Abstract

Atmospheric rivers (ARs) play vital roles in the western United States and related regions globally, not only producing heavy precipitation and flooding, but also providing beneficial water supply. This paper introduces a scale for the intensity and impacts of ARs. Its utility may be greatest where ARs are the most impactful storm type and hurricanes, nor’easters, and tornadoes are nearly nonexistent. Two parameters dominate the hydrologic outcomes and impacts of ARs: vertically integrated water vapor transport (IVT) and AR duration [i.e., the duration of at least minimal AR conditions (IVT ≥ 250 kg m–1 s–1)]. The scale uses an observed or predicted time series of IVT at a given geographic location and is based on the maximum IVT and AR duration at that point during an AR event. AR categories 1–5 are defined by thresholds for maximum IVT (3-h average) of 250, 500, 750, 1,000, and 1,250 kg m–1 s–1, and by IVT exceeding 250 kg m–1 s–1 continuously for 24–48 h. If the AR event duration is less than 24 h, it is downgraded by one category. If it is longer than 48 h, it is upgraded one category. The scale recognizes that weak ARs are often mostly beneficial because they can enhance water supply and snowpack, while stronger ARs can become mostly hazardous, for example, if they strike an area with antecedent conditions that enhance vulnerability, such as burn scars or wet conditions. Extended durations can enhance impacts. Short durations can mitigate impacts.

A scale for atmospheric river intensity and potential impacts is introduced, enhancing situational awareness and forecast communication.

Atmospheric rivers (ARs) have emerged as a subject of broad interest not only in the scientific community, but also with water managers, emergency managers, media, the public, and policy makers. The role of ARs in creating extreme precipitation, flooding, drought, and other impacts is well established (Table 1). Major field experiments, such as the 6-yr-long interagency CalWater program of field studies, have been conducted on ARs (Ralph et al. 2016). Recently, the linkages between ARs and other key phenomena such as the warm conveyor belt and tropical moisture exports have been elucidated (e.g., Dettinger et al. 2015). The recently released Fourth National Climate Assessment now includes ARs as a type of extreme storm (along with tropical storms, severe convection, and winter storms) and highlights increasing AR occurrence and intensity as a climate change risk (Wuebbles et al. 2017). Community interest in the subject led to the development of a definition of “atmospheric river” for the Glossary of Meteorology (American Meteorological Society 2018; Ralph et al. 2018), and an International Atmospheric Rivers Conference (IARC) brought together over 100 people representing AR work on six continents (Ralph et al. 2017a).

The anomalously wet 2016/17 cool season over the western United States highlighted the need for greater distinction between the majority of ARs that are primarily beneficial and the minority of ARs that are primarily hazardous. ARs of varying intensity affected Northern California during this time, contributing to reservoir storage, snowpack, and drought relief, but also cumulatively leading to the Oroville Dam crisis during February 2017 (Fig. 1). One particularly strong AR in January 2017 produced extreme precipitation over the Feather River basin (Fig. 2), in which the Oroville Dam is located. The active weather pattern over the western United States during this timeframe led to numerous requests for differentiation between weaker and stronger ARs—many of these requests came directly from operational staff with the National Weather Service, who must communicate potential impacts to the public.

The term atmospheric river has entered the scientific and public lexicon (American Meteorological Society 2018; Ralph et al. 2018), but while the meteorological community often focuses on AR-related hazards (a minority of storms), information regarding AR-related benefits (most storms) is often lacking. Yet in California, which has the largest interannual variability in precipitation of any state (Dettinger et al. 2011), ARs contribute 25%–50% of annual precipitation (including critical snowpack) in just a few days each year (Dettinger et al. 2011; Ralph et al. 2013; Rutz et al. 2014). Furthermore, the top 5% wettest days each year, most of which are attributable to ARs, are responsible for 85% of the interannual variability in precipitation over Northern California (Dettinger and Cayan 2014). Therefore, the presence or absence of a few AR events can “make or break” precipitation over the course of the water year.1 Many water managers recognize the benefits of ARs in terms of water supply (J. Jasperse 2018, Sonoma County Water Agency, personal communication; G. Woodside 2018, Orange County Water District, personal communication). In recognition of the key role ARs play in Californian hydroclimate, the state passed legislation in 2015 creating the “Atmospheric River Research, Mitigation and Climate Forecasting”2 program, which aims to develop methods to better characterize and communicate information about ARs to policy makers, decision-makers, and the public. While much has been learned about ARs on the U.S. West Coast, they have been found to be of similar importance in other key areas globally, for example, Europe (Lavers et al. 2011; Ramos et al. 2015; Eiras-Barca et al. 2018) and South America (Viale and Nuñez 2011).

Currently, there is no concise method for conveying the spectrum of benefits and hazards faced by communities during a particular AR event. This paper, written by hydrometeorological scientists, weather forecasters, and users of weather information, introduces a scale for characterizing the strength and impacts of ARs. This scale is intended to serve the western United States, and other regions with significant AR climatologies, in the same way that scales for hurricanes (Elsner and Kara 1999), tornadoes (Fujita 1981), and nor’easters (Kocin and Uccellini 2004) have served other parts of the country. The paper first describes the characteristics of ARs and the selection of vertically integrated water vapor transport (IVT) and AR event duration as key metrics by which to gauge AR strength. The AR scale is then defined, followed by a discussion of the frequencies and impacts associated with ARs of varying strength, as defined by the scale, over the western United States. The implications of this study are then summarized, and a forecast example is presented.

IDENTIFYING THE CHARACTERISTICS OF ATMOSPHERIC RIVERS ON WHICH TO BASE A SCALE.

What is an atmospheric river?

Development of a scale for ARs requires specification of what constitutes an AR. There now exists a formal definition in the Glossary of Meteorology that can form the basis (American Meteorological Society 2018). As defined in the glossary, “an atmospheric river is a long, narrow and transient corridor of strong horizontal water vapor transport that is typically associated with a low-level jet stream ahead of the cold front of an extratropical cyclone” (Zhu and Newell 1998; Ralph et al. 2004, 2006, 2017b; Bao et al. 2006; Stohl et al. 2008; Warner et al. 2012; Cordeira et al. 2013; Sodemann and Stohl 2013; Dacre et al. 2015). Some ARs entrain water vapor from the tropics (e.g., Stohl et al. 2008; Ralph et al. 2011; Cordeira et al. 2013; Sodemann and Stohl 2013), but this is not a trait of all ARs. Atmospheric rivers frequently lead to heavy precipitation where they are forced upward, for example, by mountains or by ascent in the warm conveyor belt. Horizontal water vapor transport in the midlatitudes occurs primarily in atmospheric rivers and is focused in the lower troposphere. (Henceforth, the term transport represents the vertically integrated horizontal flux of water vapor, and not the vertical fluxes of water vapor such as land–air, sea–air, and convection that are largely the focus of past meteorological studies.)

