1. Introduction
In the maritime mountain ranges of western North America, most precipitation falls during the winter months, with a large percentage falling in the form of snow. Reservoir managers must balance the objective of storing water for summer use with the need to release water to provide flood protection from warm winter storms. Coastal basins spanning a wide range of elevations, such as the North Fork of the American River basin in California (Fig. 1), are extremely sensitive to rain falling on snow at mid- to high elevations (Kattelmann 1997; Osterhuber 1999). Many studies of climatic change show that these same river basins are extremely sensitive to regional warming. Over the past 50 yr, an increasingly greater percentage of precipitation has fallen in the form of rain rather than snow, and this trend is likely to continue (Dettinger et al. 2004; Jeton et al. 1996; Knowles et al. 2006; Lettenmaier and Gan 1990). However, while the importance of flooding and erosion associated with warm rain events has been repeatedly stressed (Brunengo 1990; Ffolliott and Brooks 1983; Hall and Hannaford 1983; Harr 1986; Kattelmann 1997; Marks et al. 1998; McCabe et al. 2007; Zuzel et al. 1983), hydrologic forecasting centers still struggle to predict the elevation at which snow turns to rain in mountain watersheds (E. Strem, California–Nevada River Forecast Center, 2006, personal communication).
In the West Coast mountains, particularly warm storms result in floods primarily because rain falls at higher elevations and over a much larger contributing area for runoff than during a typical storm. Figure 2 illustrates the fraction of the North Fork American River basin contributing to runoff based on the elevation below which precipitation falls as rain. As the rain–snow line rises from 800 to 2800 m (a common range for the melting level in California winter storms), the contributing fraction increases from 25% to 100% of the basin, resulting in up to 4 times as much runoff for a given rain rate. White et al. (2002) tested the importance of the melting level by comparing river forecast simulations of peak flow as a function of melting level in four California watersheds. In three of the four watersheds, runoff tripled if the snow level rose 600 m (White et al. 2002). These results, combined with the steep slope of the curve in Fig. 2, indicate that significant uncertainty in streamflow prediction can occur as a result of relatively small errors in characterizing the precise altitude at which snow accumulates in a given storm. For these reasons it is important to monitor, understand, and predict the details of the snow level to within relatively small margins of error. This paper uses unique measurements, collected during the National Oceanic and Atmospheric Administration’s (NOAA) Hydrometeorological Test Bed field campaign (Ralph et al. 2005), to improve understanding of the melting level and snow accumulation transition zone. The analysis includes a novel radar technique to measure conditions aloft in combination with more traditional in situ observations at the earth’s surface.
Because of the difference in radar reflectivity and fall speeds between frozen and liquid hydrometeors, Doppler profiling radars are able to detect the altitude in the atmosphere where rain changes to snow (Battan 1973; Cunningham 1947; Fabry and Zawadzki 1995; Marshall et al. 1947; Mittermaier and Illingworth 2003; Stewart et al. 1984; White et al. 2002). The dielectric constant increases as melting ice particles become coated with water, and a layer of enhanced radar reflectivity, or “bright band” appears. The brightband height (BBH), defined as the altitude of maximum radar reflectivity, occurs on average about 200 m below the 0°C isotherm (White et al. 2002; for measurements taken on the California coast) at temperatures usually between 1° and 2°C (Stewart et al. 1984; for measurements near the Sierra Nevada). Doppler wind-profiling radars along the coast and central valley of California have been observing brightband heights since 1996, and an automated algorithm developed by White et al. (2002) makes this information available in real time to river forecasters.
For hydrologic applications, the BBH in the free atmosphere must be related to the melting level on the surface some distance away. First, snow falling in the free atmosphere starts melting at 0°C and changes to rain over time and space as a complex function of temperature, humidity, winds, and the type and fall rate of hydrometeors. Most winter storms impacting the mountains of the west coast of North America possess a fairly stable, stratiform structure (Medina et al. 2005), with ice crystals forming aloft and then falling through warmer atmospheric layers below, where they melt and become rain. The vertical distance through which the snow crystals pass before they are completely melted depends on the size and density of the crystals, the fall rate of the crystals (1 to 1.5 m s−1 for most snowflakes, increasing to 4 to 9 m s−1 for mostly melted snowflakes; Szyrmer and Zawadski 1999 and Mitra et al. 1990), the ventilation of the hydrometeors, and the balance of heat at the particle surface (due to conduction of heat from the warmer surrounding air and to latent heat released onto the melting particles during water vapor condensation; Szyrmer and Zawadski 1999). Aircraft observations indicate that, in general, the ratio of liquid to total hydrometeor mass increases linearly as temperatures increase from 0.3° to 2.2°C (Stewart et al. 1984), over an atmospheric thickness of 200 to 500 m (Fabry and Zawadzki 1995).
Second, the atmospheric structure and corresponding melting levels change as an air mass intersects a mountain range, such as the Sierra Nevada, and is influenced by boundary layer effects. Specifically, orographic lifting causes adiabatic cooling, and greater precipitation rates increase diabatic cooling, frequently resulting in a lowering of the BBH as it intersects the windward slopes of a mountain range (Marwitz 1983, 1987; Medina et al. 2005). Diabatic cooling can also result in down-valley airflow in mountain valleys, often in the opposite direction of the general storm movement (Steiner et al. 2003).
Detailed microphysical models are able to represent all of the processes related to the melting level height and thickness in one dimension but become computationally unwieldy in two or three dimensions, where slight variations in precipitation microphysics can set up mesoscale circulation patterns, which in turn feed back and change hydrometeor fall rates and melting rates (Szyrmer and Zawadski 1999). Thus, most operational weather forecast models parameterize ice microphysics (Barros and Lettenmaier 1994; Lin et al. 1983; Schultz 1995), and different parameterization schemes often result in quite different precipitation distributions over a basin (Wang and Georgakakos 2005; Wang and Georgakakos 2007, manuscript submitted to J. Geophys. Res.). Most hydrologic models discount atmospheric microphysics altogether, instead relying on surface temperature cutoffs to delineate rain from snow (Anderson 1976; Storck 2000; Westrick and Mass 2001).
At the mountain surface, these near-surface air temperatures provide a good estimate of precipitation type, such that at temperatures below 0°C, over 90% of precipitation events occur as snow; at temperatures above 3°C, over 90% of precipitation events occur as rain; and rain and snow are equally likely at 1.5°C (U.S. Army Corps of Engineers 1956). These empirically derived distributions of rainfall versus snowfall as a function of surface temperature are remarkably consistent across the globe, with similar results found in the Alps (Rohrer 1989), the Bolivian Andes (L’Hôte et al. 2005), and Sweden (Feiccabrino and Lundberg 2008). Detailed observations in a mixed precipitation event in the Oregon Cascades also fell within this range, where Yuter et al. (2006) used a Parsivel disdrometer to observe that rain particles made up 5% of the total concentration of hydrometeors at 0°C, 23% at 0°–0.5°C, and 93% at 0.5°–1.5°C.
