1. Introduction
Water resources in California spatially depend on variable precipitation patterns at daily, monthly, and annual time scales (Dettinger et al. 2011). Precipitation events occur with a frequency of ∼25–45 events per year in Northern California and ∼2–15 events per year in Southern California (Lamjiri et al. 2018), and they generate ∼50% of the state’s annual precipitation on ∼5–20 days (Dettinger et al. 2011). This regional variability extends to individual watersheds where precipitation and its impacts from a single event may range from beneficial (e.g., increase water supply, drought amelioration) in one watershed to hazardous (e.g., flooding) in a nearby watershed (e.g., Ralph et al. 2003; Neiman et al. 2011). The source of ∼30%–50% of California’s total precipitation, its variability, and subsequent impacts to the hydrosphere is often attributed to the occurrence or nonoccurrence of Pacific winter storms, such as extratropical cyclones and/or landfalling atmospheric rivers (ARs; e.g., Neiman et al. 2002, 2008a,b; Ralph et al. 2003, 2004, 2006; Dettinger et al. 2011; Dettinger 2013; Ralph and Dettinger 2012; Payne and Magnusdottir 2014). The goal of this study is to use a watershed-centric framework to 1) summarize the relationships among California’s landfalling ARs and their influence on both annual total precipitation and precipitation extremes and 2) specifically identify the physical characteristics of landfalling ARs that may best relate to variability in watershed-scale precipitation across the state.
ARs are long and narrow corridors of enhanced integrated water vapor (IWV) and integrated vapor transport (IVT) primarily driven by a pre-cold-frontal low-level jet stream of an extratropical cyclone (American Meteorological Society 2019). A majority of the IVT (∼75%) in ARs occurs in the lowest 2.25 km of the atmosphere (Ralph et al. 2006), often leading to orographic precipitation along coastal and inland mountain ranges where ARs make landfall across the western United States (e.g., Ralph et al. 2004, 2005, 2017; Neiman et al. 2008a, 2011; Rutz et al. 2014, 2015) with a frequency of ∼10–20 times per year across Northern California and ∼5–15 times per year across Southern California (Neiman et al. 2008a; Rutz et al. 2014). The orographic precipitation is maximized where lower-tropospheric water vapor flux is perpendicular to topographic barriers. Therefore, for a given water vapor flux magnitude and direction, spatial variability in landfall location and in topography (e.g., slope and aspect) may influence spatial variability in precipitation and flooding (e.g., Ralph et al. 2003, 2004, 2006; Neiman et al. 2011; Hughes et al. 2014; Hecht and Cordeira 2017). For example, Neiman et al. (2011) demonstrated that the annual peak daily streamflow in nearby watersheds in the Pacific Northwest occurred in association with different low-level winds in landfalling ARs due to the region’s complex topography, basin orientations, and related rain shadowing. This spatial variability in observed precipitation may also contain similar spatial variability in associated hydrometeorological hazards depending upon local antecedent conditions and land surface characteristics such as soil moisture and porosity, impervious surfaces, prior wildfire activity, or upstream snowpack. For this reason, landfalling ARs often simultaneously produce drought amelioration (Dettinger 2013), reservoir and snowpack replenishment (Guan et al. 2010, 2012), floods and insured flood losses (Corringham et al. 2019), and postfire debris flows and landslides (Oakley et al. 2017; Young et al. 2017; Cordeira et al. 2019) and necessitate watches and warnings by the National Weather Service (e.g., Cordeira et al. 2018; Bartlett and Cordeira 2021).
In addition to variability in terrain slope and aspect, the spatial variability in observed precipitation may also be influenced by meteorological processes related to 1) variability in water vapor flux magnitude and direction associated with the landfalling AR and parent midlatitude cyclone characteristics (e.g., Hecht and Cordeira 2017; Hu et al. 2017; Zhang et al. 2018), 2) vertical distributions of water vapor flux (e.g., Kaplan et al. 2009, 2012; Backes et al. 2015; Hecht and Cordeira 2017), 3) stability such as buoyant or slantwise convection along narrow cold frontal rainbands (e.g., Ralph et al. 2011; Cannon et al. 2020) or the presence of barrier jets (e.g., Kingsmill et al. 2013; Neiman et al. 2013b; Ralph et al. 2016), 4) precipitation processes such as the seeding of orographic precipitation from higher-altitude synoptic-scale driven precipitation (e.g., Bergeron 1965; Browning et al. 1974; Storebø 1976; Hill and Browning 1979; Browning 1980; Hill 1983; Neiman et al. 2002; Ralph et al. 2003) and related microphysical processes (e.g., Creamean et al. 2013), or 5) a reduction or enhancement in downstream orographic precipitation due to rain shadowing from upstream topography or the presence of terrain gaps, respectively. For example, orographic precipitation during landfalling ARs is typically reduced across the inland northern Sierra Nevada due to a decrease in water vapor transport from shadowing by the Coastal Ranges. Alternatively, precipitation may be locally enhanced when water vapor flux is directed through a terrain gap in the Coastal Ranges such as near the San Francisco Bay or Petaluma areas (e.g., Neiman et al. 2002, 2013b; Rutz et al. 2014; White et al. 2015; Lamjiri et al. 2018). The presence of the southerly Sierra barrier jet along the west slope of the northern Sierra Nevada during a landfalling AR may also enhance precipitation across the northern Central Valley and northeast California near Lake Shasta (e.g., Neiman et al. 2013b; Ralph et al. 2016).
Across the northern coast of California, the storm-total bulk-upslope water vapor flux associated with a landfalling AR is related to variability in observed storm-total precipitation (Ralph et al. 2013). The bulk-upslope water vapor flux is the upslope component of lower-tropospheric water vapor flux that has been projected onto the local terrain gradient vector and represents a portion of the IVT vector (Neiman et al. 2002, 2009), herein referred to as the “projected IVT.” The time integration of the upslope water vapor flux (i.e., considering both the duration and intensity of upslope water vapor flux) explains 74% of the variance in storm-total precipitation in the coastal terrain of the Russian River watershed north of San Francisco (Ralph et al. 2013). Spatial variability in either water vapor flux direction or watershed slope/aspect and elevation can therefore influence the duration and intensity of upslope water vapor flux and result in spatial variability in storm-total precipitation within individual events from one watershed to another (Hughes et al. 2014). For example, variability in water vapor flux direction of only a few degrees upon AR landfall resulted in extreme precipitation and localized flooding on the Pescadero Creek near Santa Cruz that did not occur among neighboring watersheds during a landfalling AR on 2–3 February 1998 (Ralph et al. 2003). Variability in water vapor flux direction or watershed slope/aspect and elevation may also greatly influence subsequent runoff and streamflow (Neiman et al. 2011; Hughes et al. 2014).
