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Uncertainties in gridded and regional climate estimates of precipitation are large at high elevations, where observations are sparse and spatial variability is substantial. We explore these uncertainties for water year 2008 across California's Sierra Nevada in 10 datasets: 6 regional climate downscalings generated using the weather research and forecasting (WRF) model at convection-permitting resolution with differing lateral boundary conditions and microphysical parameterizations, and four gauge-based, interpolation-gridded precipitation datasets. Precipitation from these 10 datasets is evaluated against 95 snow pillows and a precipitation dataset inferred from stream gauges using a Bayesian inference method. During water year 2008, the gridded datasets tend to underestimate frozen precipitation on the windward slope of the Sierra Nevada, particularly in the vicinity of Yosemite National Park. The WRF simulations with single-moment microphysics tend to overestimate precipitation throughout much of the region, whereas the WRF simulations with double-moment microphysics tend to better agree with both the snow pillows and inferred precipitation estimates, although they somewhat overestimate the windward/leeside precipitation contrast in the northern Sierra Nevada. WRF simulations, in particular those with single-moment microphysics, better distinguish spatial patterns of wet-versus-dry pillows and watersheds over the water year than the gridded estimates. Our results suggest treating gauge-based datasets as 'truth' may give a misleading representation of model accuracy, since these gauge-based datasets often have issues of their own. [ABSTRACT FROM AUTHOR]

The Bay Area of California and surrounding region receives much of its annual precipitation during the October–March wet season, when atmospheric river events bring periods of heavy rain that challenge water managers and may exceed the capacity of storm sewer systems. The complex terrain of this region further complicates the situation, with terrain interactions that are not currently captured in most operational forecast models and inadequate precipitation measurements to capture the large variability throughout the area. To improve monitoring and prediction of these events at spatial and temporal resolutions of interest to area water managers, the Bay Area Advanced Quantitative Precipitation Information project was developed. To quantify improvements in forecast precipitation, model validation studies require a reference dataset to compare against. In this paper we examine 10 gridded, high-resolution (≤10 km, hourly) precipitation estimates to assess the uncertainty of high-resolution quantitative precipitation estimates (QPE) in areas of complex terrain. The products were linearly interpolated to 3-km grid spacing, which is the resolution of the operational forecast model to be validated. Substantial differences exist between the various products at accumulation periods ranging from hourly to annual, with standard deviations among the products exceeding 100% of the mean. While the products seem to agree fairly well on the timing of precipitation, intensity estimates differ, sometimes by an order of magnitude. The results highlight both the need for additional observations and the need to account for uncertainty in the reference dataset when validating forecasts in this area. [ABSTRACT FROM AUTHOR]

The Russian River in northern California is an important hydrological resource that typically depends on a few significant precipitation events per year, often associated with atmospheric rivers (ARs), to maintain its annual water supply. Because of the highly variable nature of annual precipitation in the region, accurate quantitative precipitation estimates (QPEs) are necessary to drive hydrologic models and inform water management decisions. The basin's location and complex terrain present a unique challenge to QPEs, with sparse in situ observations and mountains that inhibit remote sensing by ground radars. Gridded multisensor QPE datasets can fill in the gaps but are susceptible to both the errors and uncertainties from the ingested datasets and uncertainties due to interpolation methods. In this study a dense network of independently operated rain gauges is used to evaluate gridded QPE from the Multi-Radar Multi-Sensor (MRMS) during 44 precipitation events occurring during the 2015/16 and 2016/17 wet seasons (October–March). The MRMS QPE products matched the gauge estimates of precipitation reasonably well in approximately half the cases but failed to capture the spatial distribution and intensity of the rainfall in the remaining cases. ERA-Interim reanalysis data suggest that the differences in performance are related to synoptic-scale patterns and AR landfall location. These synoptic-scale differences produce different rainfall distributions and influence basin-scale winds, potentially creating regions of small-scale precipitation enhancement or suppression. Data from four profiling radars indicated that a larger fraction of the precipitation in poorly captured events occurred as shallow stratiform rain unobserved by radar. [ABSTRACT FROM AUTHOR]

