Short Term Temperature Forecast Correction in the Sierra Nevada with Analog Ensemble Method for Snowmelt Streamflow and Water Storage Prediction

Accurate short-term temperature forecasts are important in snow-dominated mountain regions because small errors can strongly affect snowmelt, runoff, and water storage. This study applied analog ensemble post-processing to improve temperature forecasts in the Sierra Nevada, which provides major water supply for the western United States. Historical forecast–observation pairs were used to build analog ensembles, and the corrected forecasts were applied to a degree-day snowmelt model and a hydrological model. The corrected forecasts reduced mean bias from +1.2 °C to +0.5 °C and lowered root mean square error by about 18 % compared with raw forecasts. Snowmelt onset was predicted within two days of observation, while the control forecasts were five days early, and peak melt overestimation fell from 20 % to 7 %. Streamflow simulations using corrected forecasts also improved, with Nash–Sutcliffe efficiency increasing from 0.61 to 0.77 and peak flow error decreasing from 22 % to 8 %. Reservoir storage curves showed better agreement with observed filling and release cycles. These results show that correcting temperature forecasts improves hydrological prediction during the spring melt season. The study highlights the value of analog ensemble methods for water management, including reservoir operation, flood control, and drought planning, but also notes that further tests are needed under different climate conditions and in other regions.