This study is motivated by the aforementioned localized studies that investigate the influence of water vapor flux magnitude and direction, often illustrated via the IVT vector or bulk upslope water vapor flux, during landfalling ARs on coastal California watershed precipitation. By adopting a watershed framework this study will expand upon the IVT-related characteristics of landfalling ARs that 1) influence spatial and temporal variability in precipitation, and in turn 2) help inform AR-related decisions regarding water resources and water resources management among watersheds in California. The concluding discussion of this study will summarize results for those Hydrologic Unit Code 8 (HUC-8) watersheds which contain California’s largest lakes and reservoirs in order to provide situational awareness in support of Forecast-Informed Reservoir Operations (FIRO; Jasperse et al. 2020). FIRO leverages the skill of modern numerical weather prediction models and hydrologic forecasting techniques to inform water resources management by maximizing water supply while minimizing flood risk within a catchment area, its reservoir, and regions downstream. Model results (Delaney et al. 2020) and FIRO in practice at Lake Mendocino within the Russian River watershed, where landfalling ARs drive an overwhelming majority of annual precipitation and almost all precipitation extremes and floods (Ralph et al. 2013), indicate that FIRO can increase median storage by >30% over conventional reservoir operations while maintaining water supply, mitigating flood risk, and providing healthy ecosystems.
2. Data and methods
The Parameter-Elevation Regressions on Independent Slopes Model (PRISM) developed by Oregon State University is used for quantitative precipitation estimates (QPEs) in this study (Daly et al. 1994, 2002, 2008). These data are a combination of point observations, a digital elevation model, and other geographical datasets that are modeled to generate a 4 km × 4 km gridded daily QPEs ending at 1200 UTC each day. A variety of observational networks across California provide input data for PRISM, including remote automatic weather stations; the Community Collaborative Rain, Hail and Snow Network; snow telemetry stations; automated surface observing systems; and the U.S. Climate Reference Network. To estimate precipitation in data-sparse regions, and potentially localized points within watersheds with complex topography, the PRISM model uses both “climatologically aided interpolation” (1981–present) and the Advanced Hydrometeorological Prediction System (2002–present) to calculate a multifactor physiographic-weighted climate–elevation regression (Daly et al. 1994; PRISM Climate Group 2019). These factors include elevation, coastal proximity, topographic facet orientation, boundary layer exposure, topography, and orography (Di Luzio et al. 2008), which all may influence precipitation on scales < 10 km (Daly et al. 1994; Daley 1991; Sharples et al. 2005; Daly 2006). These factors also consider climatological orographic precipitation gradients and may locally contain large errors (Bishop and Beier 2013), especially in California where point-based QPE errors in individual storms can result in biases of ∼20% in storm-total water year precipitation (Lundquist et al. 2015). This study uses the daily 4-km PRISM QPE in order to calculate the daily mean areal precipitation (MAP) for each of 140 HUC-8-sized watersheds that intersect or are contained within the state boundary of California (Fig. 1). The 4-km gridded dataset provides an average of 222 grid points within each of the 140 watersheds whose areal average likely mitigates some, but not all, of the localized errors within any given watershed identified in the Lundquist et al. (2015) study (i.e., the areal average likely smooths out some of the aforementioned intrawatershed QPE errors). Daily MAP is also summed by water year (WY), from 1 October to 30 September, and averaged over the study’s 38-yr (1982–2019) period to generate average annual MAP values and departures from the study period 38-yr normal. Daily MAP extremes were defined as the top 5% of “wet” daily MAP days (i.e., >0 mm) over the entire 38-yr period in each watershed.
Map of California HUC-8 watersheds (black lines) and topography (m; shaded) with coastal CFSR grid points (black circles) and the four focal watersheds (shaded; see legend) used and discussed in this study. The locations of the 12 lakes/reservoirs highlighted in Table 2 are denoted by triangles (see legend). Image created using QGIS 3.12 with topography and watershed boundaries obtained from the United States Geological Survey.
Citation: Journal of Hydrometeorology 23, 9; 10.1175/JHM-D-21-0119.1
The National Oceanic and Atmospheric Administration (NOAA) National Centers for Environmental Prediction (NCEP) Climate Forecast System (CFS) Reanalysis (Saha et al. 2006) and CFS version 2 operational analysis (CFSv2) (Saha et al. 2014) datasets are used to calculate IVT along the coast of California following the methodology of Neiman et al. (2008a). The CFSR datasets are available four times daily with 0.5° latitude × 0.5° longitude grid spacing. This study uses these data at 25 grid locations every 0.5° from 30.0° to 42.0°N along the California coast (Fig. 1) to create daily average IVT data for the 24-h period ending at 1200 UTC (e.g., average of the 1800, 0000, 0600, and 1200 UTC times) to compare with 24-h precipitation over the same daily period. For the purposes of relating IVT to landfalling ARs, this study defines an “AR day” as a day with daily average IVT magnitudes ≥ 250 kg m−1 s−1 at each of the 25 coastal grid points identified in Fig. 1, which is reliably similar to landfalling ARs using both the more restrictive magnitude and geometry-defined criteria in Rutz et al. (2014) (e.g., 94.3%, 97.3%, and 98.4% match for daily average IVT magnitudes ≥ 250, ≥300, and ≥350 at 38°N, respectively). Given the high degree of similarity and the methods employed, the results of this study are not materially altered by the definition or geometric characterization of an AR; however, any analyses in this study specifically linking ARs to precipitation could produce higher or different values as compared to the Rutz et al. (2014) study or studies using a different AR definition (e.g., Chen et al. 2018; Shields et al. 2018; Rutz et al. 2019). In this study, we also focus on IVT at coastal locations instead of collocated with inland watershed grid points in order to preserve maximum “ocean-inbound” IVT not contaminated by terrain or terrain effects (e.g., rain shadowing and/or barrier jets), to focus on the characteristics of ARs at landfall, and to align with existing AR-related forecast tools that focus on IVT at similar coastal locations (e.g., Cordeira et al. 2017; Cordeira and Ralph 2021). This study focuses on the climatology of 24-h precipitation and its relationship to daily average IVT magnitudes. Comparisons between event-total precipitation and landfalling AR characteristics (e.g., intensity, duration, geometry, etc.) would require a different methodology that is beyond the scope of the current analysis.
The relationship between IVT and precipitation is investigated via two “maximum” Pearson squared (r2) correlation values generated from several time series of coastal daily average IVT and daily watershed MAP. The first maximum r2 value for each watershed is the highest of the 25 r2 values for the correlations between daily watershed MAP and the daily average IVT magnitudes at each of the 25 coastal locations. In other words, this first maximum r2 value identifies the coastal location where IVT magnitudes explain the most variance in daily MAP for each watershed. This coastal location is also used to define AR frequency (as defined in this study) for a given watershed. The second maximum r2 value for a watershed is the highest of 9000 (i.e., 25 locations × 360°) r2 values for the correlations between daily watershed MAP and daily average projected IVT (IVTp) magnitudes at each location on the coast. In other words, the 9000 r2 values are a combination of the 25 coastal locations and projecting the IVT onto each possible direction between 0° and 359° (see example for North Fork Feather watershed in Fig. 2). This second maximum r2 value identifies the coastal location where IVT magnitude and direction explain the highest variance in daily MAP for each watershed. For example, Fig. 2 demonstrates that that watershed MAP in the North Fork Feather watershed is best correlated with an IVT vector from 236° at 36.5°N along the coast. The aforementioned two maximum r2 values are also calculated for the 850- and 925-hPa water vapor flux to further investigate the altitude of lower tropospheric water vapor flux that explains the highest variance in daily MAP for each watershed.
Correlation (r2; shaded) values between daily projected IVT and watershed MAP for the North Fork Feather watershed as a function of coastal location (latitude) and IVT direction. The maximum r2 value is marked by the black dot. The projected IVT is the geometric dot product of the coastal IVT vector with a unit vector that varies in direction from 0° to 360°. The maximum r2 value indicates that watershed MAP in the North Fork Feather watershed is best correlated with an IVT vector from 236° at 36.5°N along the coast.