A series of precipitation events impacted the Pacific Northwest during the first two weeks of November 2006. This sequence was punctuated by a particularly potent inland-penetrating atmospheric river (AR) that produced record-breaking precipitation across the region during 5-7 November. The precipitation caused destructive flooding as far inland as Montana's Glacier National Park, 800 km from the Pacific Ocean. This study investigates the inland penetration of moisture during the event using a 4-1.33-km grid spacing configuration of the Weather Research and Forecasting (WRF) modeling system. A high-resolution simulation allowed an analysis of interactions between the strong AR and terrain features such as the Cascade Mountains and the Columbia River Gorge (CR Gorge). Moisture transport in the vicinity of the Cascades is assessed using various metrics. The most efficient pathway for moisture penetration was through the gap (i.e., CR Gap) between Mt. Adams and Mt. Hood, which includes the CR Gorge. While the CR Gap is a path of least resistance through the Cascades, most of the total moisture transport that survived transit past the Cascades overtopped the mountain barrier itself. This is due to the disparity between the length of the ridge (~800 km) and relatively narrow width of the CR Gap (~93 km). Moisture transport reductions were larger across the Washington Cascades and the southern-central Oregon Cascades than through the CR Gap. During the simulation, drying ratios through the CR Gap (9.3%) were notably less than over adjacent terrain (19.6%-30.6%). Drying ratios decreased as moisture transport intensity increased. [ABSTRACT FROM AUTHOR]

The near-surface climate, including the atmosphere, ocean, sea ice, and land state and fluxes, in the initial version of the Regional Arctic System Model (RASM) are presented. The sensitivity of the RASM near-surface climate to changes in atmosphere, ocean, and sea ice parameters and physics is evaluated in four simulations. The near-surface atmospheric circulation is well simulated in all four RASM simulations but biases in surface temperature are caused by biases in downward surface radiative fluxes. Errors in radiative fluxes are due to biases in simulated clouds with different versions of RASM simulating either too much or too little cloud radiative impact over open ocean regions and all versions simulating too little cloud radiative impact over land areas. Cold surface temperature biases in the central Arctic in winter are likely due to too few or too radiatively thin clouds. The precipitation simulated by RASM is sensitive to changes in evaporation that were linked to sea surface temperature biases. Future work will explore changes in model microphysics aimed at minimizing the cloud and radiation biases identified in this work. [ABSTRACT FROM AUTHOR]

The Regional Arctic System Model (RASM) is a fully coupled, regional Earth system model applied over the pan-Arctic domain. This paper discusses the implementation of the Variable Infiltration Capacity land surface model (VIC) in RASM and evaluates the ability of RASM, version 1.0, to capture key features of the land surface climate and hydrologic cycle for the period 1979-2014 in comparison with uncoupled VIC simulations, reanalysis datasets, satellite measurements, and in situ observations. RASM reproduces the dominant features of the land surface climatology in the Arctic, such as the amount and regional distribution of precipitation, the partitioning of precipitation between runoff and evapotranspiration, the effects of snow on the water and energy balance, and the differences in turbulent fluxes between the tundra and taiga biomes. Surface air temperature biases in RASM, compared to reanalysis datasets ERA-Interim and MERRA, are generally less than 2°C; however, in the cold seasons there are local biases that exceed 6°C. Compared to satellite observations, RASM captures the annual cycle of snow-covered area well, although melt progresses about two weeks faster than observations in the late spring at high latitudes. With respect to derived fluxes, such as latent heat or runoff, RASM is shown to have similar performance statistics as ERA-Interim while differing substantially from MERRA, which consistently overestimates the evaporative flux across the Arctic region. [ABSTRACT FROM AUTHOR]