Finally, the antecedent snow cover and ground cover in a river basin affect the final form precipitation takes on the surface—rain may freeze within a snowpack and be stored for some time, or snow may melt on bare ground and contribute to runoff. This last scenario—what happens on the surface—is most important in flood forecasting.
While the processes governing melting snowflakes in the atmospheric melting layer and the relationship between surface temperature and snow accumulation are relatively well known, the precise relationship between measurements of the melting level aloft and snow accumulation behavior at the surface has not been well documented, especially in complex terrain. This paper determines if BBHs measured atmospherically upstream of a mountain range can be used to infer melting levels within a hydrologic basin. Specifically, we track the altitude of the melting level from the free atmosphere, to the mountain boundary layer, to the ground (or snow covered) surface for winters 2001–2005, as illustrated in Fig. 3. Significant heights and temperatures are detailed in Table 1. Section 2 describes the observations and the methods used for classifying precipitation as rain or snow. Section 3 presents two case studies of mixed rain and snow events in the American River basin to illustrate the rapid changes in melting level and the complex surface responses associated with winter storms. Section 4 establishes temperature as an index for where surface snowmelt is taking place and compares near-surface and free-atmosphere measurements during storm events. Section 4 also presents 5-yr statistics of how these temperatures vary with location and time of day, and how they relate to different melting altitudes and different contributing areas in the North Fork of the American River. Section 5 discusses applications for streamflow forecasting and directions for further research.
2. Observations and methods
Free atmosphere melting levels (FAML; 0°C isotherm) were determined by temperature measurements from radiosondes launched at Oakland, California, and compared with BBHs at four 915-MHz radar profilers. Boundary layer melting levels were determined from a transect of surface temperatures in the American River basin, and the fate of rain versus snow at the surface was determined from precipitation, temperature, snow water equivalent (SWE), and snow depth measurements at California Department of Water Resources (CA DWR) snow pillow stations in the same basin. The American River basin is located downwind of the profilers along the primary winter storm track, which is from the west (Fig. 1).
a. Radar profilers
The 915-MHz Doppler wind profilers provide wind and precipitation profile measurements in the boundary layer and lower free troposphere, and data from four profilers in California are analyzed here (Table 2; Fig. 1). Signal-to-noise ratios (SNRs) indicate regions of high reflectivities, and Doppler vertical velocities (DVVs) approximate the fall speeds of hydrometeors. BBHs were determined using the automated algorithm detailed in White et al. (2002), which picks out the altitude where DVV starts decreasing with height, indicating the height of slower-falling snow particles, in tandem with SNR increasing with height, indicating the greater reflectance of large, water-coated snow and ice particles (Fig. 3). This level is determined with hourly temporal resolution and 100-m vertical resolution.
b. Oakland radiosonde
Upper-air measurements of temperature, dewpoint temperature, wind speed, and direction were obtained from the National Weather Service (NWS) Radiosonde Network site at Oakland, California, where they are measured at 0000 and 1200 UTC [0400 and 1600 Pacific standard time (PST)] each day. This site is generally upwind of the American River basin and represents free-air conditions before they encounter the topography of the Sierra. Wet-bulb temperatures and virtual temperatures were calculated from the measured variables (Jensen et al. 1990). The altitude of the 0°C isotherm (FAML, Table 1) and the temperature at the BBH were both determined using linear interpolation from available measurements. While this approach may result in errors during the hours surrounding a frontal passage, it is the best available approximation given the existing data.
c. Surface meteorology, snow, and hydrology
The CA DWR manages a network of 41 automated meteorological monitoring stations in or near the American River basin (Fig. 1; Table 3). These stations all measure precipitation and 23 of them also measure air temperature, typically from a sensor mounted on a pole or mast, approximately 10 m above ground level. Thirteen of the stations have snow pillows that measure the weight of snow accumulation and thereby indicate the SWE of the snow column. Data were obtained from CA DWR (http://cdec.water.ca.gov).
Most snow pillows are located in flat meadows, surrounded by forested areas that shelter the pillow from wind scouring (Farnes 1967). Compared to nearby forested areas, these sites tend to accumulate more snow and are also more exposed to sunlight during the melt season. Because snowmelt during a precipitation event is caused primarily by turbulent energy transfer, which grows with increasing wind speed (Marks et al. 1998), snow pillows may record more melt than a completely forested area and significantly less melt than an unsheltered area. While snow pillows do not record the full range of snow properties due to varying slope, aspect, and vegetative cover, they provide the most comprehensive set of high-elevation measurements available.
The CA DWR snow pillows report information at hourly intervals, but because pillows can experience several hours of delay in responding to changes in SWE (Beaumont 1965; Trabant and Clagett 1990), 12-h averages were used here. An increase in SWE may be due to snow falling on the pillow or to liquid water falling on snow already on the pillow and freezing into the snowpack, thus increasing its density. Decreases in SWE are due to snowmelt or sublimation. Redistribution of SWE from or to areas outside of the snow pillow area may also be responsible for changes. However, this effect is slight at most California snow pillows because of the high density of the snowpack and their flat locations with wind shelter from the surrounding trees.
Snow depth measurements, which are only available at a small subset of the stations (Blue Canyon, Meadow Lake, and Caple’s Lake; Fig. 1b), acoustically detect changes in snow depth at an hourly time scale. An increase in snow depth can only occur with snowfall or with deposits of drifting snow. However, the latter is rare during a rain event in the Sierra Nevada. A decrease in snow depth could be due to melt or compaction. These changes are accurate on an hourly time scale but also include noise, particularly during a snow storm, when the acoustic signal can reflect off of falling snowflakes. To minimize the noise, we smoothed the depth record with a 20-h Blackman-window filter and then established a cutoff where we declared snowfall to occur in any hour when the depth increased by at least 0.35 cm.
Most precipitation sensors in the region consist of a reservoir with antifreeze. These report accumulated precipitation and are also subject to instrumental noise. Incremental precipitation was determined by smoothing the record with a 20-h Blackman-window filter and taking the difference between hourly reports of the smoothed accumulated precipitation. A cutoff of 0.04 cm h−1 was used to indicate that precipitation actually occurred. Blue Canyon was equipped with a heated tipping bucket in September 2004 to measure incremental precipitation. Because this instrument is more sensitive than the antifreeze reservoirs, a cutoff of 0.02 cm h−1 was used to signify the occurrence of precipitation at this station. This is equivalent to 1 tip of the bucket in the rain gauge (0.01 in. or 0.254 mm).