Citation: Journal of Hydrometeorology 23, 9; 10.1175/JHM-D-21-0119.1
The aforementioned relationships between coastal IVT and daily MAP for each watershed defined by the correlation coefficient are mathematical constructs and precipitation in the watershed may not necessarily be caused by an AR existing at a given coastal grid. An example of this will be described later in the study. However, anecdotal evidence and the resulting correlation maxima of IVT magnitude and direction with watershed MAP do strongly suggest that water vapor transport along a landfalling AR at the coast is the likely mechanism producing orographic precipitation downstream within California watersheds. The annual correlations and analyses in this study are also briefly summarized for the warm (April–September) and cool (October–March) seasons. The aforementioned influence of landfalling ARs, IVT, and precipitation on water resources, water resources management, and FIRO (e.g., Dettinger et al. 2011; Jasperse et al. 2017, 2020; White et al. 2019; Henn et al. 2020) motivates summaries of the results at four focal watersheds in the North Fork Feather, Upper Yuba, Russian, and Santa Ana River watersheds and at the watersheds with the 10 largest lakes and reservoirs in California (Fig. 1).
3. Results
a. Watershed MAP climatology
The largest average annual MAP (>2000 mm) falls in watersheds along the coastal western slopes of the Klamath Mountains in northwest California along the Oregon border, with MAP > 1000 mm in watersheds along the western slopes of the North Coastal and northern Sierra Nevada mountain ranges during the 1982–2019 period (Fig. 3a). Examples of maximum average annual MAP within the study’s focal watersheds (see Fig. 1) include 1619 mm in the Upper Yuba and 1394 mm in the North Fork Feather watersheds, which are both located on the western slope of the northern Sierra Nevada (Figs. 4a,b), 1164 mm in the Russian River watershed along the northern coast of California (Fig. 4c), and 460 mm in the Santa Ana watershed along the southern coast of California (Fig. 4d).
California watershed MAP climatology of (a) average annual MAP (mm), (b) percent of average annual MAP attributed to extreme events (%), (c) the number of days to receive half the average annual MAP (days), and (d) percent of average annual MAP attributed to AR days (%). Every other coastal latitude location from Fig. 1 is shown in each panel for spatial reference. Note that the color scales in (b) and (d) are different.
Citation: Journal of Hydrometeorology 23, 9; 10.1175/JHM-D-21-0119.1
Summary of annual MAP (gray bars), annual MAP attributed to extremes (red bars; top 5% of wet days), and percentage of annual MAP attributed to extremes (black line) for (a) the North Fork Feather watershed, (b) the Upper Yuba watershed, (c) the Russian watershed, and (d) the Santa Ana watershed for WY 1982–2019. Note the left y axis in (d) is different from (a)–(c).
Citation: Journal of Hydrometeorology 23, 9; 10.1175/JHM-D-21-0119.1
The contribution of extreme (top 5%) precipitation events to average annual MAP is largest in watersheds that receive less average annual MAP and smallest in watersheds that receive more average annual MAP (cf. Figs. 3a,b). The largest contributions from extremes to average annual MAP is >40% and occur locally across watersheds in the Transverse Ranges (e.g., Santa Clara watershed), in the Colorado Desert (e.g., Salton Sea watershed), in the San Bernardino Mountains (e.g., Santa Ana watershed), near Lake Tahoe (e.g., Truckee and Upper Carson watersheds), and north of Mt. Whitney (e.g., Upper San Joaquin watershed; Fig. 3b). The smallest contributions of extremes to average annual MAP are <25% and also occur locally in watersheds near Los Angeles (e.g., Seal Beach watershed), the coastal Klamath Mountains (e.g., Chetco watershed), and in the northern Central Valley (e.g., Battle Creek watershed). The contributions of extremes to average annual MAP are >33% for approximately half of all California watersheds, prominently located in the Sierra Nevada, Coastal Mountain Ranges, Mojave Desert, Peninsular Ranges, Transverse Ranges, San Bernardino Mountains, and San Gabriel Mountains (Fig. 3b). The average contributions to annual MAP by extreme events at the study’s four focal watersheds are 36%, 34%, 33%, and 42% in the Upper Yuba, North Fork Feather, Russian, and Santa Ana watersheds, respectively (Fig. 3b). The fraction of annual MAP by extreme events is directly correlated to the annual MAP in these watersheds with r2 values of 0.88 in the Upper Yuba, 0.79 in the North Fork Feather, 0.82 in the Russian, and 0.92 in the Santa Ana watersheds (Fig. 4). In other words, variability in annual MAP is strongly influenced by variability in extreme events.
Watersheds with less average annual MAP and a higher fraction of extreme events also require, on average, the least number of rainy days per year to accumulate half of their precipitation (Figs. 3a,c). Minima of 4–7 days located in southeast California correspond to regional minima in the average annual MAP, whereas maxima of 21–24 days located along the Oregon border correspond to regional maxima > 1000–2000 mm of average annual MAP. Values at the four focal watersheds include 16, 16, 13, and 7 days in the Upper Yuba, North Fork Feather, Russian, and Santa Ana watersheds, respectively.
b. Relationships among ARs, coastal IVT, and watershed MAP
Contributions to average annual MAP across California watersheds by precipitation on AR days as defined in this study is highest in Northern California with >50% average annual MAP falling on coastally defined AR days (Fig. 3d). The maximum percentage of average annual MAP from precipitation on AR days is 71.5% at the Tomales–Drake Bays watershed northwest of San Francisco Bay that anchors a band of >60% values that extends east toward the Feather River watersheds in the northern Sierra Nevada (Fig. 3d). The minimum percentage of average annual MAP from precipitation on AR days is <25% and occurs in watersheds in the Mojave Desert and coastal Transverse Ranges (Fig. 3d). In the four focal watersheds, the average annual MAP from precipitation on AR days is 63%, 66%, 69%, and 43% in the Upper Yuba, North Fork Feather, Russian, and Santa Ana watersheds, respectively (Figs. 3d, 4, and 5). Precipitation on AR days in any given year is moderately correlated with annual MAP and annual MAP departures from normal in these watersheds, explaining 68%, 58%, 49%, and 37% of the variance between these variables in the Upper Yuba, North Fork Feather, Russian, and Santa Ana watersheds, respectively (Fig. 5). For reference, the average annual frequency of landfalling AR days as defined in this study for these watersheds is 28, 28, 28, and 8 (Fig. 4). Note that the average annual MAP from precipitation on ARs days is spatially well correlated with, but is slightly higher than, results presented in Fig. 8 of Rutz et al. (2014) likely based on our less restrictive AR definition.
Annual summary of MAP anomaly (green and brown bars) and frequency of AR days (black line) for (a) the North Fork Feather River watershed, (b) the Upper Yuba River watershed, (c) the Russian River watershed, and (d) the Santa Ana River watershed for WY 1982–2019. The AR frequency is derived from daily average IVT data in (a)–(c) at 37.5°N, 122°W and in (d) at 32.5°N, 117.0°W. Note that the right y axis in (d) is different from (a)–(c).
