Improving Weeks 1–2 Temperature Forecasts in the Sierra Nevada Region Using Analog Ensemble Postprocessing with Implications for Better Prediction of Snowmelt, Water Storage, and Streamflow

Abstract California relies on the Sierra Nevada spring snowmelt for 60% of its water, serving 23 million people. Forecasting this snowmelt is vital for water supply planning and is a key task of the California Department of Water Resources’ Bulletin 120. Accurate predictions rely on subseasonal 2-m temperature (T2m) forecasts, especially at high elevations where snowpack and runoff contributions are greatest. Current systems like the California Nevada River Forecast Center’s (CNRFC) Hydrologic Ensemble Forecast Service (HEFS) have identified T2m forecasts as a key uncertainty source. However, research on fine-resolution subseasonal temperature forecasting in complex terrain is limited. This study uses the Parameter-Elevation Regressions on Independent Slopes Model (PRISM) dataset as ground truth and National Oceanic and Atmospheric Administration (NOAA) Global Ensemble Forecast System (GEFS) reforecasts to apply analog ensemble (AnEn) postprocessing, producing high-resolution (4 km) daily T2m forecasts for the Sierra Nevada. We find that during the spring snowmelt season (April–July), AnEn reduces T2m forecast root-mean-square error by 1°C (60% for 1-day lead, 20% for 15-day lead), increases correlation by ∼11%, extends skill by an additional week beyond dynamical benchmarks, and illustrates added value beyond a basic bias correction method (the ensemble model output statistics). Improvements are more pronounced at higher elevations (e.g., 3000–3500 m), with root-mean-square error reduced by 4°C, correlation rising from 0.1 to 0.9, and skill extended by 2 weeks. By enhancing T2m accuracy for Bulletin 120 and CNRFC-HEFS systems, AnEn can boost the precision of snowmelt and streamflow predictions, supporting improved water resource management in a changing climate. Significance Statement California relies on the Sierra Nevada spring snowmelt for 60% of its water, serving 23 million people. To better manage water resources and predict spring snowmelt, it is important to forecast temperatures in the mountains accurately. This study improves week-1 and week-2 daily forecasts of 2-m temperature over the Sierra Nevada during the snowmelt season (April–July) using the analog ensemble (AnEn) method. Using 4-km spatial resolution temperature data from the Parameter-Elevation Regressions on Independent Slopes Model (PRISM) dataset as ground truth and Global Ensemble Forecast System (GEFS) reforecast data as a reference, the study creates daily temperature forecasts at a high spatial resolution (4 km) for the region. The results show that AnEn significantly improves forecasts by better capturing temperature patterns across different elevations and providing more detailed predictions over time. AnEn also illustrates added value beyond the basic bias correction method, ensemble model output statistics.