The upper Sacramento River watershed is vital to California's water supply and is susceptible to major floods. Orographic precipitation in this complex terrain involves both atmospheric rivers (ARs) and the Sierra barrier jet (SBJ). The south-southeasterly SBJ induces orographic precipitation along south-facing slopes in the Mt. Shasta-Trinity Alps, whereas landfalling ARs ascend up and over the statically stable SBJ and induce orographic precipitation along west-facing slopes in the northern Sierra Nevada. This paper explores the occurrence of extreme daily precipitation (EDP) in this region in association with landfalling ARs and the SBJ. The 50 wettest days (i.e., days with EDP) for water years (WYs) 2002-11 based on the average of daily precipitation from eight rain gauges known as the Northern Sierra 8-Station Index (NS8I) are compared to dates from an SSM/I satellite-based landfalling AR-detection method and dates with SBJ events identified from nearby wind profiler data. These 50 days with EDP accounted for 20% of all precipitation during the 10-WY period, or 5 days with EDP per year on average account for one-fifth of WY precipitation. In summary, 46 of 50 (92%) days with EDP are associated with landfalling ARs on either the day before or the day of precipitation, whereas 45 of 50 (90%) days with EDP are associated with SBJ conditions on the day of EDP. Forty-one of 50 (82%) days with EDP are associated with both a landfalling AR and an SBJ. The top 10 days with EDP were all associated with both a landfalling AR and an SBJ. [ABSTRACT FROM AUTHOR]

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

Gridded spatiotemporal maps of precipitation are essential for hydrometeorological and ecological analyses. In the United States, most of these datasets are developed using the Cooperative Observer (COOP) network of ground-based precipitation measurements, interpolation, and the Parameter-Elevation Regressions on Independent Slopes Model (PRISM) to map these measurements to places where data are not available. Here, we evaluate two daily datasets gridded at ° resolution against independent daily observations from over 100 snow pillows in California's Sierra Nevada from 1990 to 2010. Over the entire period, the gridded datasets performed reasonably well, with median total water-year errors generally falling within ±10%. However, errors in individual storm events sometimes exceeded 50% for the median difference across all stations, and in many cases, the same underpredicted storms appear in both datasets. Synoptic analysis reveals that these underpredicted storms coincide with 700-hPa winds from the west or northwest, which are associated with post-cold-frontal flow and disproportionately small precipitation rates in low-elevation valley locations, where the COOP stations are primarily located. This atmospheric circulation leads to a stronger than normal valley-to-mountain precipitation gradient and underestimation of actual mountain precipitation. Because of the small average number of storms (<10) reaching California each year, these individual storm misses can lead to large biases (~20%) in total water-year precipitation and thereby significantly affect estimates of statewide water resources. [ABSTRACT FROM AUTHOR]

Two methods were used to identify the paths of moisture transport that reach the U.S. Intermountain West (IMW) during heavy precipitation events in winter. In the first, the top 150 precipitation events at stations located within six regions in the IMW were identified, and then back trajectories were initiated at 6-h intervals on those days at the four Climate Forecast System Reanalysis grid points nearest the stations. The second method identified the leading patterns of integrated water vapor transport (IVT) using the three leading empirical orthogonal functions of IVT over land that were first normalized by the local standard deviation. The top 1% of the associated 6-hourly time series was used to construct composites of IVT, atmospheric circulation, and precipitation. The results from both methods indicate that moisture originating from the Pacific that leads to extreme precipitation in the IMW during winter takes distinct pathways and is influenced by gaps in the Cascades (Oregon-Washington), the Sierra Nevada (California), and Peninsular Ranges (from Southern California through Baja California). The moisture transported along these routes appears to be the primary source for heavy precipitation for the mountain ranges in the IMW. The synoptic conditions associated with the dominant IVT patterns include a trough-ridge couplet at 500 hPa, with the trough located northwest of the ridge where the associated circulation funnels moisture from the west-southwest through the mountain gaps and into the IMW. [ABSTRACT FROM AUTHOR]