Citation: Journal of Hydrometeorology 23, 9; 10.1175/JHM-D-21-0119.1
The average statewide temporal correlation (r2) between the daily IVT magnitude and daily watershed MAP for all 140 California watersheds is 0.34 (Table 1) with a range of ∼0.50 in watersheds near the Klamath Mountains in Northern California to ∼0.10 in watersheds along the Colorado River in Southern and southeast California (Fig. 6a). Widespread minima in this correlation value < 0.30 exist across watersheds in Southern California, and maxima > 0.40 exist across watersheds in Northern California along the coast and western slope of the Northern Sierra Nevada from the Upper Tuolumne watershed to the North Fork Feather watershed (Fig. 6a). In other words, variability in daily IVT magnitudes along the northern coast are better correlated with daily watershed MAP in Northern California than IVT magnitude and precipitation in Southern California. These results are discussed further in section 4. When both the magnitude and direction of daily IVT (i.e., IVTp) is accounted for in correlations with daily watershed MAP, correlation (r2) values increase statewide by 0.11 on average to 0.45 (Fig. 6b and Table 1). Maximum correlation values increase in watersheds across Northern and coastal California to ∼0.50–0.65 and increase in watersheds across Southern California to ∼0.30 (Fig. 6b). At the four focal watersheds, the r2 values increased by 0.15, 0.16, 0.15, and 0.09 in the Upper Yuba, North Fork Feather, Russian, and Santa Ana watersheds, respectively (Table 1). In summary, the daily projected IVT magnitudes that account for both IVT magnitude and direction along the coast explains ∼30%–65% of the variance in daily watershed MAP across California watersheds, which is on average ∼32% higher than IVT magnitude alone (0.45 as compared to 0.34 in Table 1 for the California average).
California watershed maximum correlation values (r2) for (a) IVT magnitude and watershed MAP and (b) projected IVT and watershed MAP. The (c) coastal latitudes and (d) IVT directions corresponding to the maximum correlations in (b) for each watershed are also shown.
Citation: Journal of Hydrometeorology 23, 9; 10.1175/JHM-D-21-0119.1
Summary of correlation (r2) values between IVT, the projected IVT (IVTp), the 925-hPa water vapor flux (925F), the 850-hPa water vapor flux (850F), or the projected IVT during the cool season (IVTp-cool) with watershed MAP.
The coastal latitude grid point associated with the maximum correlation between daily projected IVT and daily watershed MAP is located coastally ∼2°–3° south of the latitude of the respective watersheds (i.e., suggesting a southwest IVT; Fig. 6c). The coastal IVT direction associated with the maximum correlation between daily projected IVT and daily watershed MAP ranges from 200° to 268° (approximately south-southwest to west) across the state (Fig. 6d). Watersheds with maximum correlation values derived from southwesterly (∼225°) IVT are predominately located along the coast (excluding locations near the San Francisco Bay; see next section), whereas those derived from west-southwesterly (∼245°) IVT are predominantly located across the Sierra Nevada (Fig. 6d). These west-southwest IVT directions are consistent with the predominant climatological southwesterly orientation of landfalling cool-season ARs (Neiman et al. 2008a).
The IVT is decomposed into isobaric water vapor flux in order to investigate the specific influence of lower-tropospheric water vapor flux on MAP and whether or not daily lower-tropospheric water vapor flux may explain a higher variance in daily MAP as compared to IVT. The daily average projected 850- and 925-hPa water vapor fluxes result in a statewide average increase in correlation (r2) values with daily MAP of +0.06 and +0.07, respectively, as compared to the projected IVT r2 values across California with a range from ∼0.0 to +0.20 (Fig. 7 and Table 1). The correlation values are highest (>0.60) for both 850- and 925-hPa water vapor fluxes in watersheds across the North Coastal Ranges and inland along the western slopes of the Sierra Nevada, which resembled prior shown correlation values (cf. Figs. 6a,b and 7a,b). The highest correlation values > 0.40 associated with the projected 925-hPa water vapor flux are both across Northern California and extend into Southern California (Fig. 7b). The lower-tropospheric projected water vapor fluxes explain a noticeably higher variance in daily MAP as compared to IVT predominantly across the Coastal Ranges and southern Sierra Nevada at 850 hPa and across most of Southern California at 925 hPa (Figs. 7c,d). For example, the correlation value calculated using the projected 925-hPa water vapor flux increased 66% from 0.36 to 0.60 in the Santa Ana River watershed as compared to calculations using the projected IVT (Table 1). It is unclear why the 925-hPa water vapor flux provides a better correlation with daily MAP than IVT; however, it is likely related to water vapor flux characteristics in precipitation processes or watershed topographic characteristics across Southern California. Additional work is necessary to better understand this relationship that is beyond the scope of the current study. In the other three focal watersheds located in Northern California, the 925- and 850-hPa water vapor flux correlation values were within 0.01 of each other and represented an increase of 0.03–0.07 over those values calculated using the projected IVT (Table 1). This analysis suggests that, for a majority of the state, the more easily calculated lower tropospheric water vapor flux may explain more variance in daily watershed MAP than the IVT vector (∼15%; 0.52 as compared to 0.45 in Table 1 for the California average), and that further analysis is required to understand the apparent benefit of using the 925-hPa water vapor flux to better understand watershed MAP in Southern California.
California watershed maximum correlation values (r2) for (a) projected 850-hPa water vapor flux and watershed MAP and (b) projected 925-hPa water vapor flux and watershed MAP, for comparison with the projected IVT and watershed MAP in Fig. 6b. (c),(d) The differences between the projected water vapor flux correlation values and the projected IVT correlation values are also shown.
Citation: Journal of Hydrometeorology 23, 9; 10.1175/JHM-D-21-0119.1
4. Concluding discussion
This study summarizes both California watershed MAP and its relationship to coastal IVT, a common characteristic used to describe water vapor transport within landfalling ARs. The highest average annual MAP > 2000 mm occurs in Northern California watersheds with maxima >1500 mm extending south along both the Coastal and Sierra Nevada mountain ranges (Fig. 3a). These maxima are associated with a higher annual frequency of precipitation events as illustrated by a larger number of days (>18 days) to receive half of their average annual MAP as compared to Southern California watersheds (Fig. 3c). The minima in average annual MAP < 300 mm occur across watersheds in Southern California and are associated with less frequent precipitation events as illustrated by a smaller number of days (<10 days) to receive half their average annual MAP. These climatological results summarized by watershed are consistent with previous studies that also show direct relationships between the frequency of precipitation events, the number of days to reach half the annual precipitation, and annual precipitation totals from observations at sites across California by Dettinger et al. (2011) and Lamjiri et al. (2018).
The average annual MAP across California is influenced by extreme events with >35% of annual MAP in Southern California watersheds falling on days when precipitation exceeds the top 5% of climatological wet days (Fig. 3a). The average annual MAP influenced by extreme events is less (∼25%) in watersheds across Northern California and is related to the higher number of days to reach half their annual precipitation as compared to Southern California. Outside of the southern deserts, the average annual MAP attributed to precipitation on “AR days” as defined in this study is ∼30%–50% with higher values across the northern coast of California and northern Sierra Nevada watersheds (Fig. 3d). The spatial variability in the amount of annual precipitation attributable to ARs by watershed is directly related to latitudinal variability in the frequency of landfalling ARs (Rutz et al. 2014) and is also similar to results for precipitation and snow water equivalent observations in California (Dettinger et al. 2011), the western United States (Rutz and Steenburgh 2012), and the Sierra Nevada (Guan et al. 2010). The percentage results in this study are spatially correlated with past studies attributing annual precipitation to ARs; however, they are slightly higher in percentage likely owing to a less restrictive AR definition. These results may also differ from previous studies based on dissimilarities in how ARs are defined (e.g., different geometric, intensity, and duration criteria) or dissimilar methodologies (e.g., daily precipitation versus event-total precipitation).