Mean daily discharge measurements were examined for the North Fork (NF) of the American River (Fig. 1). It does not have dams or diversions and is included in the U.S. Geological Survey (USGS) hydroclimatic dataset (Slack and Landwehr 1992). The record began in 1941, the gauge is at an elevation of 218 m, basin area is 886 km2, and mean basin elevation is 1190 m.
d. Temperature–SWE PDF
The fate of precipitation after it reached the ground was determined by creating a probability density function (PDF) of change in SWE given a precipitation event at a given temperature. Data at all snow pillows were quality controlled, and 10 stations (indicated in Table 3) were selected for having reliable measurements of both SWE and collocated temperature. Hourly SWE, surface air temperature, and precipitation observations were averaged every 12 h. Next, the change in SWE and the incremental precipitation over each 12-h interval was calculated. Because of the lack of quality precipitation data in the snow zone, precipitation data at all 40 stations (Table 3) were considered. If 10 or more of these stations reported increased precipitation, then the precipitation rate was determined as the average of all stations. If less than 10 stations reported precipitation, then the precipitation rate was set at zero, and that 12-h time step was not considered in the subsequent analysis. Data were binned by temperatures (at 0.5°C increments) and normalized by the number of measurements at each temperature to produce probability density functions.
3. Case studies—Radar brightband levels and surface rain, temperature, and snow
Two case studies from winter 2005 were chosen to illustrate how radar measurements of BBH correspond with measurements of temperature, rainfall, and snow gain or loss at various surface elevations. BBHs varied from 600 to 3000 m during the winter 2005 period, and increased NF American River streamflow generally corresponded to warm storms, indicated by high-altitude BBHs (Fig. 4). Warm storms not only resulted in liquid precipitation falling over large fractions of the basin, but they also carried the most moisture and, hence, the highest precipitation rates. The two case study periods (marked, Fig. 4) both encompassed storms with a wide range of melting levels, but the two had very different streamflow responses.
a. January 2005
The 25 to 29 January case provided a simple example of a cold front passing with dropping melting levels in early winter. Rain falling at Blue Canyon (1600 m) was incorporated into the snowpack, increasing SWE, with minimal contributions to streamflow. Radar returns at the Grass Valley wind profiler showed decreasing BBHs during this period (Fig. 5a). Surface temperatures at Blue Canyon (1600 m) fell from 10° to −1°C, and temperatures at Caple’s Lake (2400 m) fell from 10° to −11°C (Fig. 6a). On 25 January, precipitation increased (Fig. 6b), and snow depth decreased slightly at both Caple’s Lake and Blue Canyon (Fig. 6c), indicating that rain compacted the snowpack. On 26 January, snow depth mostly increased at Caple’s Lake (with a slight decrease near noon) and decreased at Blue Canyon, indicating that while mostly snow fell at Caple’s Lake, rain fell at Blue Canyon (Fig. 6c). SWE increased at both locations (Fig. 6d), indicating that at least some of the rain at Blue Canyon was incorporated into the snowpack, increasing its density and water content, and not directly contributing to streamflow (Fig. 4). By the 28 January precipitation event, temperatures were at or below freezing at both locations, and snow fell at both locations, as indicated by increases in snow depths and SWE.
Changes in temperature (Fig. 7a) and precipitation type (Fig. 7b) with elevation corresponded with dropping radar BBHs at both Bodega Bay (the profiler on the coast, west of the American River basin) and at Grass Valley (the profiler in the Sierra foothills, just slightly north of the American River basin). On 25 January the entire basin from 1500- to 2500-m elevation was warmer than 2°C, and rain fell at both Caple’s Lake and Blue Canyon. Bodega Bay melting levels on 25 January were at about 2000 m, but this cooler storm sector did not arrive at the American River until midnight that night, when Grass Valley reported melting levels at about 2000 m, and temperatures above that altitude dropped below 0°C. At this time, snow fell at Caple’s Lake and Meadow Lake, while rain continued at Blue Canyon. On 26 January daytime rainfall and temperatures above 2°C occurred at Caple’s Lake, when the Grass Valley BBH was about 500 m lower. This may be explained by Caple’s Lake’s location being 107 km southeast of Grass Valley (Fig. 1), thus remaining in a warmer sector of the storm. When precipitation resumed near midnight on 27 January, temperatures were much colder, and the BBH at Grass Valley was below 1500 m. Snow fell at all monitored locations. Some profiler measurements at Bodega Bay and Grass Valley indicated BBHs slightly above the altitude of Blue Canyon. While surface measurements indicated that Blue Canyon had a net gain of snow, a mixture of rain and snow may have been falling from the sky. The BBH may also have dropped in the distance between Grass Valley and Blue Canyon (about 44 km).
b. March 2005
The 19–22 March case illustrated a more complicated sequence of storms, alternating between warm and cold conditions (Fig. 5b). During the second warm period, snow melted while rain fell at high rates at Blue Canyon, and American River discharge rose steeply. At the start of the storm on 19 March, snow depth decreased slightly at Blue Canyon but then began increasing at midday as temperatures dropped and rain changed to snow (Fig. 8). Snowfall continued through midday on 21 March, and then temperatures rose during a break in the storm. The second storm began several hours later and was considerably warmer, with a decrease in snow depth and SWE (indicating melting snow in addition to falling rain) at Blue Canyon (Fig. 8). At midday on 22 March, Caple’s Lake reported an increase in temperature, a sharp increase in precipitation, and slight drops in snow depth and SWE. After this time, temperatures dropped at both stations, rain switched to snow, and snow depths and SWE increased.
These patterns of alternating warm and cold storm sectors were also observed by the profilers (Fig. 9). Temperatures at all altitudes above 1500 m dropped below 0°C on 19 and 20 March, following the drop of the BBH at Grass Valley. BBH changes at Bodega Bay preceded those at Grass Valley by about 4 h. The switch from rain to snow at Blue Canyon on 22 March coincided with drops in BBHs at both Bodega Bay and Grass Valley. However, data at Caple’s Lake indicated an earlier switch to snow, and Meadow Lake reported increases in snow depth, that is, continuous snow rather than rain, throughout the warm sector of the storm. During the warm sector (early 22 March), rain fell at a rate greater than 4 mm h−1 over more than 90% of the basin. This, combined with already-saturated snowpacks and soils, resulted in a large streamflow response to this storm (Fig. 4).
c. Case study summary
The case studies above illustrate that qualitatively, radar BBHs represent the patterns and trends of melting levels on the mountain surface at an hourly time scale. However, the case studies also illustrate the difficulty of quantitatively determining the form and fate of precipitation hitting the ground, particularly in regions without human observers. From the case studies, several important points emerge. First, while cloud microphysics are difficult to model and represent spatially across a basin, preexisting surface snow characteristics are often more important than hydrometeor distributions in determining where precipitation will contribute to runoff. For example, rain at Blue Canyon was incorporated into the snowpack during case 1 but contributed to snowmelt and runoff during case 2, likely because case 1 occurred earlier in the year when the snowpack was colder and not yet saturated. Thus, improvements in model representations of snowpack temperature and liquid water content would help improve hydrologic forecasts.
