On distinguishing snowfall from rainfall using near‐surface atmospheric information: Comparative analysis, uncertainties and hydrologic importance

Abstract

The accurate estimation of precipitation phase has broad applications. In this study, we compared the skill of using various atmospheric variables and their combinations as predictors in accurately identifying surface precipitation phase, determined uncertainties associated with commonly used fixed temperature thresholds, and explored the sensitivity of hydrologic model output to uncertainty in precipitation phase using two case‐studies. The results suggest that among all single predictors, wet‐bulb temperature yields the highest skill score for determining precipitation phase and can reduce uncertainties due to regional differences, especially compared to the commonly used near‐surface air temperature. However, addition of good‐quality near‐surface wind speed measurement to dew‐point temperature and pressure showed slightly higher skill than wet‐bulb temperature. We showed that the scale mismatch between temperature from stations and gridded products can cause large uncertainties in determining precipitation phase, especially in regions with rugged topography. Such uncertainties need to be considered when the relationships developed based on station data are applied to remote‐sensing observations and model‐generated data to separate rain from snowfall. The sensitivity of hydrologic model outputs to uncertainty in precipitation phase delineation was also assessed over two major basins in California by modifying default near‐surface temperatures used in the Variable Infiltration Capacity (VIC) model. It was found that regional and scaling uncertainties in determining temperature thresholds can largely influence the accuracy of simulated downstream runoff and snow water equivalent (SWE) (e.g. up to 40% change in SWE for 2 °C shift in temperature threshold). Therefore, to reduce simulation uncertainties, it is important to improve rain–snow partitioning methods, consider regional variabilities in determining temperature thresholds, and perform the analysis at the highest possible resolutions to mitigate scale‐related uncertainties.

A Wet‐Bulb Temperature‐Based Rain‐Snow Partitioning Scheme Improves Snowpack Prediction Over the Drier Western United States

“…We developed a new snowfall fraction scheme as a sigmoid function of T w , which is derived from the T w depression equation, to fit the observation-based relationship between snowfall probability and T w of Behrangi et al (2018). In reality, a falling hydrometeor may evaporate or sublimate at its surface and thus cool off relative to the ambient air humidity of its falling path, resulting in a hydrometeor surface temperature that is cooler than the air temperature.…”

“…The depressed surface temperature is close to the wet-bulb temperature (T w ). We developed a new snowfall fraction scheme as a sigmoid function of T w , which is derived from the T w depression equation, to fit the observation-based relationship between snowfall probability and T w of Behrangi et al (2018). We tested the new scheme with the physically based snow model embedded in Noah-MP over CONUS against the high-quality UA snow data product.…”

“…T w is closer to the surface temperature of a falling hydrometeor than T a , representing the cooling effects due to surface evaporation at a constant pressure (Rogers & Yau, 1989) and thus may improve the prediction skill in distinguishing the precipitation phase (Behrangi et al, 2018;Ding et al, 2014). Behrangi et al (2018) suggest that using T w results in the highest prediction skill in the partitioning of precipitation across regions among precipitation partitioning methods that use a single atmospheric variable over global snow-covered regions.…”

“…An increasing number of studies recommended that wet-bulb temperature (T w ) should be a better discriminator for the rain-snow partitioning (Behrangi et al, 2018;Ding et al, 2014;Harder & Pomeroy, 2014;Marks et al, 2013;Olsen, 2003;Sims & Liu, 2015;Yamazaki, 2001;Zhang et al, 2011) rather than other variables such as dew point temperature (Behrangi et al, 2018;Marks et al, 2013) or surface air pressure (Dai, 2008). T w is closer to the surface temperature of a falling hydrometeor than T a , representing the cooling effects due to surface evaporation at a constant pressure (Rogers & Yau, 1989) and thus may improve the prediction skill in distinguishing the precipitation phase (Behrangi et al, 2018;Ding et al, 2014). Behrangi et al (2018) suggest that using T w results in the highest prediction skill in the partitioning of precipitation across regions among precipitation partitioning methods that use a single atmospheric variable over global snow-covered regions.…”

“…The T dry scheme assumes that any precipitation is snowfall when T a < 0.5°C, rainfall when T a > 2.5°C, a snowfall fraction (f s ) of 0.6 when 2.0°C < T a ≤ 2.5°C, and a linear transition from snowfall to rainfall between 0.5°C < T a < 2.0°C (Figure 1a). In this study, we developed a new f s scheme using a sigmoid function (e.g., Froidurot et al, 2014;Jennings et al, 2018; hereafter T wet scheme) to fit the observation-based relationship between snowfall probability and T w of Behrangi et al (2018; Figure 1b), which are derived from precipitation type data and other atmospheric variables over 547 weather stations located poleward of 35°N/S. This f s scheme represents the mean probability of snowfall occurrence as a function of T w [K]:…”