Rain versus Snow in the Sierra Nevada, California: Comparing Doppler Profiling Radar and Surface Observations of Melting Level

Abstract

The maritime mountain ranges of western North America span a wide range of elevations and are extremely sensitive to flooding from warm winter storms, primarily because rain falls at higher elevations and over a much greater fraction of a basin’s contributing area than during a typical storm. Accurate predictions of this rain–snow line are crucial to hydrologic forecasting. This study examines how remotely sensed atmospheric snow levels measured upstream of a mountain range (specifically, the bright band measured above radar wind profilers) can be used to accurately portray the altitude of the surface transition from snow to rain along the mountain’s windward slopes, focusing on measurements in the Sierra Nevada, California, from 2001 to 2005. Snow accumulation varies with respect to surface temperature, diurnal cycles in solar radiation, and fluctuations in the free-tropospheric melting level. 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 snow water equivalent (SWE). Between 2.5° and 3°C, snow is equally likely to melt or accumulate, with most cases resulting in no change to SWE. Qualitatively, brightband heights (BBHs) detected by 915-MHz profiling radars up to 300 km away from the American River study basin agree well with surface melting patterns. Quantitatively, this agreement can be improved by adjusting the melting elevation based on the spatial location of the profiler relative to the basin: BBHs decrease with increasing latitude and decreasing distance to the windward slope of the Sierra Nevada. Because of diurnal heating and cooling by radiation at the mountain surface, BBHs should also be adjusted to higher surface elevations near midday and lower elevations near midnight.

Insights into mountain precipitation and snowpack from a basin‐scale wireless‐sensor network

“…A melting-level error of 500 m can result in a 200% difference in peak flow prediction [White et al, 2002]. Previous studies interpolated ground melting elevation from atmosphere hydrometeor measurements using Doppler-profiling radar [Lundquist et al, 2008;Minder and Kingsmill, 2012]. However, the uncertainty in estimates made from these methods were at best about 300 m in the American River basin.…”

“…Therefore, using a dew-point temperature-based method to determine the phase of precipitation is generally less geographically dependent [Ye et al, 2013]. It has also been observed that using ground-based dew-point temperatures to determine the phase of precipitation is potentially more accurate than radar-based methods due to reduction of error associate to interpreting the radar measurement [Lundquist et al, 2008].…”

Meteorological Knowledge Useful for the Improvement of Snow Rain Separation in Surface Based Models

“…His equations account for: (1) Heat exchange resulting from gas, liquid, or solid phase change; (2) Change in particle mass by condensation; (3) The resulting latent heat flux from condensation; (4) Heat exchange from collision coalescence or through accretion of liquid and ice crystals; and (5) Latent heat of fusion with particles melting or freezing due to contact with each other. Detailed examples of more complicated microphysical schemes are found in e.g., Thériault and Stewart [26] or Lundquist [28].…”

“…All surface-based PPDS approaches assume a constant decrease in air temperature with height (e.g., the Climate High Resolution Model (CHRM)-7.5 °C/km) [16] and do not account for interactions either between warm and cold air masses found in the most basic mid-latitude cyclone theories, or between hydrometeors and the air as precipitation falls through the lower atmosphere [28]. Therefore, without atmospheric measurements, the most physically based surface PPDS methods can not accurately reproduce physical processes occurring between hydrometeors and the atmosphere they fall through.…”

Now You See It Now You Don’t: A Case Study of Ephemeral Snowpacks in the Great Basin U.S.A.

“…However, the underlying phase prediction method and related model decisions and climate forcing data can also be important for the quality of precipitation 10 phase prediction (Harpold et al, 2017). Further complicating rain-snow transition mechanisms is storage or drainage of liquid water in the snowpacks (Lundquist et al, 2008;Marks et al, 2001). Although SNODAS assimilates MODIS imagery into the model, it does not appear to capture the finer elevation patterns we found using the MOD10A product (Fig.…”