Second, distributions of rain versus snow vary spatially and are not solely functions of time and elevation. For example, Caple’s Lake to the southeast appeared to be in a warmer sector of the storm during case study 1, receiving rain when the altitude of the BBH measured at the Bodega Bay and Grass Valley profilers indicated snow should fall at that elevation. During case 1, the Bodega Bay and Grass Valley profilers agreed on the BBH when they reported measurements at the same time, but during case 2, Grass Valley consistently reported a lower-altitude BBH than Bodega Bay. In both cases, the Grass Valley measurement corresponded better with surface temperature measurements. This highlights the danger of comparing one profiler with one surface station, and the importance of examining the profiler measurements in relation not only to each other but to as many surface measurements as possible to better understand statistical variations across the region of interest.
4. Temperatures and melting statistics from 2001 to 2005
Few measurements of precipitation type exist, and these are noisy. However, temperature measurements are reliable, continuous, and widely available. By indexing SWE gain or loss during a precipitation event to temperature and then comparing elevational transects of surface temperatures to radar-detected BBHs, we can determine statistically whether and by how much the BBH should be adjusted up or down to represent the surface melting level as a function of location and time.
a. Free-air temperatures at BBHs
Oakland sounding temperatures, sampled every 12 h, were interpolated to hours when BBHs were detected at Bodega Bay (located about 100 km northwest of Oakland). Free-air temperatures (T) above Oakland at the Bodega Bay BBH averaged 1.8°C, and wet-bulb temperatures (Tw) averaged 0.3°C for the five winters. The standard deviation for both T and Tw over the 5-yr period was approximately 1.5°C, probably representing changing thicknesses of the melting region and different storm structures, orientations, and motions relative to these two sites. These values did not vary with the hour of the day and are consistent with prior observations.
Because the other stations were considerably further from Oakland than Bodega Bay, they sampled air masses with different vertical temperature structures. Chico (182 km northeast of Bodega Bay) sampled colder air masses, so that at times when both stations had measurements during the 5-yr period, BBHs were on average 174 m lower at Chico than at Bodega Bay. Chowchilla (283 km southeast of Bodega Bay) sampled warmer air masses, so BBHs at Chowchilla were on average 49 m higher than at Bodega Bay. Grass Valley is only slightly north of Bodega Bay, but 195 km inland, and it averaged melting levels 106 m lower. Grass Valley is on the windward slope of the Sierra, so this result is consistent with observations from studies in the Sierra (Marwitz 1983, 1987) and the Alps and Cascades (Medina et al. 2005) that show that BBHs dip to lower elevations on the windward slopes of mountains.
Mean BBHs for each station (averaged over the entire 5-yr period, without regard to having simultaneous measurement of the melting level with a neighboring station) were 1796 m at Bodega Bay, 1744 m at Chico, 1691 m at Grass Valley, and 1851 m at Chowchilla. Thus, on average, BBHs in California storms decrease with increasing latitude and with proximity to the windward slope of the Sierra Nevada. These spatial slopes in BBHs should be considered when applying melting level information to a river basin north, south, or inland of a given profiler. For example, a line fit to the average heights at the three nonfoothill stations yielded a decrease in average BBH of 41.4 m per increase in degree latitude. The average BBH at Grass Valley is 73 m lower than would be expected based on its latitude and this line. This is smaller than the approximate 400 m decrease in BBH observed where the bright band intersected the mountain in prior studies (Marwitz 1983, 1987; Medina et al. 2005), perhaps because Grass Valley is generally lower in elevation than the freezing level and is only detecting the beginning of the dip in BBH as storms intersect the mountain slopes.
b. Temperature at which snow melts on the surface
To compare BBHs with the snow response at the surface, we created a probability density function of change in SWE during precipitation events as a function of temperature. Because of the scarcity of humidity data measured at the surface, these are determined based on actual, and not wet-bulb, temperatures. Based on the similar structure and variance of actual and web-bulb temperatures measured in the free atmosphere during these events (section 4a), we assume little information is lost by neglecting surface humidity information. Figure 10a shows the likelihood of SWE gain or loss over a 12-h period, given a local surface temperature, at the 10 snow pillow stations indicated in Table 2 for 2001–05, as a ratio of change in SWE to the average basin precipitation. Although there is much scatter in the data, several features emerge. Below 0°C there is about 90% chance that snow will accumulate, and about 60% chance that snow will accumulate at a mean rate equal to or larger than the measured rate of average basin precipitation. The likelihood of the latter is due both to orographic precipitation enhancement (the snow pillows are all in the upper elevations of the basin while most precipitation sensors are at lower elevations) and to the precipitation sensors’ likelihood of undercatch during mixed rain and snow events. Above 3°C the surface snowpack is >60% likely to melt (Fig. 10b). In between 0° and 3°C, precipitation may fall as rain, snow, or a mixture of the two, with probabilities transitioning from likely snow to likely rain as the temperature rises within this range [Fig. 10c, determined by the U.S. Army Corps of Engineers (1956) from direct observations near the Sierra crest during winters 1946–51]. At 1.5°C, 50% of precipitation events fall as rain and 50% as snow, and on average, 50% of measured precipitation contributes to increases in SWE. Between 2.5° and 3°C, snow is equally likely to melt or accumulate, with most cases resulting in no change to SWE (Figs. 10a and 10b). This is slightly warmer than the free-atmosphere temperature where snow converts to rain because this value measures the fate of the precipitation phase at the surface. Cold rain and rain–snow mixtures falling on an existing snowpack are often frozen and incorporated into the snowpack, thus resulting in a measured increase in SWE. In other cases, rain passes through the snowpack with no measured change in SWE.
These statistics were checked for differences between night and day, for differences with averaging period (6 and 24 h), and for differences in temperature bin size (0.25° or 1°C). The resulting minor differences were not statistically significant.
c. Surface temperatures compared with brightband heights
Section 4a demonstrated that, on average, the BBH corresponds to a free-air temperature of 1.8°C. Section 4b demonstrated that roughly half of the precipitation passes through the snowpack as rain at a surface temperature of 1.5°C (Ts-half), and existing snow cover becomes likely to melt during a precipitation event with surface temperatures above 3°C (Ts-50). These indices provide a means to translate the radar BBHs into surface hydrologic events. Figure 11 shows the mean surface temperature in the American River basin at the altitude of each radar’s BBH for each hour of the day. At all stations, there was a clear diurnal cycle, where the surface was warmer during the day and cooler at night. Chowchilla, to the south, measured higher BBHs, corresponding to colder surface temperatures in the American River basin. Chico, to the north, measured lower BBHs, corresponding to warmer surface temperatures in the American River basin. Bodega Bay, on the coast, varied the most diurnally from surface temperatures, and Grass Valley, in the Sierra foothills, varied the least diurnally. This could be because it was often not yet precipitating in the American River basin when it was raining at Bodega Bay, or it could be because air masses above Bodega Bay arrived straight off the ocean and were least affected by diurnal heating and cooling from the continent. Temperatures corresponding to radar melting levels were quite variable in all cases, as represented by standard deviations roughly between 1° and 2°C at all hours of the day (Fig. 11b). Standard deviations were most variable at Bodega Bay and least variable at Grass Valley because Grass Valley is geographically closest to the American River basin.
