Jorge Arévalo, Josh Welty, and Xubin Zeng
The CPC Leaky Bucket Model is used in the Climate Prediction Center (CPC) for monitoring and forecasting soil moisture (SM) anomalies for the assessment of drought status at a global scale. A new layer (besides SM) has been added to this model recently to account for Snow (actually Snow Water Equivalent, SWE) as a way to improve the SM representation in snow covered areas. Here we present an improved parameterization for the snow treatment, accounting for accumulation and ablation based in the same forcing variables as in the original model, i.e. Temperature and Precipitation. Results for the SWE prognostic variable are compared with the UA-Snow dataset (Broxton et al., 2016) and observations showing an overall good fitting. This led into an improved seasonal cycle of SM closer to observed data, with a more realistic timing for the SM peaks in areas influenced by snow.