Examination of the relationship between daily coastal IVT magnitude and watershed daily MAP illustrated highest correlation (r2) values in coastal watersheds in Northern and central California (∼0.5) and the western slope of the Sierra Nevada (0.4–0.5) (Fig. 6a and Table 1). The lowest correlation values were located in watersheds over the southern Central Valley (0.2–0.3) and desert regions of Southern California (<0.2) where rain shadowing and/or precipitation processes unrelated to IVT and orographic precipitation may be more common (Fig. 6a). These decreasing correlation values from north to south are likely related to the aforementioned decreasing frequency of landfalling ARs and the associated decrease in mean duration of enhanced IVT magnitudes from north to south over California (Rutz et al. 2014). In other words, IVT plays less of a role in watershed precipitation across Southern California as compared to Northern California. Note that the lower correlation values in watersheds over northeast California (∼0.35) are likely associated with the rain shadow effect from the upstream Klamath and Coastal Mountain Ranges similar to results by Lamjiri et al. (2018).
When both the magnitude and direction of IVT is accounted for using the projected IVT, the correlation values with watershed MAP increases by 0.11 on average across watersheds in California, with increases >0.15 in individual watersheds across Northern and central California (Fig. 6b and Table 1). These results demonstrate that both the magnitude and direction of IVT are important factors in describing the relationships between landfalling ARs and watershed precipitation on average, as confirmed by individual case studies (e.g., Neiman et al. 2011) and local studies along the northern coast of California (e.g., Ralph et al. 2013; Hecht and Cordeira 2017). Correlations between water vapor flux and watershed MAP also demonstrate that the altitude of water vapor flux within the IVT distribution, specifically in the lower troposphere at 925 and 850 hPa, is an important factor in describing the relationship between landfalling ARs and watershed precipitation, especially within Southern California watersheds. While these results are intuitive, they simultaneously motivate and provide validation to improve applications using IVT diagnostics associated with landfalling ARs that only use IVT magnitude or that could be complemented by more-easily calculated isobaric water vapor flux diagnostics.
The coastal latitude corresponding to the maximum correlation between the projected IVT and watershed MAP (Fig. 6c) demonstrated a southward displacement relative to the watershed similar to previous findings by Rutz et al. (2015), indicative of the climatological southwesterly IVT associated with landfalling ARs in California that produces inland orographically enhanced precipitation in California. The IVT direction corresponding to the maximum correlations between the projected IVT and watershed MAP (Fig. 6d) ranged from ∼200° in watersheds in southeast California to ∼270° in watersheds in Northern California. Watersheds in the northern Sierra Nevada uniquely favored west-southwesterly (∼245°) IVT directions with coastal latitudes near ∼37°N (Figs. 6c,d) that suggested a preference for west-southwesterly water vapor transport along landfalling ARs through terrains gaps in the Coastal Ranges near the Petaluma and San Francisco Bay areas (Neiman et al. 2013b; Ralph et al. 2016) that may produce inland orographic precipitation and/or interact with the Sierra barrier jet (Lundquist et al. 2010; Smith et al. 2010; Kingsmill et al. 2013; Neiman et al. 2013b; White et al. 2015; Ralph et al. 2016). Watersheds in southeast California also uniquely favored south-southwesterly (∼210°) IVT directions with coastal latitudes much farther south near ∼30°–32°N (Figs. 6c,d) that suggested a preference for water vapor transport through terrain gaps in the Baja Peninsular Ranges (e.g., Rutz and Steenburgh 2012; Neiman et al. 2013a; Hughes et al. 2014; Rutz et al. 2015). In this case, however, the displacement of IVT far to the south of the region may not be representative of water vapor transport along an AR producing orographic precipitation within the desert regions of southeast California, but more so representative of a synoptic-scale environment containing a cutoff low pressure system producing inland precipitation while an AR coincidentally makes landfall in Baja California (e.g., Oakley and Redmond 2014; Oakley et al. 2018, 2020).
Given the climatological preference for variability in seasonal precipitation distributions across California related to cool-season ARs (Neiman et al. 2008a), vernal cutoff low pressure systems (Oakley and Redmond 2014; Oakley et al. 2018, 2020), and warm-season North American monsoon (NAM) surges and related convection (e.g., Adams and Comrie 1997; Ralph et al. 2014), we illustrate the correlations between the projected IVT and watershed MAP for the warm (April–September) and cool (October–March) seasons (Figs. 8a,b and Table 1) and the associated “best” IVT directions (Figs. 8c,d). These images demonstrate the primary utility of IVT magnitude and direction as a potential predictor of watershed MAP during the cool-season (widespread r2 values > 0.60) with limited use in the warm season (r2 values < 0.10) when ARs and enhanced IVT are less frequent (i.e., not a common precipitation mechanism). The associated warm-season IVT directions in southeast California are primarily south-southeast (∼160°) and suggest that NAM surges of south-southeasterly lower-tropospheric water vapor flux from the Bay of California (e.g., Adams and Comrie 1997) or southeasterly midtropospheric water vapor flux through the Chiricahua Gap in the Continental Divide (Ralph and Galarneau 2017) into southeast California may contribute to a very small portion of the variance in daily watershed MAP. Given the limited role of IVT in watershed MAP in southeast California, and suggested limited role of orographic processes, it is likely that other factors related to synoptic and convective processes such as cutoff low pressure systems (Oakley and Redmond 2014; Abatzoglou 2016), processes associated with the NAM (Adams and Comrie 1997), or precipitation efficiency (e.g., the Eidhammer et al. 2018) may play a larger role in modulating precipitation in these regions.
California watershed maximum correlation values (r2) for projected IVT and watershed MAP in (a) the April–September warm season and (b) the October–March cool season, for comparison with the projected IVT and watershed MAP in Fig. 6b. (c),(d) The IVT directions corresponding to the maximum correlations in (a) and (b) for each watershed are also shown. Note that the colors for IVT directions in (c) and (d) differ from those colors for IVT directions in Fig. 6d.
Citation: Journal of Hydrometeorology 23, 9; 10.1175/JHM-D-21-0119.1
Altogether, the results of this study affirm that IVT magnitude and direction and the vertical distribution of water vapor flux are important factors in describing the relationships between landfalling ARs and watershed precipitation in California with annual r2 values > 0.60 and cool-season r2 values near 0.70 in the focal watersheds in Northern California in Table 1 (i.e., Upper Yuba, North Fork Feather, and Russian). This study expands upon a study on the relationship between observations of the storm-total bulk upslope water flux and storm-total precipitation in the Russian River watershed (Ralph et al. 2013; their r2 = 0.74) and adopts a watershed framework to visualize the similar daily relationship between IVT and precipitation across California. The watershed framework lends itself to summarizing the results of this study for those watersheds which contain California’s largest lakes and reservoirs in order to provide potential situational awareness in support of FIRO (Table 2). For example, Lake Oroville which contains two tributary HUC-8 watersheds (Middle Fork Feather and North Fork Feather) receives an average of ∼65% of its annual MAP on days with daily average coastal IVT magnitudes ≥ 250 kg m−1 s−1 (i.e., AR days) with annual maximum correlation (r2) values > 0.60 associated with enhanced coastal IVT at 36.5°N with a direction of ∼238° (Table 2). These annual values increase to maximum correlation (r2) values of 0.68–0.69 with similar coastal IVT characteristics during the cool season (Table 2). From a water resource management perspective, this information may provide AR-related situational awareness when using forecast tools in the decision-making process of releasing or storing water in advance of a landfalling AR.
