibit a maximum along the Oregon–Washington coast, a strong transition zone upwind (west) of and over the Cascade–Sierra ranges, and a broad minimum that extends from the “high” Sierra south of Lake Tahoe eastward across the central Great Basin and into the deep interior. East of the Cascade–Sierra ranges, AR frequency and duration are largest over the interior northwest, while AR duration is large compared to AR frequency over the interior southwest. The fractions of cool-season precipitation and top-decile 24-h precipitation events attributable to ARs are largest over and west of the Cascade–Sierra ranges. Farther east, these fractions are largest over the northwest and southwest interior, with distinctly different large-scale patterns and AR orientations enabling AR penetration into each of these regions. In contrast, AR-related precipitation over the Great Basin east of the high Sierra is rare. These results indicate that water vapor depletion over major topographic barriers is a key contributor to AR decay, with ARs playing a more prominent role in the inland precipitation climatology where lower or less continuous topography facilitates the inland penetration of ARs.

Extreme Surface Winds During Landfalling Atmospheric Rivers: The Modulating Role of Near-Surface Stability

2021 ◽  
Author(s):  
Terence J. Pagano ◽  
Duane E. Waliser ◽  
Bin Guan ◽  
Hengchun Ye ◽  
F. Martin Ralph ◽  
...  
Keyword(s):  
Water Vapor ◽  
Static Stability ◽  
Vapor Transport ◽  
Water Vapor Transport ◽  
Surface Winds ◽  
Near Surface ◽  
Extreme Surface ◽  
Atmospheric Rivers ◽  
Explained Variance

AbstractAtmospheric rivers (ARs) are long and narrow regions of strong horizontal water vapor transport. Upon landfall, ARs are typically associated with heavy precipitation and strong surface winds. A quantitative understanding of the atmospheric conditions that favor extreme surface winds during ARs has implications for anticipating and managing various impacts associated with these potentially hazardous events. Here, a global AR database (1999–2014) with relevant information from MERRA-2 reanalysis, QuikSCAT and AIRS satellite observations are used to better understand and quantify the role of near-surface static stability in modulating surface winds during landfalling ARs. The temperature difference between the surface and 1 km MSL (ΔT; used here as a proxy for near-surface static stability), and integrated water vapor transport (IVT) are analyzed to quantify their relationships to surface winds using bivariate linear regression. In four regions where AR landfalls are common, the MERRA-2-based results indicate that IVT accounts for 22-38% of the variance in surface wind speed. Combining ΔT with IVT increases the explained variance to 36-52%. Substitution of QuikSCAT surface winds and AIRS ΔT in place of the MERRA-2 data largely preserves this relationship (e.g., 44% compared to 52% explained variance for USA West Coast). Use of an alternate static stability measure–the bulk Richardson number–yields a similar explained variance (47%). Lastly, AR cases within the top and bottom 25% of near-surface static stability indicate that extreme surface winds (gale or higher) are more likely to occur in unstable conditions (5.3%/14.7% during weak/strong IVT) than in stable conditions (0.58%/6.15%).

scholarly journals Assessment of Numerical Weather Prediction Model Reforecasts of the Occurrence, Intensity, and Location of Atmospheric Rivers along the West Coast of North America

2018 ◽  
Vol 146 (10) ◽  
pp. 3343-3362 ◽  
Author(s):  
Kyle M. Nardi ◽  
Elizabeth A. Barnes ◽  
F. Martin Ralph
Keyword(s):  
Water Vapor ◽  
North America ◽  
Weather Prediction ◽  
Vapor Transport ◽  
Water Vapor Transport ◽  
Lead Times ◽  
Model Errors ◽  
The West ◽  
Atmospheric Rivers ◽  
Landfall Location

