This study utilizes multiple decades of daily streamflow data gathered in four major watersheds in western Washington to determine the meteorological conditions most likely to cause flooding in those watersheds. Two are located in the Olympic Mountains and the other two in the western Cascades; and each has uniquely different topographic characteristics. The flood analysis is based on the maximum daily flow observed during each water year (WY) at each site [i.e., the annual peak daily flow (APDF)], with an initial emphasis on the 12 most recent water years between WY1998 and 2009, and then focusing on a 30-year interval between WY1980 and 2009. The shorter time period coincides with relatively complete passive microwave satellite coverage of integrated water vapor (IWV) over the Pacific basin. The combination of IWV imagery and streamflow data highlights a close link between landfalling atmospheric rivers (ARs) and APDFs (i.e., 46 of the 48 APDFs occurred with landfalling ARs). To complement this approach, the three-decade time series of APDFs, which correspond to the availability of the North American Regional Reanalysis (NARR) dataset, are examined. The APDFs occur most often, and are typically largest in magnitude, from November to January. The NARR is used to assess the composite meteorological conditions associated with the 10 largest APDFs at each site during this 30-year period. Heavy precipitation fell during the top 10 APDFs, and anomalously high composite NARR melting levels averaged ;1.9 km MSL, which is primarily above the four basins of interest. Hence, on average, mostly rain rather than snow fell within these basins, leading to enhanced runoff. The flooding on the four watersheds shared common meteorological attributes, including the presence of landfalling ARs with anomalous warmth, strong low-level water vapor fluxes, and weak static stability. There were also key differences that modulated the orographic control of precipitation. Notably, two watersheds experienced their top 10 APDFs when the low-level flow was southwesterly, while the other two basins had their largest APDFs with west-southwesterly flow. These differences arose because of the region's complex topography, basin orientations, and related rain shadowing.

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.

17In mountain terrain, well-configured high-resolution atmospheric models are able to 18 simulate total annual rain and snowfall better than spatial estimates derived from in situ 19 observational networks of precipitation gauges, and significantly better than radar or 20 satellite-derived estimates. This conclusion is primarily based on comparisons with 21 streamflow and snow in basins across the Western United States and in Iceland, Europe, 22and Asia. Even though they outperform gridded datasets based on gauge-networks, 23 atmospheric models still disagree with each other on annual average precipitation and 24 often disagree more on their representation of individual storms. Research to address 25 these difficulties must make use of a wide range of observations (snow, streamflow, 26 ecology, radar, satellite) and bring together scientists from different disciplines and a wide-27 range of communities. 28 29 30 Capsule 31We have now crossed a threshold where, for many mountain ranges, well-configured high-32 resolution atmospheric models are better able to represent range-wide total annual 33 precipitation than the collective network of precipitation gauges, i.e., observations. 34 35 3 Lundquist et al.: Orographic precip: Models vs. Obs 36 Introduction 37We have now crossed a threshold where, for many mountain ranges, well-configured 38 high-resolution atmospheric models are better able to represent range-wide total annual 39 precipitation than the collective network of precipitation gauges, i.e., observations. The 40 prior sentence is disturbing. If we even assign some truth to the statement "Models are 41 better than observations," where does that lead us? If two models are "better than 42 observations" but disagree with each other, which one should we trust more? What do we 43 mean by "better," and what counts as an "observation"? Generalities are dangerous, and for 44 which models, which observations, and which times and locations might this statement be 45 true? How do we identify these specificities in a way that allows us to move forward, 46 scientifically and objectively, in a situation where the truth is difficult to discern? 47Here, we review recent research that collectively suggests, at least for the mid to 48 northern latitudes, that modeled-precipitation has crossed a threshold in range-wide 49 accuracy relative to observation-based-precipitation datasets in complex terrain (Sections 50 2 and 3). We carefully examine the basis for this conclusion, which often consists of 51 multiple indirect observations. We then propose that crossing this threshold requires a 52 fundamental shift in how hydrologists and atmospheric scientists interact. In the past, 53 gridded precipitation datasets that interpolated between existing observations, such as 54 PRISM (Daly et al. 2008), provided a stable medium of both the best available input to a 55 hydrologic model and the best available benchmark for an atmospheric model (Figure 1). 56These gridded datasets are often referred to as "observations," when in truth th...

[1] Fire plays a crucial role in many ecosystems, and a better understanding of different controls on fire activity is needed. Here we analyze spatial variation in fire danger during episodic wind events in coastal southern California, a densely populated Mediterranean-climate region. By reconstructing almost a decade of fire weather patterns through detailed simulations of Santa Ana winds, we produced the first high-resolution map of where these hot, dry winds are consistently most severe and which areas are relatively sheltered. We also analyzed over half a century of mapped fire history in chaparral ecosystems of the region, finding that our models successfully predict where the largest wildfires are most likely to occur. There is a surprising lack of information about extreme wind patterns worldwide, and more quantitative analyses of their spatial variation will be important for effective fire management and sustainable long-term urban development on fire-prone landscapes.

The atmospheric conditions that

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.

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.

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 Froude number decreases, and an inspection of winds confirms that this is due to increasing prevalence of orographic blocking. 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 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. We therefore surmise the linear model would be significantly improved during low Froude hours by the addition of a term to reduce the effective slope of the topography. Low Froude, 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. These results suggest orographic blocking may substantially affect climatological precipitation distributions in similarly configured coastal areas.1