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

Abstract

Diagnosing the source of errors in snow models requires intensive observations, a flexible model framework to test competing hypotheses, and a methodology to systematically test the dominant snow processes. We present a novel process‐based approach to diagnose model errors through an example that focuses on snow accumulation processes (precipitation partitioning, new snow density, and snow compaction). Twelve years of meteorological and snow board measurements were used to identify the main source of model error on each snow accumulation day. Results show that modeled values of new snow density were outside observational uncertainties in 52% of days available for evaluation, while precipitation partitioning and compaction were in error 45% and 16% of the time, respectively. Precipitation partitioning errors mattered more for total winter accumulation during the anomalously warm winter of 2014–2015, when a higher fraction of precipitation fell within the temperature range where partition methods had the largest error. These results demonstrate how isolating individual model processes can identify the primary source(s) of model error, which helps prioritize future research.

Regional Snow Parameters Estimation for Large‐Domain Hydrological Applications in the Western United States

“…In a warm maritime climate such as the Pacific Northwest, generally lower SWE model skills can be attributed to two causes. Greater sensitivity to errors associated with the temperature‐only precipitation partitioning method (Harpold, Kaplan, et al, ; Jennings et al, ; Wayand et al, ). Improvement can be made by incorporating wet bulb temperature, dew point temperature, or relative humidity into the partitioning method (Ding et al, ; Harpold, Rajagopal, et al, ; Jennings et al, ; Marks et al, ). Greater sensitivity to errors in model simulated energy balances (Essery et al, ; Lapo et al, ).…”

“…1. Greater sensitivity to errors associated with the temperature-only precipitation partitioning method (Harpold, Kaplan, et al, 2017;Jennings et al, 2018;Wayand et al, 2016). Improvement can be made by incorporating wet bulb temperature, dew point temperature, or relative humidity into the partitioning method (Ding et al, 2014;Jennings et al, 2018;Marks et al, 2013).…”

“…Recent studies (Chen et al, ; Essery et al, ; Etchevers et al, ; Feng et al, ; He et al, ; Houle et al, ; Koivusalo & Heikinheimo, ; Mendoza et al, ; Wayand et al, ) have explored the impact of model complexity on model performance and found that the model performance can be very dependent on model application. Feng et al () compared the performance of five physics‐based Land Surface Models (LSMs) with different physical complexities of snow processes on snow simulations at three experimental sites in north‐central Colorado.…”

Snowpack Contributions and Temperature Characterization of Landfalling Atmospheric Rivers in the Western Cordillera of the United States

“…In this work, ARs are identified by integrated water vapor transport derived from integrating specific humidity and total vector wind values from the surface to 500-hPa height (Rutz et al, 2014). As one of the largest sources of error in modeling accumulation (Harder & Pomeroy, 2014;Wayand et al, 2017), incorrect representation of precipitation phase can lead to errors in oft-used modeled snow metrics such as SWE, snow depth, and snow cover duration (Jennings et al, 2018;Maurer & Mass, 2006), which may propagate into snowmelt and streamflow forecasts (Harpold et al, 2017). Our results demonstrate that even probability-based temperature index models may considerably underestimate SWE, given that some of the largest accumulation days-AR days-are also the warmest.…”

“…Our results demonstrate that even probability-based temperature index models may considerably underestimate SWE, given that some of the largest accumulation days-AR days-are also the warmest. As one of the largest sources of error in modeling accumulation (Harder & Pomeroy, 2014;Wayand et al, 2017), incorrect representation of precipitation phase can lead to errors in oft-used modeled snow metrics such as SWE, snow depth, and snow cover duration (Jennings et al, 2018;Maurer & Mass, 2006), which may propagate into snowmelt and streamflow forecasts (Harpold et al, 2017).…”