Validation of Snow Cover Fraction for Regional Climate Simulations in the Sierra Nevada
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Series/Report no.:2014 Edward F. Hayes Graduate Research Forum. 28th
Mountain snow cover plays an important role in regional climate due to its high albedo, its effects on atmospheric convection, and its influence on lower-elevation runoff. Snowpack water storage is also a critical water resource and understanding how it varies is of great social value. Unfortunately, in situ measurements of snow cover are not widespread; therefore, models are often depended on to assess snowpack and snow cover variability. Here, we use a new satellite-derived snow product to evaluate the ability of the Weather Research and Forecasting (WRF) regional climate model with the Noah land surface model with multiparameterization options (Noah-MP) to simulate snow cover fraction (SCF) and snow water equivalent (SWE) on a 3 km domain over the central Sierra Nevada. WRF/Noah-MP SWE simulations improve upon previous versions of the Noah land surface model by removing the early bias in snow melt. As a result, WRF/Noah-MP now accurately simulates spatial variations in SWE. Additionally, WRF/Noah-MP correctly identifies the areas where snow is present and captures large-scale variability in SCF. Temporal RMSE of the domain-average SCF was 1863.9 km2 (24%). However, our study reveals that WRF/Noah-MP struggles to simulate SCF at the scale of individual grid cells. The equation used to calculate SCF fails to produce temporal variations in grid-scale SCF and depletion occurs too rapidly. Therefore SCF is a nearly binary metric in 2 mountain environments. Sensitivity tests of the equation may improve simulation of SCF during accumulation or melt but does not remove the bias for the entire snow season. Though WRF/Noah-MP accurately simulates the presence or absence of snow, high-resolution, reliable SCF measurements may only be attainable if snow depletion equations are designed specifically for complex topographical areas.
Mathematical and Physical Sciences: 2nd Place (The Ohio State University Edward F. Hayes Graduate Research Forum)
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