The diurnal variation in surface temperatures at BBHs can best be explained by revisiting our March 2005 case study (Fig. 12). BBHs at Bodega Bay and Grass Valley generally fell within the 0° to 3°C temperature contours determined from Oakland soundings (Fig. 12a). However, the correspondence between BBH and temperature became less well defined when compared with contours of surface temperatures (from the 23 stations in Table 3) versus elevation (Fig. 12b). These deviations were due primarily to diurnal changes in solar radiation, which heats the surface and not the free air even during rain events. The differences between surface and free-air temperatures were particularly noticeable during the onset and end of each storm (Fig. 12c), when more sunlight reached the basin.
While radiation is a relatively small component of the total energy balance during storm events (18%–30% of the energy available for snowmelt during rain-on-snow events in Oregon in February 1996; Marks et al. 1998), it is by far the most predictable component and has consistent diurnal patterns. Marks et al. (1998) observed daily fluctuations in net radiation during rain-on-snow events ranging from −30 W m−2 at night to a peak of 50 to 100 W m−2 at solar noon. While this is about half (or less) of the diurnal variation observed during a subsequent sunny period, it is not negligible. This range in radiation alone would result in peak daytime melt rates about 1.3 mm h−1 higher than nighttime minimum melt rates. Assuming that the specific heat of vegetation (2928 J kg−1 K−1; Thom 1975) controls local surface temperatures, this radiation increase would raise peak daytime surface temperatures about 1.5°C higher than nighttime minimum temperatures. This value is similar to the observed differences in daily variations between the radar and surface temperatures.
These results suggest that melting levels at the surface should be adjusted up (to higher elevations) during daylight hours and down (to lower elevations) during nighttime hours, particularly at the onset of a storm. Figure 13 illustrates what these adjustments might be, on average, for corresponding each radar’s measured BBH with the 1.5°C contour, that is, the level where precipitation is equally likely to fall as rain or snow, in the American River basin. Bodega Bay BBHs needed to be adjusted up 270 m during daylight hours (0600 to 1800 PST) and adjusted down 8 m during nighttime hours (1800 to 0600 PST). Grass Valley (the profiler closest to the ARB) needed to be adjusted down 78 m during the day and down 205 m at night. When compared with the surface 3°C contour, where surface snow cover is likely to melt, Bodega Bay heights were an average of 223 m too high during the day and 488 m too high during the night. Grass Valley heights were an average of 318 m too high during the day and 433 m too high at night. Thus, surface snowmelt is always lower than the elevation where falling snowflakes melt to rain.
d. Uncertainties, corrections, and application to hydrologic nowcasting
The spatial and diurnal patterns of how BBHs relate to surface temperatures in the Sierra Nevada provide guidance for how these radar measurements can best be employed in hydrologic forecasting. There is a great deal of uncertainty in all of the measurements, but the data presented here can help quantify that uncertainty and, to some degree, reduce it. BBH can be determined to within 100-m vertical resolution (White et al. 2002). However, the error associated with directly interpreting that height as the elevation where snowfall changes to rain near the surface (here inferred to be 1.5°C) ranged from 326 to 457 m (Table 4). Assigning constant elevation offsets based on how each location related geographically to the American River basin reduced those RMSEs to 296 to 425 m (Table 4). Applying a further correction, with different offsets based on time of day (i.e., as illustrated in Fig. 13), reduced the RMSEs to 273 to 371 m (Table 4). The average improvement in RMSE through this process was 78 m. If we want to define the surface melting level as the height where the snowpack is likely to melt (here inferred to be 3°C), offset corrections become even more important in adjusting the radar melting levels to lower elevations. Table 4 details the RMSEs for the original and corrected heights at this level, which resulted in an average improvement of roughly 200 m.
Over most of the North Fork of the American River basin, the increase in contributing area with elevation is linear (Fig. 2), such that a change in melting level of 100 m corresponds to a change in runoff contributing area of about 48 km2, or 5% of the total basin area. Thus, an RMSE of 500 m corresponds to an error of 25% of the basin’s contributing area. A decrease of RMSE of 78 m represents a hydrologic forecasting improvement of 4% contributing area, and a decrease in RMSE of 200 m represents an improvement of 10% contributing area. For example, consider the March 2005 case study peak of 1.3 mm h−1 (11 300 ft3 s−1). Assuming a constant rain rate over the basin, 4% corresponds to 0.05 mm h−1 (441 ft3 s−1), and 10% corresponds to 0.13 mm h−1 (1130 ft3 s−1). However, even after making the corrections detailed above, the remaining RMSE is about 300 m, corresponding to 15% of the basin contributing area, leaving room for further improvements.
5. Conclusions
a. Summary
The elevation where precipitation transitions from contributing to snow water storage versus contributing to runoff is critical for hydrologic monitoring and forecasting all along the western coast of North America. Vertically pointing profiling radars are able to detect the elevation where snow changes to rain in the free atmosphere. Thus, a critical step toward improving hydrologic forecasts in these mountain basins is to quantitatively relate the radar-observed snow level to what is happening on the ground. Specifically, forecasters need to know which altitude offset would best distinguish between surface snow accumulation and runoff and need to characterize the uncertainty in this vertical offset.
To accomplish these objectives, this study tracked the altitude of the melting level from the free atmosphere, to the mountain boundary layer, to the ground (or snow covered) surface for winters 2001–05 (Fig. 3) to address three major issues. First, how well do BBHs measured at profiler sites located some distance from each other and from a river basin represent melting levels on the mountain surface? Because of the large-scale nature of most of California’s winter storm events, radars quite distant (300 km) from a river basin can be used to interpret surface melting levels. On average, RMSEs of 326 to 457 m occur when BBHs are directly assumed to represent surface melting levels. Additional local errors may arise when different basin locations are in warm versus cold sectors of a frontal passage or when drainage flows resulting from evaporative cooling develop in canyons (Steiner et al. 2003).
Second, what offsets, as a function of profiler and basin locations and of time of day, should be used to adjust BBH measurements to better match surface melting levels? Forecasters should incorporate spatial gradients of decreasing BBHs with increasing latitude (on average, −41.4 m °−1), due to regional temperature gradients, and with decreasing distance to where the bright band intersects the windward mountain slope, due to adiabatic and diabatic cooling (Medina et al. 2005). In addition to these spatial offsets, the fate of precipitation at the mountain surface will also be modified by diurnal cycles in solar radiation, which result in warmer temperatures and greater melt rates during daylight hours, even during cloudy precipitation events. Depending on a profiler’s location, BBHs would need to be shifted down 127 to 278 m further during the night than during the day to correspond with surface observations. Incorporating both space and time of day to BBH offsets reduced RMSEs in predicted surface melting levels to between 273 and 371 m, an average of 73 m less error than when these corrections were not implemented.