This definition represents an AR as an “object” in space and time that has the appropriate characteristics. This has led to the development of many AR detection techniques (ARDTs) that can identify the locations, times, and characteristics of ARs regionally or globally (Shields et al. 2018). However, it is also useful (especially in context of many practical, on-the-ground applications and communications) to consider an Eulerian perspective, that is, that of an observer at a specific location monitoring conditions over time at that site. From this perspective, the observer experiences the passage of an AR as a sequence of meteorological conditions associated with the AR. Many studies take this approach, particularly those describing the climatological characteristics of ARs (e.g., Rutz et al. 2014; Guan and Waliser 2015). The AR scaling method introduced here takes this Eulerian perspective. For this reason, the term atmospheric river event will henceforth refer to the period that AR conditions occur at a given location.

What variable to use to measure ARs?

Historically, both vertically integrated water vapor (IWV; also known as precipitable water) and IVT (see  appendix for calculation) have been used to define the spatial extent and intensity of ARs. Initially, AR-related studies used satellite-based observations of IWV as a proxy to identify ARs, since observations of column wind speed are not available everywhere (Ralph et al. 2004; Neiman et al. 2008). However, IVT is less dependent on surface elevation, more directly related to precipitation outcomes (e.g., Moore et al. 2012; Rutz et al. 2014; Oakley et al. 2017), and is used in most current studies as the basis for identifying ARs (e.g., Cordeira et al. 2017; Young et al. 2017; Waliser and Guan 2017; Dettinger et al. 2018). Furthermore, numerical weather prediction models predict IVT more skillfully than precipitation itself, offering an advantage in forecasting (Lavers et al. 2016). Hence, IVT is also used here in defining the AR scale.

What is the range of AR intensities in terms of IVT?

Since IVT is chosen to define the AR scale, it is useful to examine the wide range of IVT magnitudes associated with ARs. In many ARDT strategies, the term atmospheric river is, by definition, restricted to features with IVT ≥ 250 kg m–1 s–1 (and IWV ≥ 2.0 cm). However, the IVT values in ARs cover a wide range, with reanalyses and observations of ARs from radiosondes and dropsondes providing many examples of landfalling ARs over the northeast Pacific with IVT magnitudes >1,000 kg m–1 s–1 and IWV values >3.5 cm, as shown in Figs. 2a and 2b. In this example, a landfalling AR in early January 2017 contained radiosonde-derived IWV and IVT magnitudes of 3.5 cm and 1,102 kg m–1 s–1, respectively, at Bodega Bay (BBY), California, where AR conditions (i.e., IVT ≥ 250 kg m–1 s–1) persisted for ∼36 h. Furthermore, offshore dropsonde measurements from 21 ARs collected over several field campaigns show that observed AR IVT intensities can exceed 1,250 kg m–1 s–1. In addition to observational data, experience with the AR Landfall Probability Tool (Cordeira et al. 2017), which uses 3-hourly Global Forecast System (GFS) output, shows that ensemble forecasts contain many ARs with IVT ≥ 500, and on occasion IVT ≥ 1,000 kg m–1 s–1. It has already been found useful to represent the “AR intensity” as the maximum value of IVT at a given location during the AR event at that location. Based on these observations and analyses of ARs that have made landfall on the U.S. West Coast, the following intensity thresholds are chosen for the AR scale:

  • weak is ≥250–500 kg m–1 s–1,

  • moderate is ≥500–750 kg m–1 s–1 (transitional from mostly beneficial to hazardous),

  • strong is ≥750–1,000 kg m–1 s–1,

  • extreme is ≥1,000–1,250 kg m–1 s–1, and

  • exceptional is ≥1,250 kg m–1 s–1.

These thresholds can be applied to observational analyses of past events, or to forecasts.

To better communicate the magnitude of the strongest ARs, based on their frequencies of occurrence, Dettinger et al. (2018) used reanalysis data from 1948 to 2015 to diagnose IVT return periods for different locations along the U.S. West Coast. An example of this is shown in Fig. 3 for the landfall location of the storm highlighted in Fig. 2. It shows that the return periods for 750, 1,000, and 1,250 kg m–1 s–1 are roughly 1, 3, and 20 years , respectively, for those locations (these IVT thresholds are 3-h averages). A more comprehensive analysis of the entire U.S. West Coast is provided in Dettinger et al. (2018), which reveals that the Oregon coast has the strongest ARs on average, with IVT reaching 1,000 kg m–1 s–1 about once per year, while the Washington coast sees this once every 2 years, San Francisco once every 3 years, and Los Angeles about once every 10 years. These results refine earlier studies of AR landfall frequency by Neiman et al. (2008), Rutz et al. (2014), and others that also concluded ARs were more common in the Pacific Northwest than in California.

The important role of duration of AR conditions at landfall.

Although the maximum intensity of IVT during a landfalling AR largely controls the hourly rain rates, it has also been shown that the storm-total precipitation (and hence runoff) is strongly controlled by the storm-total water vapor transport (Ralph et al. 2013). In this framework, a “storm” or an “AR event” is defined by the period during which AR conditions are continuously met at a given location. This analysis was based on observations of AR conditions using an atmospheric river observatory (ARO) and found that 74% of the variance in storm-total precipitation is explained by variance in storm-total upslope IVT (where the upslope direction is based on regional terrain orientation). This study found that an average AR lasted 20 h at this location, a result confirmed by analysis from Rutz et al. (2014) using reanalysis methods. Rutz et al. (2014) extended this result from one coastal location to the entire coast and showed that AR duration varies from about 18 h on average to about 25 h depending on latitude. Lamjiri et al. (2017) concluded also that in the western United States, storm duration was a more important parameter in determining storm-total precipitation than was the magnitude of peak hourly precipitation rate within the storm.

In summary, AR duration and (IVT) intensity are the key characteristics in determining streamflow magnitudes and related hydrologic impacts. Since AR durations greater than ∼24 h are, subjectively, often recognized as being associated with greater impacts, this study classifies AR durations as ≤24, ≥24–48, and ≥48 h. The ∼24-h threshold is simply a convenient, though arbitrary, marker of event-based impacts as a function of duration.

Some dramatic recent examples illustrate ways that stronger and more persistent IVTs along the U.S. West Coast have been associated with more hazardous impacts as compared to weaker and less persistent ARs. For example, the 8 January 2017 AR depicted in Figs. 2a and 2b produced 72-h rainfall totals on 6–8 January 2017 that exceeded 200 mm at 22 Cooperative Observer Program (COOP) observing sites across Northern and central California, and totals that exceeded 300 mm at five COOP observing sites (Fig. 2c). These rainfall totals resulted in record streamflow for the date on several rivers and streams across the California Coastal Ranges and northern Sierra Nevada (Fig. 2d).

