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

Abstract

An accurate precipitation phase determination-i.e., solid versus liquid-is of paramount importance in a number of hydrological, ecological, safety and climatic applications. Precipitation phase can be determined by hydrological, meteorological or combined approaches. Meteorological approaches require atmospheric data that is not often utilized in the primarily surface based hydrological or ecological models. Many surface based models assign precipitation phase from surface temperature dependent snow fractions, which assume that atmospheric conditions acting on hydrometeors falling through the lower atmosphere are invariant. This ignores differences in phase change probability caused by air mass boundaries which can introduce a warm air layer over cold air leading to more atmospheric melt energy than expected for a given surface temperature, differences in snow grain-size or precipitation rate which increases the magnitude of latent heat exchange between the hydrometers and atmosphere required to melt the snow resulting in snow at warmer temperatures, or earth surface properties near a surface observation point heating or cooling a shallow layer of air allowing rain at cooler temperatures or snow at warmer OPEN ACCESSHydrology 2015, 2 267 temperatures. These and other conditions can be observed or inferred from surface observations, and should therefore be used to improve precipitation phase determination in surface models.

Relative Humidity Has Uneven Effects on Shifts From Snow to Rain Over the Western U.S.

“…The accuracy of the T RH method depends on the observational time interval (Harder & Pomeroy, ), atmospheric pressure (Dai, ), and atmospheric temperature and humidity profiles (Wayand et al, ). The simplicity of equation is consistent with the PPMs used in hydrological models, but it has seen limited verification (Feiccabrino et al, ).…”

“…Empirically calibrated temperature‐only PPMs that rely on relating global temperature and phase observations will have difficulties when applied to areas with diverse hydroclimates, particularly those with near‐surface humidity regimes different from the global average. While atmospheric models offer more complete process representation, their computational requirements limit pairing them with hydrological models and they remain sensitive to the microphysics scheme (Feiccabrino et al, ). Simple PPM like equation may improve hydrological prediction using existing gridded climate observations with limited computational requirements.…”

“…The same is true for best fit estimates of probability of snow based on regression to a global precipitation phase versus air temperature data set (hereafter referred to as the Dai method based on Dai ()). The Dai method empirically shows temperatures above 0°C often lead to snowfall because phase change depends on factors other than temperature, including humidity, air pressure, and the vertical temperature profile in the atmosphere (Feiccabrino et al, ). Consequently, many PPMs calibrate temperature thresholds to minimize error compared to local observations, which can lead to widely varying temperature thresholds (Rajagopal & Harpold, ).…”

“…Simple humidity‐dependent threshold temperature PPMS can either incorporate humidity implicitly through empirical relationships (e.g., (Gjertsen & Ødegaard, ) or explicitly using physical processes (e.g., (Harder & Pomeroy, ). However, three common approaches yield similar linear relationships for air temperatures between 0.5 and 5°C (Gjertsen & Ødegaard, ; Harder & Pomeroy, ; Matsuo et al, ) that can be used to correct the threshold temperature for the effects of relative humidity ( T RH ) (Feiccabrino et al, ): …”

Diagnosing snow accumulation errors in a rain‐snow transitional environment with snow board observations

“…The most common method of predicting precipitation phase uses ground measurements of air temperature (T a ) (USACE, 1956;Auer, 1974), dew point temperature (T d ) (Marks, Winstral, Reba, Pomeroy, & Kumar, 2013), or wet bulb temperature (T wet ) (Harder & Pomeroy, 2013;Marks et al, 2013), and sometimes upper-air observations (Sims & Liu, 2015;Wayand, Clark, & Lundquist, 2016c), which are applied at subdaily or daily time scales. See Feiccabrino, Graff, Lundberg, Sandström, and Gustafsson (2015) for an in-depth review of existing methods. For this study, we use the wet-bulb temperature form based on the theory that it best represents the temperature of a falling hydrometer (Marks et al, 2013).…”