Third, given an existing snowpack, what are the probabilities of snow accumulating versus melting at a given temperature during a precipitation event? Surface measurements indicate that precipitation has an equal likelihood of falling as rain or snow at a surface temperature of 1.5°C, but a preexisting snowpack is 50% likely to melt at a surface temperature of 3°C. Case studies illustrate that rain may be incorporated into the snowpack, pass through the snowpack, or be accompanied by melting snow, depending on antecedent snow temperature and liquid water content.
b. Applications and implications for model development
Accurate forecasts or nowcasts of the altitude where snow changes to rain are crucial for hydrologic forecasting not only in mountain basins in California but also all along the western coast. The current “rule-of-thumb” used by the California–Nevada River Forecast Center (CNRFC) for forecasting the surface melting level is to take the forecasted freezing level (0°C isotherm) and subtract 300 to 460 m (1000 to 1500 ft; E. Strem, CNRFC, 2006, personal communication). This melting level, combined with basin areas above and below, is then used to determine how mean areal precipitation for a basin is partitioned into rain and snow. This paper provides statistical relationships for how BBHs could be used to check and improve these forecast adjustments.
The observations described here illustrate the physical processes that must be included in a model to accurately represent elevation zones of rain versus snow to determine hydrologic runoff in any area with orographic precipitation. First, the model must accurately represent how the atmospheric melting layer elevation and thickness change with space and time, by reproducing large-scale latitudinal gradients and topographically induced changes, such as slope-induced cooling (Medina et al. 2005). Second, the model must include spatial representation of the surface energy balance to account for the effects of radiation on near-surface air temperatures and melting. This could be accomplished most precisely by modeling the thickness and optical properties of the cloud cover (radiation transmission) and the radiative properties (albedo) of the land surface cover. Radiative heating is neglected in most atmospheric models of orographically induced precipitation (Barros and Lettenmaier 1994), but could have important feedback effects on not only the type and fate of hydrometeors but also on the type and location of secondary mesoscale circulation patterns (Szyrmer and Zawadski 1999). Finally, the model needs a spatial accounting of snowpack characteristics, such as density, temperature, and liquid water content (as in the Utah Energy Balance Model; Tarboton and Luce 1996), to determine when and where rain will be incorporated into the snowpack, as opposed to directly contributing to runoff. Collectively, these modeling requirements consist of model components that have already been developed in various separate forms and should be much simpler to implement than those required for accurate quantitative precipitation estimation (QPE; Wang and Georgakakos 2007, manuscript submitted to J. Geophys. Res.) Accurate implementation of the above processes should improve model representations of where precipitation contributes to runoff, and thus would provide immediate benefits to hydrologic forecasting in coastal mountain regions.
In addition to providing ad hoc information for operational forecasts, vertically pointing radars also provide a resource for model testing and development. The California and Oregon coasts are unique in having over 10 yr (since 1996) of vertically pointing Doppler radar measurements, which include BBHs. These could be used to check model representations of atmospheric melting levels as a function of space and time. Most tests of microphysics parameterization schemes are constrained to the short duration of major field campaigns [Sierra Cooperative Pilot Project (SCPP; Meyers and Cotton 1992; Wang and Georgakakos 2007, manuscript submitted to J. Geophys Res.) or the Improvement of Microphysical Parameterization through Observational Verification Experiment (IMPROVE; Colle et al. 2005)] because these are the only times validation data are available. A multiyear investigation comparing model melting levels with BBHs could provide statistics of how well various schemes represent atmospheric melt processes under different meteorological conditions.
Most operational and many scientific hydrology models rely on temperature to identify whether precipitation falls as rain or snow, such as Snow-17 (Anderson 1976), Variable Infiltration Capacity (VIC; Cherkauer et al. 2003), and Distributed Hydrology Soil Vegetation Model (DHSVM; Wigmosta et al. 1994, 2002). The probability distribution functions presented here of likelihood of snow gain or loss as a function of temperature may help to improve such model parameters.
As temperatures continue to warm across the western United States, basins at progressively higher elevations will experience mixed rain and snow during winter precipitation events, impacting both storm runoff and snowpack storage for the following summer. Changes in the frequency of high melting levels represent a changing baseline for hydrologic runoff and will make statistical forecasts based on historic datasets less reliable. The spatial patterns of precipitation type and intensity will influence landslides and ecosystem processes. These applications provide impetus to improve our physical knowledge of processes in this regime, and lessons learned from the relatively lower-elevation American River basin may become crucial for understanding and modeling streamflow in mountain basins across the west.
Acknowledgments
We thank the dedicated engineering staff in the Physical Sciences Division of NOAA/ESRL for deploying and maintaining the NOAA wind profilers used in this study. Funding for this study was provided by a CIRES Postdoctoral Fellowship through NOAA and the University of Colorado, Boulder. Thank you to Socorro Medina for discussions of the paper’s content and to two anonymous reviewers for comments.
REFERENCES
Anderson, E. A., 1976: A point energy and mass balance model of a snow cover. NOAA Tech. Rep. NWS 19, 150 pp.
Barros, A. P., and Lettenmaier D. P. , 1994: Dynamic modeling of orographically induced precipitation. Rev. Geophys., 32 , 265–284.
Battan, L. J., 1973: Radar Observations of the Atmosphere. University of Chicago Press, 279 pp.
Beaumont, R. T., 1965: Mt. Hood pressure pillow snow gage. J. Appl. Meteor., 4 , 626–631.
Brunengo, M. J., 1990: A method of modeling the frequency characteristics of daily snow amount for stochastic simulation of rain-on-snow events. Proc. 88th Western Snow Conf., Sacramento, CA, Western Snow Conference, 110–121.
Cherkauer, K. A., Bowling L. C. , and Lettenmaier D. P. , 2003: Variable infiltration capacity cold land process model updates. Global Planet. Change, 38 , 151–159.
Colle, B. A., Garvert M. F. , Wolfe J. B. , Mass C. F. , and Woods C. P. , 2005: The 13–14 December 2001 IMPROVE-2 event. Part III: Simulated microphysical budgets and sensitivity studies. J. Atmos. Sci., 62 , 3535–3558.
Cunningham, R. M., 1947: A different explanation of the “bright line”. J. Meteor., 4 , 163.
Dettinger, M. D., Cayan D. R. , Meyer M. K. , and Jeton A. E. , 2004: Simulated hydrologic responses to climate variations and change in the Merced, Carson, and American river basins, Sierra Nevada, California, 1900–2099. Climatic Change, 62 , 283–317.
Fabry, F., and Zawadzki I. , 1995: Long-term radar observations of the melting layer of precipitation and their interpretation. J. Atmos. Sci., 52 , 838–851.
Farnes, P. E., 1967: Criteria for determining mountain snow pillow sites. Proc. 35th Western Snow Conf., Boise, ID, Western Snow Conference, 59–62.
Feiccabrino, J., and Lundberg A. , 2008: Precipitation phase discrimination by dew point and air temperature. Proc. 75th Western Snow Conf., Kailua-Kona, HI, Western Snow Conference, 141–146.