Based on this behavior, the AR scale will use 24–48-h duration as the norm. To accommodate the fact that some ARs will have shorter duration, and some longer, as well as the fact that impacts on streamflow are so sensitive to duration (e.g., Ralph et al. 2013), the AR will be downgraded by one level if its duration is <24 h, and upgraded if ≥48 h.

A SCALE TO CATEGORIZE AR STRENGTHS AND IMPACTS.

The previous section established the roles of IVT and the duration of AR conditions in producing high-impact hydrometeorological events. This section lays out a strategy for a scaling of ARs that considers both these factors, based on time series of observed or predicted conditions at individual points. Because this approach does not include shape requirements typical of earlier studies’ object-oriented AR identification methods, and it uses only time series of IVT in an Eulerian framework, it greatly simplifies implementation of the scale in gridded datasets like reanalyses and forecasts.

After much consideration, it was decided to use a 0.5° × 0.5° spatial grid to assess and display AR conditions in terms of the scale. The resolution is ultimately somewhat arbitrary. The value used here is based on the following considerations: i) the spatial areas of ARs are much larger than this; ii) gridded data on this resolution are increasingly available from reanalyses, operational global weather prediction models, and even climate models, at scales comparable to this; and iii) although it is technically feasible to apply the AR categorization method to finer grids, it is likely that the greatest value of the AR scale will be for situational awareness.

The AR scale categorizes AR events based on the maximum instantaneous IVT “intensity” and the duration of the event (i.e., the duration of IVT ≥ 250 kg m–1 s–1) at a given point (Table 2; Fig. 4). An AR event at a given location is categorized by locating the row associated with the maximum IVT and the column associated with the event duration. For example, a maximum IVT ≥500 and <750 kg m–1 s–1 would be classified as being of “moderate” intensity, and a duration ≥24 and <48 h would rank this as an AR category (Cat) 2 event. If the same event instead had a duration ≥48 h, it would be upgraded to an AR Cat 3 event, and a duration <24 h would downgrade it to an AR Cat 1 event. This system of classification works up and down the AR scale with two exceptions. First, the maximum category on the scale is AR Cat 5, even if event duration is ≥48 h. Second, “weak” ARs (i.e., those with maximum IVT ≥250 and <500 kg m–1 s–1, but with a duration <24 h) do not receive a categorical ranking on the AR scale, as represented by the gray area in Fig. 4 (even weak events require a minimum duration of 12 h).

Figure 5 and Table 3 provide examples of a range of AR Cat 1–5 events using both satellite-observed IWV [from Special Sensor Microwave Imager (SSM/I)] and IVT (from GFS) from the 2016/17 cool season. The synoptic-scale pattern associated with these events is characterized by a midlatitude cyclone–anticyclone pair over the northeast Pacific and an AR located between these features. The AR in each case is readily identified as a long and narrow region of large IWV and IVT extending poleward and eastward toward the U.S. West Coast. Generally, the magnitude of IWV and IVT increases as a function of increasing AR Cat, but because IWV magnitude is not directly considered as part of the AR scale, it does not increase uniformly as a function of increasing AR Cat. For example, the IWV magnitude associated with the listed AR Cat 5 event is smaller than that associated with the listed AR Cat 3 and 4 events.

FREQUENCY OF OCCURRENCE OF AR EVENTS IN THE WESTERN UNITED STATES BASED ON AR CATEGORY.

This section presents the average annual number of AR Cat 1–5 events from January 1980 to April 2017 over the western United States, based on IVT data calculated from Modern-Era Retrospective Analysis for Research and Applications (MERRA; Fig. 6; Rienecker et al. 2011).

ARs of each category tend to be most frequent over the northeastern Pacific Ocean as shown in earlier studies (e.g., Rutz et al. 2014; Guan and Waliser 2015). AR Cat 5 and Cat 4 events over land are generally limited to the coastal regions north of Point Conception (∼34°N) and west of the Cascades and Sierra Nevada. The map of AR Cat 5 events (Fig. 6a) highlights a local maximum in occurrence of the strongest ARs near the Oregon coast, reflecting patterns in the IVT return period analysis of Dettinger et al. (2018). Since IWV generally increases equatorward and low-level winds associated with ARs increase poleward (as shown in Ralph et al. 2017b), this likely represents the most favorable geographic overlap. It should also be noted that the scale identifies a few (once every few years) AR Cat 4 and 5 events along the very southern portion of the domain (i.e., the Baja Peninsula, Southern California, and southern Arizona). These events occur during summer and fall and are likely related to tropical cyclones, the North American monsoon, and other non-AR features but are identified by the scale, since it has no geometric requirements for meteorological features.

The distribution of AR Cat 2 and 3 events (Figs. 6c,d) more clearly shows the influence of topography on ARs over the western United States. As midlatitude cyclones supportive of ARs move eastward across this region, they interact with complex and formidable mountain ranges, which tend to result in cyclolysis and AR decay through rainout and other processes (Rutz et al. 2015). Therefore, achieving the combination of IVT magnitude and event duration associated with AR Cat 2 and 3 events becomes increasingly difficult as one moves inland. The distribution of AR Cat 1 and weak AR events (Figs. 6e,f) is characterized by a similar spatial pattern to that of higher AR Cat events, but their frequency is larger due to less restrictive criteria. Note that the frequency of events over the northern Great Plains is inflated by warm-season events featuring strong IVT.

In summary, the spatial distributions of weak AR events and AR Cat 1–5 events over the western United States closely resembles the pattern of AR frequency shown by Rutz et al. (2014), with the number of events becoming increasingly restricted to lower-elevation corridors and coastal regions as a function of increasing AR Cat (i.e., increasing IVT and event duration). AR Cat 4 and especially AR Cat 5 events are primarily limited to coastal locations, whereas weak AR events occur multiple times per year, on average, nearly everywhere over the western United States.

SPATIAL EXTENT AND PRECIPITATION IMPACTS OF AR CAT 1–5 EVENTS MAKING LANDFALL AT A NORTHERN CALIFORNIA COASTAL LOCATION.

It is common for strong AR events to have impacts across broad areas of the West, although the heaviest precipitation and greatest impacts are limited to where the AR is strongest and longest lasting. This section analyzes ARs that arrived over the MERRA grid cell closest to Bodega Bay (38°N, 123.125°W) to illustrate the seasonality of AR Cat events at that location, and the typical spatial extent of AR conditions and associated precipitation.