AbstractAtmospheric rivers (ARs)—narrow corridors of high atmospheric water vapor transport—occur globally and are associated with flooding and maintenance of the water supply. Therefore, it is important to improve forecasts of AR occurrence and characteristics. Although prior work has examined the skill of numerical weather prediction (NWP) models in forecasting atmospheric rivers, these studies only cover several years of reforecasts from a handful of models. Here, we expand this previous work and assess the performance of 10–30 years of wintertime (November–February) AR landfall reforecasts from the control runs of nine operational weather models, obtained from the International Subseasonal to Seasonal (S2S) Project database. Model errors along the west coast of North America at leads of 1–14 days are examined in terms of AR occurrence, intensity, and landfall location. Occurrence-based skill approaches that of climatology at 14 days, while models are, on average, more skillful at shorter leads in California, Oregon, and Washington compared to British Columbia and Alaska. We also find that the average magnitude of landfall integrated water vapor transport (IVT) error stays fairly constant across lead times, although overprediction of IVT is common at later lead times. Finally, we show that northward landfall location errors are favored in California, Oregon, and Washington, although southward errors occur more often than expected from climatology. These results highlight the need for model improvements, while helping to identify factors that cause model errors.

scholarly journals Influence of Atmospheric Rivers on Mountain Snowpack in the Western United States

2018 ◽  
Vol 31 (24) ◽  
pp. 9921-9940 ◽  
Author(s):  
N. Goldenson ◽  
L. R. Leung ◽  
C. M. Bitz ◽  
E. Blanchard-Wrigglesworth
Keyword(s):  
United States ◽  
Sierra Nevada ◽  
Snow Water Equivalent ◽  
Region Of Interest ◽  
Internal Variability ◽  
Western United States ◽  
Historical Period ◽  
Atmospheric Rivers ◽  
Atmospheric River ◽  
The Impact

In the coastal mountains of western North America, most extreme precipitation is associated with atmospheric rivers (ARs), narrow bands of moisture originating in the tropics. Here we quantify how interannual variability in atmospheric rivers influences snowpack in the western United States in observations and a model. We simulate the historical climate with the Model for Prediction Across Scales (MPAS) with physics from the Community Atmosphere Model, version 5 [CAM5 (MPAS-CAM5)], using prescribed sea surface temperatures. In the global variable-resolution domain, regional refinement (at ~30 km) is applied to our region of interest and upwind over the northeast Pacific. To better characterize internal variability, we conduct simulations with three ensemble members over 30 years of the historical period. In the Cascade Range, with some exceptions, winters with more atmospheric river days are associated with less snowpack. In California’s Sierra Nevada, winters with more ARs are associated with greater snowpack. The slope of the linear regression of observed snow water equivalent (SWE) on reanalysis-based AR count has the same sign as that arrived at using the model, but is statistically significant in observations only for California. In spring, internal variance plays an important role in determining whether atmospheric river days appear to be associated with greater or less snowpack. The cumulative (winter through spring) number of atmospheric river days, on the other hand, has a relationship with spring snowpack, which is consistent across ensemble members. Thus, the impact of atmospheric rivers on winter snowpack has a greater influence on spring snowpack than spring atmospheric rivers in the model for both regions and in California consistently in observations.

A Climatological Study of National Weat her Service Watches, Warnings, and Advisories and Landfalling Atmospheric Rivers in the Western U.S. 2006–2018

2021 ◽  
Author(s):  
Samuel M. Bartlett ◽  
Jason M. Cordeira
Keyword(s):  
Water Vapor ◽  
Pacific Northwest ◽  
Vapor Transport ◽  
Water Vapor Transport ◽  
Synoptic Scale ◽  
Flash Flooding ◽  
Atmospheric Rivers ◽  
The Pacific ◽  
Central Regions ◽  
Heavy Snow

AbstractAtmospheric rivers (ARs) are synoptic-scale phenomena associated with long, narrow corridors of enhanced low-level water vapor transport. Landfalling ARs may produce numerous beneficial (e.g. drought amelioration and watershed recharge) and hazardous (e.g. flash flooding and heavy snow) impacts that may require the National Weather Service (NWS) to issue watches, warnings, and advisories (WWAs) for hazardous weather. Prior research on WWAs and ARs in California found that 50–70% of days with flood-related and 60–80% of days with winter weather-related WWAs occurred on days with landfalling ARs in California. The present study further investigates this relationship for landfalling ARs and WWAs during the cool seasons of 2006–2018 across the entire western U.S. and considers additional dimensions of AR intensity and duration. Across the western U.S., regional maxima of 70–90% of days with WWAs issued for any hazard type were associated with landfalling ARs. In the Pacific Northwest and Central regions, flood-related and wind-related WWAs were also more frequently associated with more intense and longer duration ARs. While a large majority of days with WWAs were associated with landfalling ARs, not all landfalling ARs were necessarily associated with WWAs (i.e., not all ARs are hazardous). For example, regional maxima of only 50–70% of AR days were associated with WWAs issued for any hazard type. However, as landfalling AR intensity and duration increased, the association with a WWA and the “hazard footprint” of WWAs increased quasi-exponentially across the western U.S.