Ffolliott, P. F., and Brooks K. N. , 1983: Rain-on-snow flood event in central Arizona. Proc. 51st Western Snow Conf., Vancouver, WA, Western Snow Conference, 130–138.
Hall, R. L., and Hannaford J. F. , 1983: Analysis of three rain-on- snow floods in the Sierra Nevada, California. Proc. 51st Western Snow Conf., Vancouver, WA, Western Snow Conference, 46–54.
Harr, R. D., 1986: Effects of clearcutting on rain-on-snow runoff in western Oregon: A new look at old studies. Water Resour. Res., 22 , 1095–1100.
Jensen, M. E., Burman R. D. , and Allen R. G. , 1990: Evapotranspiration and Irrigation Water Requirements. American Society of Civil Engineers, 332 pp.
Jeton, A. E., Dettinger M. D. , and Smith J. L. , 1996: Potential effects of climate change on streamflow, eastern and western slopes of the Sierra Nevada, California and Nevada. USGS Water Resources Investigations Rep. 95-4260, 44 pp.
Kattelmann, R., 1997: Flooding from rain-on-snow events in the Sierra Nevada. Destructive Water: Water-Caused Natural Disasters, Their Abatement and Control, G. H. Leavesley et al., Eds., IAHS Publication 239, 59–65.
Knowles, N., Dettinger M. D. , and Cayan D. R. , 2006: Trends in snowfall versus rainfall in the western United States. J. Climate, 19 , 4545–4559.
Lettenmaier, D. P., and Gan T. Y. , 1990: Hydrologic sensitivities of the Sacramento-San Joaquin River basin, California, to global warming. Water Resour. Res., 26 , 69–86.
L’Hôte, Y., Chevallier P. , Coudrain A. , Lejeune Y. , and Etchevers P. , 2005: Relationship between precipitation phase and air temperature: Comparison between the Bolivian Andes and the Swiss Alps. Hydrol. Sci. J., 50 , 989–997.
Lin, Y-L., Farley R. D. , and Orville H. D. , 1983: Bulk parameterization of the snow field in a cloud model. J. Climate Appl. Meteor., 22 , 1065–1092.
Marks, D., Kimball J. , Tingey D. , and Link T. , 1998: The sensitivity of snowmelt processes to climate conditions and forest cover during rain-on-snow: A case study of the 1996 Pacific Northwest flood. Hydrol. Processes, 12 , 1569–1587.
Marshall, J. S., Langille R. C. , and Palmer W. Mc K. , 1947: Measurements of rainfall by radar. J. Meteor., 4 , 186–192.
Marwitz, J. D., 1983: The kinematics of orographic airflow during Sierra storms. J. Atmos. Sci., 40 , 1218–1227.
Marwitz, J. D., 1987: Deep orographic storms over the Sierra Nevada. Part I: Thermodynamic and kinematic structure. J. Atmos. Sci., 44 , 159–173.
McCabe, G. J., Clark M. P. , and Hay L. E. , 2007: Rain-on-snow events in the western United States. Bull. Amer. Meteor. Soc., 88 , 319–328.
Medina, S., Smull B. F. , Houze R. A. Jr., and Steiner M. , 2005: Cross-barrier flow during orographic precipitation events: Results from MAP and IMPROVE. J. Atmos. Sci., 62 , 3580–3598.
Meyers, M. P., and Cotton W. R. , 1992: Evaluation of the potential for wintertime quantitative precipitation forecasting over mountainous terrain with an explicit cloud model. Part I: Two-dimensional sensitivity experiments. J. Appl. Meteor., 31 , 26–50.
Mitra, S. K., Vohl O. , Ahr M. , and Pruppacher H. R. , 1990: A wind tunnel and theoretical study of the melting behavior of atmospheric ice particles. IV: Experiment and theory for snow flakes. J. Atmos. Sci., 47 , 584–591.
Mittermaier, M. P., and Illingworth A. J. , 2003: Comparison of model-derived and radar-observed freezing-level heights: Implications for vertical reflectivity profile-correction schemes. Quart. J. Roy. Meteor. Soc., 129 , 83–95.
Osterhuber, R., 1999: Precipitation intensity during rain-on-snow. Proc. 67th Western Snow Conf., South Lake Tahoe, CA, Western Snow Conference, 153–155.
Ralph, F. M., and Coauthors, 2005: Improving short-term (0–48 h) cool-season quantitative precipitation forecasting: Recommendations from a USWRP workshop. Bull. Amer. Meteor. Soc., 86 , 1619–1632.
Rohrer, M., 1989: Determination of the transition air temperature from snow to rain and intensity of precipitation. Instruments and Observing Methods, WMO Tech. Rep. 328, Rep. 48, World Meteorological Organization, 475–482.
Schultz, P., 1995: An explicit cloud physics parameterization for operational numerical weather prediction. Mon. Wea. Rev., 123 , 3331–3343.
Slack, J. R., and Landwehr J. M. , 1992: Hydro-climatic data network (HCDN): A U.S. Geological Survey streamflow data set for the United States for the study of climate variations, 1874–1988. USGS Open-File Rep. 92-129, 193 pp.
Steiner, M., Bousquet O. , Houze R. A. Jr., Smull B. F. , and Mancini M. , 2003: Air flow within major Alpine river valleys under heavy rainfall. Quart. J. Roy. Meteor. Soc., 129 , 411–431.
Stewart, R. E., Marwitz J. D. , Pace J. C. , and Carbone R. E. , 1984: Characteristics through the melting layer of stratiform clouds. J. Atmos. Sci., 41 , 3227–3237.
Storck, P., 2000: Trees, snow and flooding: An investigation of forest canopy effects on snow accumulation and melt at the plot and watershed scales in the Pacific Northwest. Water Resources Series Tech. Rep. 61, Dept. of Civil Engineering, University of Washington, 176 pp.
Szyrmer, W., and Zawadski I. , 1999: Modeling of the melting layer. Part I: Dynamics and microphysics. J. Atmos. Sci., 56 , 3573–3592.
Tarboton, D. G., and Luce C. H. , 1996: Utah Energy Balance snow accumulation and melt model (UEB): Computer model technical description and users guide. Utah Water Research Laboratory and USDA Forest Service Intermountain Research Station, 41 pp. [Available online at http://www.engineering.usu.edu/dtarb/.].
Thom, A. S., 1975: Momentum, mass and heat exchange of plant communities. Vegetation and the Atmosphere, J. L. Monteith, Ed., Vol. 1, Academic Press, 57–109.
Trabant, D. C., and Clagett G. P. , 1990: Measurement and evaluation of snowpacks. Cold Regions Hydrology and Hydraulics, W. L. Ryan and R. D. Crissman, Eds., American Society of Civil Engineers, 39–93.
U.S. Army Corps of Engineers, 1956: Snow hydrology: Summary report of the snow investigations. North Pacific Division, U.S. Army Corps of Engineers, 437 pp.