Summary of results for the HUC-8 watersheds containing 12 California lakes/reservoirs, listed from north to south.
While the present study focuses on the correlation of daily IVT magnitude and direction with daily watershed precipitation in a bulk sense, individual landfalling ARs and their associated water vapor flux characteristics may deviate from these statistical relationships. For example, a given landfalling AR may simultaneously produce synoptic-scale precipitation that may seed orographic precipitation, thereby producing higher precipitation than otherwise might be expected from orographic precipitation alone (Hecht and Cordeira 2017). Similarly, landfalling ARs with similar IVT magnitudes may be comprised of different thermodynamic characteristics (e.g., equivalent potential temperature, saturation, or wind profiles, or even aerosol composition) that make precipitation processes more or less efficient within a given watershed (e.g., Gyakum and Roebber 2001; Eidhammer et al. 2018; Voss et al. 2021). The PRISM dataset used in this study also has a known dry bias for cases of postfrontal non-AR related precipitation that could impact the results presented in this study (i.e., the fraction of annual precipitation associated with ARs may be too high if non-AR-related QPE is not accounted for in a watershed’s annual MAP). In addition to using the results of this study to motivate and inform the future development of AR-related forecast tools, future work is therefore aimed at exploring those factors which influence watershed MAP and streamflow in addition to IVT and water vapor flux mentioned above, and at evaluating how the results of this study change using different observational or reanalysis products and methodologies pertaining to AR characteristics (e.g., IVT intensity, structure, their environment) and precipitation.
Acknowledgments.
Support for this project was primarily provided by awards supporting FIRO by the U.S. Army Corps of Engineers (W912HZ-15-2-0019, W912HZ-19-2-0023) and the State of California, Department of Water Resources (4600013361) as part of broader projects led by the Center for Western Weather and Water Extremes (CW3E) at the Scripps Institution of Oceanography, University of California, San Diego. We are grateful for two anonymous reviewers for providing comments that improved the quality of this manuscript.
Data availability statement.
Data analyzed in this study were a reanalysis and derivation of existing data, which are openly available at locations cited in section 2.
REFERENCES
Abatzoglou, J. T., 2016: Contribution of cutoff lows to precipitation across the United States. J. Appl. Meteor. Climatol., 55, 893–899, https://doi.org/10.1175/JAMC-D-15-0255.1.
Adams, D. K., and A. C. Comrie, 1997: The North American monsoon. Bull. Amer. Meteor. Soc., 78, 2197–2214, https://doi.org/10.1175/1520-0477(1997)078<2197:TNAM>2.0.CO;2.
American Meteorological Society, 2019: Atmospheric river. Glossary of Meteorology, https://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.
Bartlett, S. M., and J. M. Cordeira, 2021: A climatological study of National Weather Service watches, warnings, and advisories and landfalling atmospheric rivers in the western United States 2006–18. Wea. Forecasting, 36, 1097–1112, https://doi.org/10.1175/WAF-D-20-0212.1.
Bergeron, T., 1965: On the low-level redistribution of atmospheric water caused by orography. Proc. Int. Conf. on Cloud Physics, Tokyo, Japan, IAMAP/WMO, 96–100.
Bishop, D. A., and C. M. Beier, 2013: Assessing uncertainty in high-resolution spatial climate data across the US Northeast. PLOS ONE, 8, e70260, https://doi.org/10.1371/journal.pone.0070260.
Browning, K. A., 1980: Structure, mechanism, and prediction of orographically enhanced rain in Britain. Orographic Effects in Planetary Flows, R. Hide and P. W. White, Eds., WMO GARP Publications Series 23, 85–114.
Browning, K. A., F. F. Hill, and C. W. Pardoe, 1974: Structure and mechanism of precipitation and the effect of orography in a wintertime warm sector. Quart. J. Roy. Meteor. Soc., 100, 309–330, https://doi.org/10.1002/qj.49710042505.
Cannon, F., and Coauthors, 2020: Observations and predictability of a high-impact narrow cold-frontal rainband over Southern California of 2 February 2019. Wea. Forecasting, 35, 2083–2097, https://doi.org/10.1175/WAF-D-20-0012.1.
Chen, X., R. L. Leung, Y. Gao, Y. Liu, M. Wigmosta, and M. Richmond, 2018: Predictability of extreme precipitation in western U.S. watersheds based on atmospheric river occurrence, intensity, and duration. Geophys. Res. Lett., 45, 11 693–11 701, https://doi.org/10.1029/2018GL079831.
Cordeira, J. M., and F. M. Ralph, 2021: A summary of GFS ensemble integrated water vapor transport forecasts and skill along the U.S. West Coast during water years 2017–2020. Wea. Forecasting, 36, 361–377, https://doi.org/10.1175/WAF-D-20-0121.1.
Cordeira, J. M., F. M. Ralph, A. Martin, N. Gaggini, J. R. Spackman, P. J. Neiman, J. Rutz, and R. Pierce, 2017: Forecasting atmospheric rivers during CalWater 2015. Bull. Amer. Meteor. Soc., 98, 1827–1837, https://doi.org/10.1175/BAMS-D-15-00245.1.
Cordeira, J. M., M. M. Neureuter, and L. Kelleher, 2018: Atmospheric rivers and National Weather Service watches, warnings, and advisories issued over California 2007–2016. J. Oper. Meteor., 6, 87–94, https://doi.org/10.15191/nwajom.2018.0608.
Cordeira, J. M., J. Stock, M. D. Dettinger, A. M. Young, J. F. Kalansky, and F. M. Ralph, 2019: A 142-year climatology of Northern California landslides and atmospheric rivers. Bull. Amer. Meteor. Soc., 100, 1499–1509, https://doi.org/10.1175/BAMS-D-18-0158.1.
Corringham, T. W., F. M. Ralph, A. Gershunov, D. R. Cayan, and C. A. Talbot, 2019: Atmospheric rivers drive flood damages in the western United States. Sci. Adv., 5, eaax4631, https://doi.org/10.1126/sciadv.aax4631.
Creamean, J. M., and Coauthors, 2013: Dust and biological aerosols from the Sahara and Asia influence precipitation in the western U.S. Science, 339, 1572–1578, https://doi.org/10.1126/science.1227279.
Daley, R., 1991: Atmospheric Data Analysis. Cambridge University Press, 457 pp.
Daly, C., 2006: Guidelines for assessing the suitability of spatial climate data sets. Int. J. Climatol., 26, 707–721, https://doi.org/10.1002/joc.1322.
Daly, C., R. P. Neilson, and D. L. Phillips, 1994: A statistical-topographic model for mapping climatological precipitation over mountainous terrain. J. Appl. Meteor., 33, 140–158, https://doi.org/10.1175/1520-0450(1994)033<0140:ASTMFM>2.0.CO;2.
