A Parameterization of the Probability of Snow–Rain Transition

Abstract

Abstract When estimating precipitation using remotely sensed observations, it is important to correctly classify the phase of precipitation. A misclassification can result in order-of-magnitude errors in the estimated precipitation rate. Using global ground-based observations over multiple years, the influence of different geophysical parameters on precipitation phase is investigated, with the goal of obtaining an improved method for determining precipitation phase. The parameters studied are near-surface air temperature, atmospheric moisture, low-level vertical temperature lapse rate, surface skin temperature, surface pressure, and land cover type. To combine the effects of temperature and moisture, wet-bulb temperature, instead of air temperature, is used as a key parameter for separating solid and liquid precipitation. Results show that in addition to wet-bulb temperature, vertical temperature lapse rate affects the precipitation phase. For example, at a near-surface wet-bulb temperature of 0°C, a lapse rate of 6°C km−1 results in an 86% conditional probability of solid precipitation, while a lapse rate of −2°C km−1 results in a 45% probability. For near-surface wet-bulb temperatures less than 0°C, skin temperature affects precipitation phase, although the effect appears to be minor. Results also show that surface pressure appears to influence precipitation phase in some cases; however, this dependence is not clear on a global scale. Land cover type does not appear to affect precipitation phase. Based on these findings, a parameterization scheme has been developed that accepts available meteorological data as input and returns the conditional probability of solid precipitation.