The seasonality of all AR Cat events near Bodega Bay is characterized by a rapid increase during October, a maximum during November–March (peaking in December), a gradual decline from April to June, and a minimum during July–September (Fig. 7). The strongest, AR Cats 4 and 5, events are primarily restricted to the October–March period, and are most frequent during January and February. During the July–September minimum, events stronger than AR Cat 2 are rare.

The average characteristics of ARs in each AR Cat are calculated and summarized in Table 4. It is not surprising that, from AR Cat 1 to Cat 5, the number of events decreases (from 268 to 10), the mean duration increases (from 21 to 72 h), and the maximum 3-h mean IVT increases (from 480 to 1,118 kg m–1 s–1). It is more surprising, however, that these maximum 3-h mean IVT values for AR Cats 3–5 are slightly less than the IVT thresholds used in determining these categories. This indicates that at least some AR Cat 3–5 events achieve their categorical rating by being “promoted” due to their duration exceeding 48 h, and that this happens more often than being “demoted” due to the duration being less than 24 h. The increases in mean AR duration, maximum 3-h mean IVT, and storm-total IVT progress steadily from AR Cat 1 to Cat 5, suggesting the scaling is representing systematic changes in the core characteristics of AR intensity and duration.

It is useful to examine the relationship between AR Cat events and major flooding along the Russian River {i.e., 12.2 m or 40 ft at the Guerneville stream gauge [U.S. Geological Survey (USGS) stream gauge 11467000]}. Of the 10 AR Cat 5 events near this location, 6 were associated with major flooding, 3 struck either early in the season or during a major drought when dry soils attenuated runoff, and 1 occurred when streamflow data were not available (Table 5). Another approach is to compare all major flood events on the Russian River to the presence of AR Cat events, as shown in Table 6. A number of these dates were consecutive, and the events can be viewed as 11 separate floods, with some being of 2, 3, or even 5 days in duration. Because of the typical 1–2-day time lag between precipitation and flooding on this river, the AR Cat levels on the day of the flood and on the 1–2 days before are assessed. As with the Ralph et al. (2006) study, which first showed the connection between landfalling ARs and flooding on the Russian River, all 11 floods in this analysis were associated with landfalling ARs. In particular, 3 out of 10 (33% of) AR Cat 5 events, 6 out of 22 (30% of) AR Cat 4 events, and only 2 out of 78 (3% of) AR Cat 3 were associated with major flooding at Guerneville.

The geographic spread of AR Cat intensities during AR Cat 5 landfalls near Bodega Bay is shown in Fig. 8a (for each grid point, this is calculated by determining the maximum AR Cat achieved during an AR Cat 5 event at Bodega Bay, and then averaging over the 10 such events). On average, AR Cat 4 and Cat 5 conditions are localized near Bodega Bay and the immediate West Coast. Meanwhile, at least AR Cat 1 conditions exist along the entire coast from Southern California to Washington and extend inland, affecting much of the western United States. A test of whether this method is contaminated by propagation of the AR along the West Coast was made by including up to 2 days before and after the AR Cat 5 conditions at Bodega Bay, and results were generally insensitive to this.

The precipitation associated with the 10 AR Cat 5 events near Bodega Bay is examined by calculating mean 3-day accumulations at COOP weather stations across the western United States. The 3-day accumulation window begins with the onset of each AR Cat 5 event, with 3 days chosen for simplicity (as in Ralph and Dettinger 2012), and because the average duration of AR Cat 5 events is ∼3 days (Table 4). The largest precipitation values occur over Northern California, where many sites exceed 150 mm, and some reach 250–300 mm (8–12 in.; Fig. 8b). Many sites in Oregon receive 100–150 mm, while sites as distant as Idaho and Utah receive 50–100 mm (a significant, but mostly beneficial, amount given the dryness of many inland areas). Figure 8c shows the maximum 3-day accumulation for each COOP site during any of the 10 AR Cat 5 events and reveals that many sites in Northern California experienced greater than 300 mm of precipitation, and some over 500 mm (∼20 in.) during at least one of the AR Cat 5 events. It is notable that events exceeding 400 mm (∼16 in.) in 3 days are very rare nationally, with only four COOP sites per year reaching this threshold (Ralph and Dettinger 2012).

The spatial distribution of precipitation over the western United States, during the occurrence of each AR Cat at Bodega Bay, is shown in Fig. 9 (top). Because many weaker ARs last much less than 3 days, this analysis uses a variable time window for accumulations, based on the observed times of each AR at Bodega Bay. The precipitation associated with AR Cat 1 and Cat 2 events is modest enough (mostly 25–75 mm) to be largely beneficial (unless preceded by a strong AR, or producing other hazards), whereas AR Cat 5 events produce well over 200 mm at many sites. To further this analysis, the frequency of occurrence of different 3-day precipitation totals, at all COOP sites in California, associated with different AR Cat events is shown in Fig. 10. It reveals that AR Cat 1–3 events group closely together in terms of precipitation, but that AR Cat 4 and 5 events have a much higher probability of producing extreme 3-day precipitation totals. This distinction suggests a significant increase in hazardous impacts during AR Cat 4 and 5 events, and it could be the basis for classifying these as “major” ARs, an approach analogous to that used in hurricane and tornado scales.

The large precipitation totals associated with the higher AR Cat levels in Fig. 9 (top) are often more hazardous falling as rain, adding immediate runoff that floods rivers and lowlands, and more beneficial falling as snow, increasing water storage via mountain snowpack. However, ARs are often warm storms, leading to increased snow levels and a skewing away from benefits toward hazards as heavy rainfall occurs at higher elevations. Indeed, Fig. 9 (bottom) shows that on days with AR landfall near Bodega Bay (together with the subsequent day), temperatures across the California–Nevada region become increasingly warmer as a function of AR Cat. This increases the chances that these storms will yield larger fractions of rain than snow, particularly at higher elevations that usually receive snow, contributing to a greater risk of floods and related hazards across this region.

AR SCALE FORECAST EXAMPLE.

The AR scale can be implemented using forecast data, and a preliminary concept is highlighted here by focusing on a series of landfalling AR events coinciding with the Oroville Dam crisis during early February 2017 (Fig. 11). In this example, the GFS forecast initialized at 0000 UTC 3 February 2017 is used to calculate AR Cats during a 5-day forecast period valid from 1200 UTC 5 February 2017 through 1200 UTC 10 February 2017, which represents forecast lead times from 60 to 180 h. Shown for this period are the analyzed maximum IVT (Fig. 11a) and the analyzed AR duration used to calculate the analyzed AR Cat (Fig. 11b), the forecast AR Cat (Fig. 11c), and the analyzed AR Cat (Fig. 11d). Note that Figs. 11a, 11b, and 11d are based on GFS analyses during this period, whereas Fig. 11c is based on the GFS forecast.