Daly, C., W. P. Gibson, G. H. Taylor, G. L. Johnson, and P. Pasteris, 2002: A knowledge-based approach to the statistical mapping of climate. Climate Res., 22, 99–113, https://doi.org/10.3354/cr022099.
Daly, C., M. Halbleib, J. I. Smith, W. P. Gibson, M. K. Dogget, G. H. Taylor, J. Curtis, and P. P. Pasteris, 2008: Physiographically sensitive mapping of climatological temperature and precipitation across the conterminous United States. Int. J. Climatol., 28, 2031–2064, https://doi.org/10.1002/joc.1688.
Delaney, C. J., and Coauthors, 2020: Forecast informed reservoir operations using ensemble streamflow predictions for a multipurpose reservoir in Northern California. Water Resour. Res., 56, e2019WR026604, https://doi.org/10.1029/2019WR026604.
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., F. M. Ralph, T. Das, P. J. Neiman, and D. R. Cayan, 2011: Atmospheric rivers, floods, and the water resources of California. Water, 3, 445–478, https://doi.org/10.3390/w3020445.
Di Luzio, M., G. L. Johnson, C. Daly, J. K. Eischeid, and J. G. Arnold, 2008: Constructing retrospective gridded daily precipitation and temperature datasets for the conterminous United States. J. Appl. Meteor. Climatol., 47, 475–497, https://doi.org/10.1175/2007JAMC1356.1.
Eidhammer, T., V. Grubišić, R. Rasmussen, and K. Ikdea, 2018: Winter precipitation efficiency of mountain ranges in the Colorado Rockies under climate change. J. Geophys. Res. Atmos., 123, 2573–2590, https://doi.org/10.1002/2017JD027995.
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, N. P. Molotoch, E. J. Fetzer, and P. J. Neiman, 2012: Does the Madden–Julian oscillation influence wintertime atmospheric rivers and snowpack in the Sierra Nevada? Mon. Wea. Rev., 140, 325–342, https://doi.org/10.1175/MWR-D-11-00087.1.
Gyakum, J. R., and P. J. Roebber, 2001: The 1998 ice storm—Analysis of a planetary-scale event. Mon. Wea. Rev., 129, 2983–2997, https://doi.org/10.1175/1520-0493(2001)129<2983:TISAOA>2.0.CO;2.
Hecht, C. W., and J. M. Cordeira, 2017: Characterizing the influence of atmospheric river orientation and intensity on precipitation distributions over north coastal California. Geophys. Res. Lett., 44, 9048–9058, https://doi.org/10.1002/2017GL074179.
Henn, B., K. N. Musselman, L. Lestak, F. M. Ralph, and N. P. Molotch, 2020: Extreme runoff generation from atmospheric river driven snowmelt during the 2017 Oroville Dam spillways incident. Geophys. Res. Lett., 47, e2020GL088189, https://doi.org/10.1029/2020GL088189.
Hill, F. F., 1983: The use of average annual rainfall to derive estimates of orographic enhancement of frontal rain over England and Wales for different wind directions. J. Climatol., 3, 113–129, https://doi.org/10.1002/joc.3370030202.
Hill, F. F., and K. A. Browning, 1979: Persistence and orographic modulation of mesoscale precipitation areas in a potentially unstable warm sector. Quart. J. Roy. Meteor. Soc., 105, 57–70, https://doi.org/10.1002/qj.49710544305.
Hu, H., F. Dominguez, Z. Wang, D. A. Lavers, G. Zhang, and F. M. Ralph, 2017: Linking atmospheric river hydrological impacts on the U.S. West Coast to Rossby wave breaking. J. Climate, 30, 3381–3399, https://doi.org/10.1175/JCLI-D-16-0386.1.
Hughes, M., K. M. Mahoney, P. J. Neiman, B. J. Moore, M. Alexander, and F. M. Ralph, 2014: The landfall and inland penetration of a flood-producing atmospheric river in Arizona. Part II: Sensitivity of modeled precipitation to terrain height and atmospheric river orientation. J. Hydrometeor., 15, 1954–1974, https://doi.org/10.1175/JHM-D-13-0176.1.
Jasperse, J., and Coauthors, 2017: Preliminary viability assessment of Lake Mendocino forecast informed reservoir operations. Center for Western Weather and Water Extremes, 75 pp., https://pubs.er.usgs.gov/publication/70192184.
Jasperse, J., and Coauthors, 2020: Lake Mendocino forecast informed reservoir operations: Final viability assessment. University of California, San Diego, 131 pp., https://escholarship.org/uc/item/3b63q04n.
Kaplan, M. L., C. S. Adaniya, P. J. Marzette, K. C. King, S. J. Underwood, and J. M. Lewis, 2009: The role of upstream midtropospheric circulations in the Sierra Nevada enabling leeside (spillover) precipitation. Part II: A secondary atmospheric river accompanying a midlevel jet. J. Hydrometeor., 10, 1327–1354, https://doi.org/10.1175/2009JHM1106.1.
Kaplan, M. L., R. K. Vellore, P. J. Marzette, and J. M. Lewis, 2012: The role of windward-side diabatic heating in Sierra Nevada spillover precipitation. J. Hydrometeor., 13, 1172–1194, https://doi.org/10.1175/JHM-D-11-06.1.
Kingsmill, D. E., P. J. Neiman, B. J. Moore, M. Hughes, S. E. Yuter, and F. M. Ralph, 2013: Kinematic and thermodynamic structures of Sierra barrier jets and overrunning atmospheric rivers during a landfalling winter storm in Northern California. Mon. Wea. Rev., 141, 2015–2036, https://doi.org/10.1175/MWR-D-12-00277.1.
Lamjiri, M. A., M. D. Dettinger, F. M. Ralph, N. S. Oakley, and J. J. Rutz, 2018: Hourly analyses of the large storms and atmospheric rivers that provide most of California’s precipitation in only 10 to 100 hours per year. San Francisco Estuary Watershed Sci., 16, 1, https://doi.org/10.15447/sfews.2018v16iss4art1.
Lundquist, J. D., M. Hughes, B. Henn, E. D. Gutmann, B. Livneh, J. Dozier, and P. Neiman, 2015: High-elevation precipitation patterns: Using snow measurements to assess daily gridded datasets across the Sierra Nevada, California. J. Hydrometeor., 16, 1773–1792, https://doi.org/10.1175/JHM-D-15-0019.1.
Lundquist, J. D., J. R. Minder, P. J. Neiman, and E. Sukovich, 2010: Relationships between barrier jet heights, orographic precipitation gradients, and streamflow in the northern Sierra Nevada. J. Hydrometeor., 11, 1141–1156, https://doi.org/10.1175/2010JHM1264.1.
Neiman, P. J., F. M. Ralph, A. B. White, D. E. Kingsmill, and P. O. G. Persson, 2002: The statistical relationship between upslope flow and rainfall in California’s coastal mountains: Observations during CALJET. Mon. Wea. Rev., 130, 1468–1492, https://doi.org/10.1175/1520-0493(2002)130<1468:TSRBUF>2.0.CO;2.
Neiman, P. J., F. M. Ralph, G. A. Wick, J. D. Lundquist, and M. D. Dettinger, 2008a: 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., F. M. Ralph, G. A. Wick, Y.-H. Kuo, T.-K. Wee, Z. Ma, G. H. Taylor, and M. D. Dettinger, 2008b: Diagnosis of an intense atmospheric river impacting the Pacific Northwest: Storm summary and offshore vertical structure observed with COSMIC satellite retrievals. Mon. Wea. Rev., 136, 4398–4420, https://doi.org/10.1175/2008MWR2550.1.