Wang, J., and Georgakakos K. P. , 2005: Validation and sensitivities of dynamic precipitation simulation for winter events over the Folsom Lake watershed: 1964–99. Mon. Wea. Rev., 133 , 3–19.
Westrick, K. J., and Mass C. F. , 2001: An evaluation of a high-resolution hydrometeorological modeling system for prediction of a cool-season flood event in a coastal mountainous watershed. J. Hydrometeor., 2 , 161–180.
White, A. B., Gottas D. J. , Strem E. T. , Ralph F. M. , and Neiman P. J. , 2002: An automated brightband height detection algorithm for use with Doppler radar spectral moments. J. Atmos. Oceanic Technol., 19 , 687–697.
Wigmosta, M. S., Vail L. W. , and Lettenmaier D. P. , 1994: A distributed hydrology-vegetation model for complex terrain. Water Resour. Res., 30 , 1665–1679.
Wigmosta, M. S., Nijssen B. , Storck P. , and Lettenmaier D. P. , 2002: The distributed hydrology soil vegetation model. Mathematical Models of Small Watershed Hydrology and Applications, V. P. Singh and D. K. Frevert, Eds., Water Resource Publications, 7–42.
Yuter, S. E., Kingsmill D. E. , Nance L. B. , and Löffler-Mang M. , 2006: Observations of precipitation size and fall speed characteristics within coexisting rain and wet snow. J. Appl. Meteor. Climatol., 45 , 1450–1464.
Zuzel, J. F., Greenwalt R. N. , and Allmaras R. R. , 1983: Rain on snow: Shallow, transient snowpacks with frozen soils. Proc. 51st Western Snow Conf., Vancouver, WA, Western Snow Conference, 67–75.
Locations of radar profilers (black circles), surface measurement sites (plus signs), and surface snow and temperature sites (white circled plus signs). KOAK identifies the Oakland sounding location.
Citation: Journal of Hydrometeorology 9, 2; 10.1175/2007JHM853.1
Cumulative fraction of total basin area contributing to runoff as a function of altitude of rain–snow line for the North Fork of the American River, CA.
Citation: Journal of Hydrometeorology 9, 2; 10.1175/2007JHM853.1
Schematic diagram of subjects discussed in this paper.
Citation: Journal of Hydrometeorology 9, 2; 10.1175/2007JHM853.1
(a) Radar brightband heights and (b) NF American River discharge (normalized by basin area on the left axis and actual quantity on the right axis) for winter 2005 (PST). Case study periods are marked by vertical dashed lines.
Citation: Journal of Hydrometeorology 9, 2; 10.1175/2007JHM853.1
Wind profiles and BBHs for the Grass Valley profiler (PST) for (top) case study 1 and (bottom) the last 4 days of case study 2.
Citation: Journal of Hydrometeorology 9, 2; 10.1175/2007JHM853.1
Surface measurements at Blue Canyon (1600 m) and Caple’s Lake (2400 m) between 24 and 30 Jan 2005 (PST) of (a) air temperature, (b) precipitation rates, (c) changes in snow depth, and (d) changes in SWE. To aid in comparison of snow changes, both snow depth and SWE are offset to equal 0 at the start of the analysis period.
Citation: Journal of Hydrometeorology 9, 2; 10.1175/2007JHM853.1
(a) Surface temperatures at seven snow pillow locations, plotted at each location’s elevation between 24 and 30 Jan 2005 (PST). Times with no mark indicate the free-atmosphere transition zone of a mixture of rain and snow with 0° < T < 2°C. Black squares are brightband melting levels detected at Bodega Bay (BBY). Gray circles are brightband levels detected at Grass Valley (GVY). (b) Increases in precipitation and snow depth at three snow pillow locations (Caple’s Lake, Meadow Lake, and Blue Canyon). Precipitation at Meadow Lake is inferred from records at Alpha, the station nearest in elevation. Black triangles (snow) indicate increases in snow depth, and gray upside-down triangles (rain) indicate increases in precipitation. Brightband heights are the same as in (a).
Citation: Journal of Hydrometeorology 9, 2; 10.1175/2007JHM853.1
Surface data for Blue Canyon and Caple’s Lake, as in Fig. 6, but for 18 to 24 Mar 2005 (PST). Note changed limits for each axis from Fig. 6 (smaller temperature range and increased precipitation and depth ranges).
Citation: Journal of Hydrometeorology 9, 2; 10.1175/2007JHM853.1
(a) Surface temperatures and (b) forms of surface precipitation compared to radar-measured melting levels, as in Fig. 7, but for 18–24 Mar 2005 (PST).
Citation: Journal of Hydrometeorology 9, 2; 10.1175/2007JHM853.1
The probability of (a) fractional SWE gain or loss during 12-h periods with precipitation for the 10 American River basin snow pillow stations marked with an asterisk in Table 3 for the five winters from 2001 to 2005. Contours show probability of distributions at each temperature, and the heavy dashed line shows the mean. (b) Percent of occurrences with 100% of precipitation contributing to SWE gain (ΔSWE/Δprecip ≥ 1, dashed line, left axis), only part of precipitation contributing to SWE gain (0 ≥ ΔSWE/Δprecip < 1, width of region between dashed and solid lines), and SWE loss (ΔSWE/Δprecip < 0, solid line, right axis). (c) Percent of occurrences of snow (dashed line, left axis), mixed rain and snow (width of region between dashed and solid lines), and rain (solid line, right axis) observed falling from the sky at Donner Summit. [Note: (c) is based on data collected by the U.S. Army Corps of Engineers (1956)]
Citation: Journal of Hydrometeorology 9, 2; 10.1175/2007JHM853.1
(a) Mean and (b) standard deviation of American River basin surface temperatures at the elevation closest to the measured BBH as a function of hour of day (PST).
Citation: Journal of Hydrometeorology 9, 2; 10.1175/2007JHM853.1
(a) Profile of temperatures observed at Oakland sounding compared with observations of melting level at Bodega Bay (black squares) and Grass Valley (white circles) for 18 to 25 Mar 2005, in local time. Heavy black contour represents 0°C, and each contour line represents a change of 1°C, with lighter shades indicating warmer and darker shades indicating colder temperatures. (b) As in (a), but for surface temperatures in the American River basin. (c) Difference between surface and free-air temperatures shown in (b) and (a). Positive values indicate warmer surface temperatures.
Citation: Journal of Hydrometeorology 9, 2; 10.1175/2007JHM853.1
(a) Mean and (b) std dev of the height difference in meters between the altitude of the 1.5°C contour on the surface of the American River basin and the radar-measured brightband height.
Citation: Journal of Hydrometeorology 9, 2; 10.1175/2007JHM853.1
Summary of significant heights and temperatures.
Radar profilers.
American River surface sites and types of measurements available. Note that Meadow Lake is adjacent to, but not within, the American River basin.
RMSE (m) resulting from assuming each station’s brightband height corresponds to a given surface temperature contour; 1.5°C represents the point where snow switches to rain, and 3°C represents the point where an existing snowpack melts.