In this example, the 60–180-h forecast indicates a broad swath of AR Cat 5 conditions along the axis of the AR, which verified. The forecast also highlights the potential for AR Cat 4 conditions along the coast of Northern California and southern Oregon, as well as AR Cat 3 conditions over the northern Sierra Nevada (near the Oroville Dam) and AR Cat 2 conditions extending into the Great Basin (Fig. 11c). In general, verification is ∼1 AR Cat stronger than predicted forecast over much of the western United States, and ∼1–2 AR Cat stronger from coastal California northeastward into eastern Oregon, Idaho, and southwestern Montana. AR Cat 5 conditions occurred over land north of San Francisco Bay, AR Cat 4+ conditions occurred along a much greater stretch of the coast and over the Oroville Dam, and AR Cat 3 conditions occurred much farther inland than anticipated (Fig. 11d). Taking a broader view, the distribution of higher (lower) AR Cat verifications along the northwestern and southeastern (southwestern and northeastern) edges of the AR suggests a more zonally oriented AR than was anticipated in the forecast. In addition, the underforecast AR Cat over the northern Rockies indicates that the GFS struggled to capture the inland penetration of the AR, particularly downstream from the core of the AR. Hence, we stress that while the AR scale provides a solid framework for characterizing the strength and impacts of an AR, it is dependent on an accurately modeled atmospheric forecast, and will only be as reliable as the forecast model being used.

While the concept shown here is only preliminary, it is the authors’ plan to implement the AR forecast operationally over the coming months, with a finished product likely resembling that shown here.

SUMMARY AND FUTURE WORK.

This paper presents a scale to characterize atmospheric river strength and impacts in a way that is both useful to scientists and conducive to communication with nonexperts. The scale uses the intensity of vertically integrated water vapor transport and AR event duration to characterize AR strength, providing a framework for differentiating between the impacts of those that are primarily beneficial and those that are primarily hazardous. The scale is also readily applied to gridded datasets such as atmospheric reanalyses, weather forecasts, and climate projections. Given an increased focus on AR-related science and impacts, it is likely that this AR Cat scale will be widely used to communicate the benefits and hazards associated with ARs both in the western United States and in other regions where ARs contribute strongly to hydrometeorological impacts.

In addition to introducing the AR Cat scale, this paper provides a few diagnostics regarding their frequency of occurrence and their impacts on precipitation. Figure 12 shows the average annual maximum AR Cat event across the western United States and illustrates that the scale is broadly applicable across the region. AR Cat 4 or 5 events occur primarily along the Northern California and Pacific Northwest coasts, whereas the strongest ARs affecting the Southern California coast at roughly annual scales are typically AR Cat 2 or 3. Most of the West (except the highest interior) experience AR Cat 1 events at least annually. In addition, the well-established connection between ARs and extreme precipitation in the western United States is highlighted, revealing that the strongest AR events often affect the entire West to some degree. It was shown that 30% of landfalling AR Cat 4 and 5 events (combined) were associated with significant floods on the Russian River, and that all of the floods in the period studied were associated with landfalling ARs.

A few shortcomings of the AR scale are worth pointing out. First, some high-impact events (e.g., debris flows) result primarily from very intense rainfall rates that can be short-lived (Oakley et al. 2017). Hence, the duration requirement of the scale may overexert itself by not recognizing weather systems that will be short-lived, but capable of producing such intense rates. Second, the impacts associated with a particular AR Cat may vary due to other factors such as temperature throughout the column, and particularly the rain/snow line. More broadly, since the AR scale is not linked to a particular location, the impacts associated with the AR Cats will vary spatially as a function of geographic considerations such as topography, land surface type, and antecedent conditions.

Future work is envisioned to more fully describe the transition between primarily beneficial and primarily hazardous AR Cat events in terms of a variety of hydrometeorological benefits (e.g., increased reservoir levels, drought relief, and water supply) and hazards (e.g., heavy rain, flooding, and high winds). Since the AR scale is easily applied to gridded datasets, these analyses can shed light on how the relationship between AR Cat and impacts varies geographically. The unique mesonet throughout the western United States (Ralph et al. 2014; White et al. 2013), combined with storm reports from the National Weather Service, is uniquely tailored for this work. The scale presented here can also be used, along with verification studies such as those by Wick et al. (2013), DeFlorio et al. (2018), and Nardi et al. (2018), to reveal whether AR forecast skill varies as a function of AR Cat over different locales. An intriguing new direction is emerging on the correspondence between strong ARs and strong extratropical cyclogenesis (Eiras-Barca et al. 2018; Zhang et al. 2019), which could benefit from an objective scaling of ARs. Finally, the AR scale, along with information from numerical weather prediction model ensembles, can be leveraged to produce gridded, probabilistic forecasts of AR intensity, which should prove valuable to a variety of users.

APPENDIX: KEY TERMINOLOGY AND CALCULATION OF IVT.

A clear understanding of the AR scale presented here benefits from the definition of a few key terms.

  • An atmospheric river (AR) is a long, narrow, transient corridor of strong horizontal water vapor transport that is typically associated with a low-level jet stream ahead of the cold front of an extratropical cyclone (American Meteorological Society 2018). An AR can be thought of as both an Eulerian object and a Lagrangian process—it encompasses a three-dimensional region of the atmosphere in which certain dynamic processes are taking place, with a shape and location that change as these processes evolve.

  • AR conditions denote the instantaneous presence of IVT ≥ 250 kg m–1 s–1 at a point (i.e., in an Eulerian sense).

  • An AR event refers to the full period of time over which AR conditions occur at a fixed geographical point (i.e., applies in an Eulerian sense). Note that the AR scale presented is based on AR conditions and AR events at a point and does not account for the movement or tracking of ARs themselves (i.e., as objects).

  • The spatial and temporal distribution of AR events as a function of AR scale category relies on vertically integrated vapor transport (IVT), which is calculated as

 
formula

where q is the specific humidity, Vh is the horizontal wind vector, g is the acceleration due to gravity, pb is 1,000 hPa, and pt is 200 hPa (e.g., Neiman et al. 2008; Moore et al. 2012). The IVT is calculated using atmospheric data from the MERRA reanalysis at 0.5° × 0.625° horizontal resolution (Rienecker et al. 2011). Vertical levels used to calculate IVT are every 25 hPa from 1,000 to 700 hPa and every 50 hPa from 700 to 200 hPa.