This manuscript documents numerical modeling experiments based on a January 2010 atmospheric river (AR) event that caused extreme precipitation in Arizona. The control experiment (CNTL), using the Weather Research and Forecasting (WRF) Model with 3-km grid spacing, agrees well with observations. Sensitivity experiments in which 1) model grid spacing decreases sequentially from 81 to 3 km and 2) upstream terrain is elevated are used to assess the sensitivity of interior precipitation amounts and horizontal water vapor fluxes to model grid resolution and height of Baja California terrain. The drying ratio, a measure of airmass drying after passage across terrain, increases with Baja's terrain height and decreases with coarsened grid spacing. Subsequently, precipitation across Arizona decreases as the Baja terrain height increases, although it changes little with coarsened grid spacing. Northern Baja's drying ratio is much larger than that of southern Baja. Thus, ARs with a southerly orientation, with water vapor transports that can pass south of the higher mountains of northern Baja and then cross the Gulf of California, can produce large precipitation amounts in Arizona. Further experiments are performed using a linear model (LM) of orographic precipitation for a central-Arizona-focused subdomain. The actual incidence angle of the AR (211°) is close to the optimum angle for large region-mean precipitation. Changes in region-mean precipitation amounts are small (~6%) owing to AR angle changes; however, much larger changes in basin-mean precipitation of up to 33% occur within the range of physically plausible AR angles tested. Larger LM precipitation sensitivity is seen with the Baja-terrain-modification experiments than with AR-angle modification. [ABSTRACT FROM AUTHOR]

Atmospheric rivers (ARs) are a dominant mechanism for generating intense wintertime precipitation along the U.S. West Coast. While studies over the past 10 years have explored the impact of ARs in, and west of, California's Sierra Nevada and the Pacific Northwest's Cascade Mountains, their influence on the weather across the intermountain west remains an open question. This study utilizes gridded atmospheric datasets, satellite imagery, rawinsonde soundings, a 449-MHz wind profiler and global positioning system (GPS) receiver, and operational hydrometeorological observing networks to explore the dynamics and inland impacts of a landfalling, flood-producing AR across Arizona in January 2010. Plan-view, cross-section, and back-trajectory analyses quantify the synoptic and mesoscale forcing that led to widespread precipitation across the state. The analyses show that a strong AR formed in the lower midlatitudes over the northeastern Pacific Ocean via frontogenetic processes and sea surface latent-heat fluxes but without tapping into the adjacent tropical water vapor reservoir to the south. The wind profiler, GPS, and rawinsonde observations document strong orographic forcing in a moist neutral environment within the AR that led to extreme, orographically enhanced precipitation. The AR was oriented nearly orthogonal to the Mogollon Rim, a major escarpment crossing much of central Arizona, and was positioned between the high mountain ranges of northern Mexico. High melting levels during the heaviest precipitation contributed to region-wide flooding, while the high-altitude snowpack increased substantially. The characteristics of the AR that impacted Arizona in January 2010, and the resulting heavy orographic precipitation, are comparable to those of landfalling ARs and their impacts along the west coasts of midlatitude continents. [ABSTRACT FROM AUTHOR]

This wind profiler–based study highlights key characteristics of the barrier jet along the windward slope of California’s Sierra Nevada. Between 2000 and 2007 roughly 10% of 100 000 hourly wind profiles, recorded at two sites, satisfied the sierra barrier jet (SBJ) threshold criteria described in the text. The mean magnitude of the terrain-parallel flow in the SBJ core (i.e., Vmax) was similar at both sites (∼17.5 m s−1) and at a comparable altitude, 500–1000 m above the surface. The cross-mountain wind speed was weak at the altitude of Vmax, consistent with blocked conditions. The seasonal cycle of SBJ occurrences showed a maximum during the cooler months and a minimum in summer. Additionally, the SBJ was stronger in winter than in summer. Because the warm-season (May–September) SBJs were different than their cool-season (October–April) counterparts and occurred during California’s dry season, they were not discussed in detail. An inventory of ∼250 cool-season SBJ cases from the two sites was generated (a case contains ≥8 consecutive SBJ profiles). Up to 60% of the nearby cool-season precipitation fell during SBJ cases, and these cases shifted the precipitation down the sierra’s windward slope and enhanced precipitation at the north end of the Central Valley (relative to non-SBJ conditions). The large number of cool-season SBJ cases was stratified by the mean strength and altitude of Vmax and by the case duration. Composite profiles of the along-barrier component for the top- and bottom-20 ranked cases in each of these three SBJ classes reveal stark differences in the magnitude and vertical positioning of the barrier jet. The three SBJ classes yielded uniquely different local precipitation characteristics in proximity to the wind profilers, with the strongest and longest-lived SBJs yielding the greatest precipitation. North American Regional Reanalysis plan-view composites were generated to explore the synoptic conditions responsible for, and to showcase the precipitation distributions associated with, the top- and bottom-20 ranked cases in each of the three classes of SBJs. The composite analyses yielded large contrasts between the SBJ classes that could prove useful in forecasting SBJs and their precipitation impacts. All SBJ classes occurred, on average, in the pre-cold-frontal environment of landfalling winter storms. [ABSTRACT FROM AUTHOR]