Neiman, P. J., A. B. White, F. M. Ralph, D. J. Gottas, and S. I. Gutman, 2009: A water vapor flux tool for precipitation forecasting. Water Manage., 162, 83–94, https://doi.org/10.1680/wama.2009.162.2.83.
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.
Neiman, P. J., F. M. Ralph, B. J. Moore, M. Hughes, K. M. Mahoney, J. M. Cordeira, and M. D. Dettinger, 2013a: The landfall and inland penetration of a flood-producing atmospheric river in Arizona. Part I: Observed synoptic-scale, orographic, and hydrometeorological characteristics. J. Hydrometeor., 14, 460–484, https://doi.org/10.1175/JHM-D-12-0101.1.
Neiman, P. J., M. Hughes, B. J. Moore, F. M. Ralph, and E. M. Sukovich, 2013b: Sierra barrier jets, atmospheric rivers, and precipitation characteristics in Northern California: A composite perspective based on a network of wind profilers. Mon. Wea. Rev., 141, 4211–4233, https://doi.org/10.1175/MWR-D-13-00112.1.
Oakley, N. S., and K. T. Redmond, 2014: A climatology of 500-hPa closed lows in the northeastern Pacific Ocean, 1948–2011. J. Appl. Meteor. Climatol., 53, 1578–1592, https://doi.org/10.1175/JAMC-D-13-0223.1.
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.
Oakley, N. S., J. T. Lancaster, B. J. Hatchett, J. Stock, F. M. Ralph, S. Roj, and S. Lukashov, 2018: A 22-year climatology of cool season hourly precipitation thresholds conducive to shallow landslides in California. Earth Interact., 22, https://doi.org/10.1175/EI-D-17-0029.1.
Oakley, N. S., M. D. Orla-Barile, B. Hatchett, P. Burgess, and F. M. Ralph, 2020: Characteristics and impacts of the April 3-11-2020 cutoff low storm in California. Center for Western Weather and Water Extremes, https://cw3e.ucsd.edu/characteristics-and-impacts-of-the-april-4-11-2020-cutoff-low-storm-in-california/.
Payne, A. E., and G. Magnusdottir, 2014: Dynamics of landfalling atmospheric rivers over the North Pacific in 30 years of MERRA reanalysis. J. Climate, 27, 7133–7150, https://doi.org/10.1175/JCLI-D-14-00034.1.
PRISM Climate Group, 2019: Descriptions of PRISM spatial climate datasets for the conterminous United States. Oregon State University, 29 pp., https://prism.oregonstate.edu/documents/PRISM_datasets.pdf.
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., and T. J. Galarneau, 2017: The Chiricahua Gap and the role of easterly water vapor transport in southeastern Arizona monsoon precipitation. J. Hydrometeor., 18, 2511–2520, https://doi.org/10.1175/JHM-D-17-0031.1.
Ralph, F. M., P. J. Neiman, D. E. Kingsmill, P. O. G. Persson, A. B. White, E. T. Strem, E. D. Andrews, and R. C. Antweiler, 2003: The impact of a prominent rain shadow on flooding in California’s Santa Cruz mountains: A CALJET case study and sensitivity to the ENSO cycle. J. Hydrometeor., 4, 1243–1264, https://doi.org/10.1175/1525-7541(2003)004<1243:TIOAPR>2.0.CO;2.
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, and R. Rotunno, 2005: Dropsonde observations in low-level jets over the northeastern Pacific Ocean from CALJET-1998 and PACJET-2001: Mean vertical-profile and atmospheric-river characteristics. Mon. Wea. Rev., 133, 889–910, https://doi.org/10.1175/MWR2896.1.
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: The role of atmospheric rivers. Geophys. Res. Lett., 33, L13801, https://doi.org/10.1029/2006GL026689.
Ralph, F. M., P. J. Neiman, G. N. Kiladis, and K. Weickmann, 2011: A multiscale 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., J. M. Cordeira, P. J. Neiman, and M. Hughes, 2016: Landfalling atmospheric rivers, the Sierra barrier jet, and extreme precipitation in Northern California’s upper Sacramento River watershed. J. Hydrometeor., 17, 1905–1914, https://doi.org/10.1175/JHM-D-15-0167.1.
Ralph, F. M., and Coauthors, 2017: Atmospheric rivers emerge as a global science and applications focus. Bull. Amer. Meteor. Soc., 98, 1969–1973, https://doi.org/10.1175/BAMS-D-16-0262.1.
Rutz, J. J., and W. J. Steenburgh, 2012: Quantifying the role of atmospheric rivers in the interior western United States. Atmos. Sci. Lett., 13, 257–261, https://doi.org/10.1002/asl.392.
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.
Rutz, J. J., and Coauthors, 2019: The Atmospheric River Tracking Method Intercomparison Project (ARTMIP): Quantifying uncertainties in atmospheric river climatology. J. Geophys. Res. Atmos., 124, 13 777–13 802, https://doi.org/10.1029/2019JD030936.
Saha, S., and Coauthors, 2006: The NCEP Climate Forecast System. J. Climate, 19, 3483–3517, https://doi.org/10.1175/JCLI3812.1.
Saha, S., and Coauthors, 2014: The NCEP Climate Forecast System version 2. J. Climate, 27, 2185–2208, https://doi.org/10.1175/JCLI-D-12-00823.1.
Sharples, J. J., M. F. Hutchinson, and D. R. Jellet, 2005: On the horizontal scale of elevation dependence of Australian monthly precipitation. J. Appl. Meteor. Climatol., 44, 1850–1865, https://doi.org/10.1175/JAM2289.1.
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.
Smith, B. L., S. E. Yuter, P. J. Neiman, and D. E. Kingsmill, 2010: Water vapor fluxes and orographic precipitation over Northern California associated with a land-falling atmospheric river. Mon. Wea. Rev., 138, 74–100, https://doi.org/10.1175/2009MWR2939.1.
Storebø, P. B., 1976: Small scale topographical influences on precipitation. Tellus, 28, 45–58, https://doi.org/10.3402/tellusa.v28i1.10193.
Voss, K. K., A. T. Evan, and F. M. Ralph, 2021: Evaluating the meteorological conditions associated with dusty atmospheric rivers. J. Geophys. Res. Atmos., 126, e2021JD035403, https://doi.org/10.1029/2021JD035403.
White, A. B., P. J. Neiman, J. M. Creamean, T. Coleman, F. M. Ralph, and K. A. Prather, 2015: The impacts of California’s San Francisco Bay Area Gap on precipitation observed in the Sierra Nevada during HMT and CalWater. J. Hydrometeor., 16, 1048–1069, https://doi.org/10.1175/JHM-D-14-0160.1.
White, A. B., B. J. Moore, D. J. Gottas, and P. J. Neiman, 2019: Winter storm conditions leading to excessive runoff above California’s Oroville Dam during January and February 2017. Bull. Amer. Meteor. Soc., 100, 55–70, https://doi.org/10.1175/BAMS-D-18-0091.1.
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, 2018: The relationship between extratropical cyclone strength and atmospheric river intensity and position. Geophys. Res. Lett., 46, 1814–1823, https://doi.org/10.1029/2018GL079071.