scholarly journals Spatiotemporal Characteristics of Snowpack Density in the Mountainous Regions of the Western United States

2008 ◽  
Vol 9 (6) ◽  
pp. 1416-1426 ◽  
Author(s):  
Naoki Mizukami ◽  
Sanja Perica
Keyword(s):  
United States ◽  
Snow Depth ◽  
Snow Water Equivalent ◽  
Winter Season ◽  
Western United States ◽  
Densification Rate ◽  
Snow Density ◽  
Mountainous Regions ◽  
Spatiotemporal Characteristics ◽  
Snow Water

Abstract Snow density is calculated as a ratio of snow water equivalent to snow depth. Until the late 1990s, there were no continuous simultaneous measurements of snow water equivalent and snow depth covering large areas. Because of that, spatiotemporal characteristics of snowpack density could not be well described. Since then, the Natural Resources Conservation Service (NRCS) has been collecting both types of data daily throughout the winter season at snowpack telemetry (SNOTEL) sites located in the mountainous areas of the western United States. This new dataset provided an opportunity to examine the spatiotemporal characteristics of snowpack density. The analysis of approximately seven years of data showed that at a given location and throughout the winter season, year-to-year snowpack density changes are significantly smaller than corresponding snow depth and snow water equivalent changes. As a result, reliable climatological estimates of snow density could be obtained from relatively short records. Snow density magnitudes and densification rates (i.e., rates at which snow densities change in time) were found to be location dependent. During early and midwinter, the densification rate is correlated with density. Starting in early or mid-March, however, snowpack density increases by approximately 2.0 kg m−3 day−1 regardless of location. Cluster analysis was used to obtain qualitative information on spatial patterns of snowpack density and densification rates. Four clusters were identified, each with a distinct density magnitude and densification rate. The most significant physiographic factor that discriminates between clusters was proximity to a large water body. Within individual mountain ranges, snowpack density characteristics were primarily dependent on elevation.

scholarly journals SnowClim v1.0: High-resolution snow model and data for the western United States

2021 ◽  
Author(s):  
Abby C. Lute ◽  
John Abatzoglou ◽  
Timothy Link
Keyword(s):  
Climate Change ◽  
United States ◽  
Spatial Resolution ◽  
Snow Water Equivalent ◽  
High Spatial Resolution ◽  
Western United States ◽  
Climate Data ◽  
Western Us ◽  
Snow Water ◽  
Snow Model

Abstract. Seasonal snowpack dynamics shape the biophysical and societal characteristics of many global regions. However, snowpack accumulation and duration have generally declined in recent decades largely due to anthropogenic climate change. Mechanistic understanding of snowpack spatiotemporal heterogeneity and climate change impacts will benefit from snow data products that are based on physical principles, that are simulated at high spatial resolution, and that cover large geographic domains. Existing datasets do not meet these requirements, hindering our ability to understand both contemporary and changing snow regimes and to develop adaptation strategies in regions where snowpack patterns and processes are important components of Earth systems. We developed a computationally efficient physics-based snow model, SnowClim, that can be run in the cloud. The model was evaluated and calibrated at Snowpack Telemetry sites across the western United States (US), achieving a site-median root mean square error for daily snow water equivalent of 62 mm, bias in peak snow water equivalent of −9.6 mm, and bias in snow duration of 1.2 days when run hourly. Positive biases were found at sites with mean winter temperature above freezing where the estimation of precipitation phase is prone to errors. The model was applied to the western US using newly developed forcing data created by statistically downscaling pre-industrial, historical, and pseudo-global warming climate data from the Weather Research and Forecasting (WRF) model. The resulting product is the SnowClim dataset, a suite of summary climate and snow metrics for the western US at 210 m spatial resolution (Lute et al., 2021). The physical basis, large extent, and high spatial resolution of this dataset will enable novel analyses of changing hydroclimate and its implications for natural and human systems.

Sign in / Sign up

Export Citation Format

Share Document