Using a 6-km-resolution regional climate simulation of Southern California, the effect of orographic blocking on the precipitation climatology is examined. To diagnose whether blocking occurs, precipitating hours are categorized by a bulk Froude number. The precipitation distribution becomes much more spatially homogeneous as the Froude number decreases, and an inspection of winds confirms that this results from the increasing prevalence of orographic blocking. Low Froude (Froude approximately less than 1), blocked cases account for a large fraction of climatological precipitation, particularly at the coastline where more than half is attributable to blocked cases. Thus, the climatological precipitation–slope relationship seen in observations and in the simulation is a hybrid of blocked and unblocked cases. Simulated precipitation distributions are compared to those predicted by a simple linear model that includes only rainfall arising from direct forced topographic ascent. The agreement is nearly perfect for high Froude (Froude substantially larger than 1) cases but degrades dramatically as the index decreases; as blocking becomes more prevalent, the precipitation–slope relationship becomes continuously weaker than that predicted by the linear model. Because of its high fidelity during unblocked cases, it is surmised that blocking effects are the primary limitation preventing the linear model from accurately representing precipitation climatology and that the representation would be significantly improved during low Froude hours by the addition of a term to reduce the effective slope of the topography. These results suggest orographic blockingmay substantially affect climatological precipitation distributions in similarly configured coastal areas. [ABSTRACT FROM AUTHOR]

Given uncertainty in precipitation gauge-based gridded datasets over complex terrain, we use multiple streamflow observations as an additional source of information about precipitation, in order to identify spatial and temporal differences between a gridded precipitation dataset and precipitation inferred from streamflow. We test whether gridded datasets capture across-crest and regional spatial patterns of variability, as well as year-to-year variability and trends in precipitation, in comparison to precipitation inferred from streamflow. We use a Bayesian model calibration routine with multiple lumped hydrologic model structures to infer the most likely basin-mean, water-year total precipitation for 56 basins with long-term (>30 year) streamflow records in the Sierra Nevada mountain range of California. We compare basin-mean precipitation derived from this approach with basin-mean precipitation from a precipitation gauge-based, 1/16° gridded dataset that has been used to simulate and evaluate trends in Western United States streamflow and snowpack over the 20th century. We find that the long-term average spatial patterns differ: in particular, there is less precipitation in the gridded dataset in higher-elevation basins whose aspect faces prevailing cool-season winds, as compared to precipitation inferred from streamflow. In a few years and basins, there is less gridded precipitation than there is observed streamflow. Lower-elevation, southern, and east-of-crest basins show better agreement between gridded and inferred precipitation. Implied actual evapotranspiration (calculated as precipitation minus streamflow) then also varies between the streamflow-based estimates and the gridded dataset. Absolute uncertainty in precipitation inferred from streamflow is substantial, but the signal of basin-to-basin and year-to-year differences are likely more robust. The findings suggest that considering streamflow when spatially distributing precipitation in complex terrain may improve its representation, particularly for basins whose orientations (e.g., windward-facing) are favored for orographic precipitation enhancement. [ABSTRACT FROM AUTHOR]