REFERENCES

REFERENCES
Albano
,
C.
,
M.
Dettinger
, and
C.
Soulard
,
2017
:
Influence of atmospheric rivers on vegetation productivity and fire patterns in the southwestern U.S
.
J. Geophys. Res. Biogeosci.
,
122
,
308
323
, https://doi.org/10.1002/2016JG003608.
American Meteorological Society
,
2018
: Atmospheric river. Glossary of Meteorology, http://glossary.ametsoc.org/wiki/Atmospheric_river.
Backes
,
T. M.
,
M. L.
Kaplan
,
R.
Schumer
, and
J. F.
Mejia
,
2015
:
A climatology of the vertical structure of water vapor transport to the Sierra Nevada in cool season atmospheric river precipitation events
.
J. Hydrometeor.
,
16
,
1029
1047
, https://doi.org/10.1175/JHM-D-14-0077.1.
Bao
,
J.-W.
,
S. A.
Michelson
,
P. J.
Neiman
,
F. M.
Ralph
, and
J. M.
Wilczak
,
2006
:
Interpretation of enhanced integrated water vapor bands associated with extratropical cyclones: Their formation and connection to tropical moisture
.
Mon. Wea. Rev.
,
134
,
1063
1080
, https://doi.org/10.1175/MWR3123.1.
Cheng
,
B. S.
,
A. L.
Chang
,
A.
Deck
, and
M. C.
Ferner
,
2016
:
Atmospheric rivers and the mass mortality of wild oysters: Insight into an extreme future?
Proc. Biol. Sci.
,
283
, 20161462, https://doi.org/10.1098/rspb.2016.1462.
Cordeira
,
J. M.
,
F. M.
Ralph
, and
B. J.
Moore
,
2013
:
The development and evolution of two atmospheric rivers in proximity to western North Pacific tropical cyclones in October 2010
.
Mon. Wea. Rev.
,
141
,
4234
4255
, https://doi.org/10.1175/MWR-D-13-00019.1.
Cordeira
,
J. M.
,
F. M.
Ralph
,
A.
Martin
,
N.
Gaggini
,
J. R.
Spackman
,
P. J.
Neiman
,
J. J.
Rutz
, and
R.
Pierce
,
2017
:
Forecasting atmospheric rivers during CalWater 2015
.
Bull. Amer. Meteor. Soc.
,
98
,
449
459
, https://doi.org/10.1175/BAMS-D-15-00245.1.
Dacre
,
H. F.
,
P. A.
Clark
,
O.
Martinez-Alvarado
,
M. A.
Stringer
, and
D. A.
Lavers
,
2015
:
How do atmospheric rivers form?
Bull. Amer. Meteor. Soc.
,
96
,
1243
1255
, https://doi.org/10.1175/BAMS-D-14-00031.1.
DeFlorio
,
M. J.
,
D. E.
Waliser
,
B.
Guan
,
D. A.
Lavers
,
F. M.
Ralph
, and
F.
Vitart
,
2018
:
Global assessment of atmospheric river prediction skill
.
J. Hydrometeor.
,
19
,
409
426
, https://doi.org/10.1175/JHM-D-17-0135.1.
Dettinger
,
M. D.
,
2004
: Fifty-two years of “Pineapple-Express” storms across the west coast of North America. PIER Project Rep. CEC-500-2005-004, California Energy Commission, 15 pp., www.energy.ca.gov/2005publications/CEC-500-2005-004/CEC-500-2005-004.PDF.
Dettinger
,
M. D.
,
2013
:
Atmospheric rivers as drought busters on the U.S. West Coast
. J. Hydrometeor.
,
14
,
1721
1732
, https://doi.org/10.1175/JHM-D-13-02.1.
Dettinger
,
M. D.
, and
D.
Cayan
,
2014
:
Drought and the California delta—A matter of extremes
.
San Francisco Estuary Watershed Sci
.,
12
(
2
), https://doi.org/10.15447/sfews.2014v12iss2art4.
Dettinger
,
M. D.
,
F. M.
Ralph
,
T.
Das
,
P. J.
Neiman
, and
D.
Cayan
,
2011
:
Atmospheric rivers, floods, and the water resources of California
.
Water
,
3
,
455
478
, https://doi.org/10.3390/w3020445.
Dettinger
,
M. D.
,
F. M.
Ralph
, and
D.
Lavers
,
2015
:
Setting the stage for a global science of atmospheric rivers
.
Eos, Trans. Amer. Geophys. Union
,
96
, https://doi.org/10.1029/2015EO038675.
Dettinger
,
M. D.
,
F. M.
Ralph
, and
J. J.
Rutz
,
2018
:
Empirical return periods of the most intense vapor transports during historical atmospheric river landfalls on the
U.S. West Coast. J. Hydrometeor.
,
19
,
1363
1377
, https://doi.org/10.1175/JHM-D-17-0247.1.
Eiras-Barca
,
J.
,
A. M.
Ramos
,
J. G.
Pinto
,
R. M.
Trigo
,
M. L. R.
Liberato
, and
G.
Miguez-Macho
,
2018
:
The concurrence of atmospheric rivers and explosive cyclogenesis in the North Atlantic and North Pacific basins
.
Earth Syst. Dyn.
,
9
,
91
102
, https://doi.org/10.5194/esd-9-91-2018.
Elsner
,
J.
, and
A. B.
Kara
,
1999
: Hurricanes of the North Atlantic: Climate and Society.
Oxford University Press
,
488
pp.
Florsheim
,
J.
, and
M.
Dettinger
,
2015
:
Promoting atmospheric-river and snowmelt-fueled biogeomorphic processes by restoring river-floodplain connectivity in California’s Central Valley
. Geomorphic Approaches to Integrated Floodplain Management of Lowland Fluvial Systems in North America and Europe,
P.
Hudson
and
H.
Middelkoop
, Eds.,
Springer
,
119
141
, https://doi.org/10.1007/978-1-4939-2380-9_6.
Fujita
,
T.
,
1981
:
Tornadoes and downbursts in the context of generalized planetary scales
.
J. Atmos. Sci.
,
38
,
1511
1534
, https://doi.org/10.1175/1520-0469(1981)038<1511:TADITC>2.0.CO;2.
Guan
,
B.
, and
D. E.
Waliser
,
2015
:
Detection of atmospheric rivers: Evaluation and application of an algorithm for global studies
.
J. Geophys. Res. Atmos.
,
120
,
12 514
12 535
, https://doi.org/10.1002/2015JD024257.
Guan
,
B.
,
N. P.
Molotch
,
D. E.
Waliser
,
E. J.
Fetzer
, and
P. J.
Neiman
,
2010
:
Extreme snowfall events linked to atmospheric rivers and surface air temperature via satellite measurements
.
Geophys. Res. Lett.
,
37
,
L20401
, https://doi.org/10.1029/2010GL044696.
Guan
,
B.
,
D. E.
Waliser
,
F. M.
Ralph
,
E. J.
Fetzer
, and
P. J.
Neiman
,
2016
:
Hydrometeorological characteristics of rain-on-snow events associated with atmospheric rivers
.
Geophys. Res. Lett.
,
43
,
2964
2973
, https://doi.org/10.1002/2016GL067978.
Hatchett
,
B. J.
,
S.
Burak
,
J. J.
Rutz
,
N. S.
Oakley
,
E. H.
Bair
, and
M. L.
Kaplan
,
2017
:
Avalanche fatalities during atmospheric river events in the western United States
.
J. Hydrometeor.
,
18
,
1359
1374
, https://doi.org/10.1175/JHM-D-16-0219.1.
Herbst
,
D. B.
, and
S. D.
Cooper
,
2010
:
Before and after the deluge—Rain-on-snow flooding effects on aquatic invertebrate communities of small streams in the Sierra Nevada, California
.
J. N. Amer. Benthol. Soc.
,
29
,
1354
1366
, https://doi.org/10.1899/09-185.1.
Khouakhi
,
A.
, and
G.
Villarini
,
2016
:
On the relationship between atmospheric rivers and high sea water levels along the U.S. West Coast
. Geophys. Res. Lett.
,
43
,
8815
8822
, https://doi.org/10.1002/2016GL070086.
Kocin
,
P. J.
, and
L. W.
Uccellini
,
2004
:
A snowfall impact scale derived from Northeast storm snowfall distributions
.
Bull. Amer. Meteor. Soc.
,
85
,
177
194
, https://doi.org/10.1175/BAMS-85-2-177.
Konrad
,
C. P.
, and
M. D.
Dettinger
,
2017
:
Flood runoff in relation to water vapor transport by atmospheric rivers over the western United States, 1949–2015
.
Geophys. Res. Lett.
,
44
,
11 456
11 462
, https://doi.org/10.1002/2017GL075399.
Lamjiri
,
M. A.
,
M. D.
Dettinger
,
F. M.
Ralph
, and
B.
Guan
,
2017
:
Hourly storm characteristics along the U.S. West Coast: Role of atmospheric rivers in extreme precipitation
.
Geophys. Res. Lett.
,
44
,
11 456
11 462
, https://doi.org/10.1002/2017GL075399.
Lavers
,
D. A.
,
R. P.
Allan
,
E. F.
Wood
,
G.
Villarini
,
D. J.
Brayshaw
, and
A. J.
Wade
,
2011
:
Winter floods in Britain are connected to atmospheric rivers
.
Geophys. Res. Lett.
,
38
,
L23803
, https://doi.org/10.1029/2011GL049783.
Lavers
,
D. A.
,
D. E.
Waliser
,
F. M.
Ralph
, and
M. D.
Dettinger
,
2016
:
Predictability of horizontal water vapor transport relative to precipitation: Enhancing situational awareness for forecasting western U.S. extreme precipitation and flooding
.
Geophys. Res. Lett.
,
43
,
2275
2282
, https://doi.org/10.1002/2016GL067765.
Moore
,
B. J.
,
P. J.
Neiman
,
F. M.
Ralph
, and
F.
Barthold
,
2012
:
Physical processes associated with heavy flooding rainfall in Nashville, Tennessee and vicinity during 1–2 May 2010: The role of an atmospheric river and mesoscale convective systems
.
Mon. Wea. Rev.
,
140
,
358
378
, https://doi.org/10.1175/MWR-D-11-00126.1.
Nardi
,
K. M.
,
E. A.
Barnes
, and
F. M.
Ralph
,
2018
:
Assessment of numerical weather prediction model reforecasts of the occurrence, intensity, and location of atmospheric rivers along the West Coast of North America
.
Mon. Wea. Rev.
,
146
,
3343
3362
, https://doi.org/10.1175/MWR-D-18-0060.1.
Neiman
,
P. J.
,
F. M.
Ralph
,
G. A.
Wick
,
J. D.
Lundquist
, and
M. D.
Dettinger
,
2008
:
Meteorological characteristics and overland precipitation impacts of atmospheric rivers affecting the West Coast of North America based on eight years of SSM/I satellite observations
.
J. Hydrometeor.
,
9
,
22
47
, https://doi.org/10.1175/2007JHM855.1.
Neiman
,
P. J.
,
L. J.
Schick
,
F. M.
Ralph
,
M.
Hughes
, and
G. A.
Wick
,
2011
:
Flooding in western Washington: The connection to atmospheric rivers
.
J. Hydrometeor.
,
12
,
1337
1358
, https://doi.org/10.1175/2011JHM1358.1.
Oakley
,
N. S.
,
J. T.
Lancaster
,
M. L.
Kaplan
, and
F. M.
Ralph
,
2017
:
Synoptic conditions associated with cool season post-fire debris flows in the Transverse Ranges of southern California
.
Nat. Hazards
,
88
,
327
354
, https://doi.org/10.1007/s11069-017-2867-6.
Ralph
,
F. M.
, and
M. D.
Dettinger
,
2012
:
Historical and national perspectives on extreme West Coast precipitation associated with atmospheric rivers during December 2010
.
Bull. Amer. Meteor. Soc.
,
93
,
783
790
, https://doi.org/10.1175/BAMS-D-11-00188.1.
Ralph
,
F. M.
,
P. J.
Neiman
, and
G. A.
Wick
,
2004
:
Satellite and CALJET aircraft observations of atmospheric rivers over the eastern North Pacific Ocean during the winter of 1997/98
.
Mon. Wea. Rev.
,
132
,
1721
1745
, https://doi.org/10.1175/1520-0493(2004)132<1721:SACAOO>2.0.CO;2.
Ralph
,
F. M.
,
P. J.
Neiman
,
G. A.
Wick
,
S. I.
Gutman
,
M. D.
Dettinger
,
D. R.
Cayan
, and
A. B.
White
,
2006
:
Flooding on California’s Russian River: Role of atmospheric rivers
.
Geophys. Res. Lett.
,
33
,
L13801
, https://doi.org/10.1029/2006GL026689.
Ralph
,
F. M.
,
P. J.
Neiman
,
G. N.
Kiladis
,
K.
Weickman
, and
D. W.
Reynolds
,
2011
:
A multi-scale observational case study of a Pacific atmospheric river exhibiting tropical-extratropical connections and a mesoscale frontal wave
.
Mon. Wea. Rev.
,
139
,
1169
1189
, https://doi.org/10.1175/2010MWR3596.1.
Ralph
,
F. M.
,
T.
Coleman
,
P. J.
Neiman
,
R. J.
Zamora
, and
M. D.
Dettinger
,
2013
:
Observed impacts of duration and seasonality of atmospheric-river landfalls on soil moisture and runoff in coastal Northern California
.
J. Hydrometeor.
,
14
,
443
459
, https://doi.org/10.1175/JHM-D-12-076.1.
Ralph
,
F. M.
, and Coauthors
,
2014
:
A vision for future observations for western U.S. extreme precipitation and flooding
.
J. Contemp. Water Res. Educ.
,
153
,
16
32
, https://doi.org/10.1111/j.1936-704X.2014.03176.x.
Ralph
,
F. M.
, and Coauthors
,
2016
:
CalWater field studies designed to quantify the roles of atmospheric rivers and aerosols in modulating U.S. West Coast precipitation in a changing climate
.
Bull. Amer. Meteor. Soc.
,
97
,
1209
1228
, https://doi.org/10.1175/BAMS-D-14-00043.1.
Ralph
,
F. M.
, and Coauthors
,
2017a
:
Atmospheric rivers emerge as a global science and applications focus
.
Bull. Amer. Meteor. Soc.
,
98
, https://doi.org/10.1175/BAMS-D-16-0262.1.
Ralph
,
F. M.
, and Coauthors
,
2017b
:
Dropsonde observations of total water vapor transport within North Pacific atmospheric rivers
.
J. Hydrometeor.
,
18
,
2577
2596
, https://doi.org/10.1175/JHM-D-17-0036.1.
Ralph
,
F. M.
,
M. D.
Dettinger
,
M. M.
Cairns
,
T. J.
Galarneau
, and
J.
Eylander
,
2018
:
Defining “atmospheric river”: How the Glossary of Meteorology helped resolve a debate
.
Bull. Amer. Meteor. Soc.
,
99
,
837
839
, https://doi.org/10.1175/BAMS-D-17-0157.1.
Ramos
,
A. M.
,
R. M.
Trigo
,
M. L. R.
Liberato
, and
R.
Tome
,
2015
:
Daily precipitation extreme events in the Iberian Peninsula and its association with atmospheric rivers
.
J. Hydrometeor.
,
16
,
579
597
, https://doi.org/10.1175/JHM-D-14-0103.1.
Rienecker
,
M.
, and Coauthors
,
2011
:
MERRA: NASA’s Modern-Era Retrospective Analysis for Research and Applications
.
J. Climate
,
24
,
3624
3648
, https://doi.org/10.1175/JCLI-D-11-00015.1.
Rutz
,
J. J.
,
W. J.
Steenburgh
, and
F. M.
Ralph
,
2014
:
Climatological characteristics of atmospheric rivers and their inland penetration over the western United States
.
Mon. Wea. Rev.
,
142
,
905
921
, https://doi.org/10.1175/MWR-D-13-00168.1.
Rutz
,
J. J.
,
W. J.
Steenburgh
, and
F. M.
Ralph
,
2015
:
The inland penetration of atmospheric rivers over western North America: A Lagrangian analysis
.
Mon. Wea. Rev.
,
143
,
1924–1944
, https://doi.org/10.1175/MWR-D-14-00288.1.
Shields
,
C. A.
, and Coauthors
,
2018
:
Atmospheric River Tracking Method Intercomparison Project (ARTMIP): Project goals and experimental design
.
Geosci. Model Dev.
,
11
,
2455
2474
, https://doi.org/10.5194/gmd-11-2455-2018.
Sodemann
,
H.
, and
A.
Stohl
,
2013
:
Moisture origin and meridional transport in atmospheric rivers and their association with multiple cyclones
.
Mon. Wea. Rev.
,
141
,
2850
2868
, https://doi.org/10.1175/MWR-D-12-00256.1.
Stohl
,
A.
,
C.
Forster
, and
H.
Sodemann
,
2008
:
Remote sources of water vapor forming precipitation on the Norwegian west coast at 60° N—A tale of hurricanes and an atmospheric river
.
J. Geophys. Res.
,
113
,
D05102
, https://doi.org/10.1029/2007JD009006.
Viale
,
M.
, and
M. N.
Nuñez
,
2011
:
Climatology of winter orographic precipitation over the subtropical central Andes and associated synoptic and regional characteristics
.
J. Hydrometeor.
,
12
,
481
507
, https://doi.org/10.1175/2010JHM1284.1.
Waliser
,
D.
, and
B.
Guan
,
2017
:
Extreme winds and precipitation during landfall of atmospheric rivers
.
Nat. Geosci.
,
10
,
179
183
, https://doi.org/10.1038/ngeo2894.
Warner
,
M. D.
,
C. F.
Mass
, and
E. P.
Salathé
Jr.
,
2012
:
Wintertime extreme precipitation events along the Pacific Northwest coast: Climatology and synoptic evolution
.
Mon. Wea. Rev.
,
140
,
2021
2043
, https://doi.org/10.1175/MWR-D-11-00197.1.
White
,
A. B.
, and Coauthors
,
2013
:
A twenty-first-century california observing network for monitoring extreme weather events
.
J. Atmos. Oceanic Technol.
,
30
,
1585
1603
, https://doi.org/10.1175/JTECH-D-12-00217.1.
Wick
,
G. A.
,
P. J.
Neiman
,
F. M.
Ralph
, and
T. M.
Hamill
,
2013
:
Evaluation of forecasts of the water vapor signature of atmospheric rivers in operational numerical weather prediction models
.
Wea. Forecasting
,
28
,
1337
1352
, https://doi.org/10.1175/WAF-D-13-00025.1.
Wuebbles
,
D. J.
, and Coauthors
,
2017
:
Our globally changing climate
. Climate Science Special Report: Fourth National Climate Assessment, Vol. 1,
D. J.
Wuebbles
et al.
, Eds.,
U.S. Global Change Research Program
,
35
72
, https://doi.org/10.7930/J08S4N35.
Young
,
A. M.
,
K. T.
Skelly
, and
J. M.
Cordeira
,
2017
:
High-impact hydrologic events and atmospheric rivers in California: An investigation using the NCEI Storm Events Database
.
Geophys. Res. Lett.
,
44
,
3393
3401
, https://doi.org/10.1002/2017GL073077.
Zhang
,
Z.
,
F. M.
Ralph
, and
M.
Zheng
,
2019
:
The relationship between extratropical cyclone strength and atmospheric river intensity and position
.
Geophys. Res. Lett.
, https://doi.org/10.1029/2018GL079071, in press.
Zhu
,
Y.
, and
R. E.
Newell
,
1998
:
A proposed algorithm for moisture fluxes from atmospheric rivers
.
Mon. Wea. Rev.
,
126
,
725
735
, https://doi.org/10.1175/1520-0493(1998)126<0725:APAFMF>2.0.CO;2.

Footnotes

*

Retired.

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1

The term water year is used extensively in the western United States based on the annual cycle of precipitation and runoff. For example, water year 2016 started on 1 October 2015 and ended on 30 September 2016.