Yang et al. (2025) 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

⚠️ Warning: This summary was generated from the abstract only, as the full text was not available.

Identification

• Journal: Journal of Hydrometeorology
• Year: 2025
• Date: 2025-09-12
• Authors: Zhiqi Yang, Weiming Hu, Agniv Sengupta, Luca Delle Monache, Michael J. DeFlorio, Mohammadvaghef Ghazvinian, Mu Xiao, Ming Pan, Jacob Kollen, Andrew Reising, Angelique Fabbiani-Leon, David Rizzardo, Julie Kalansky
• DOI: 10.1175/jhm-d-25-0012.1

Research Groups

California Department of Water Resources (Bulletin 120) California Nevada River Forecast Center (CNRFC)

Short Summary

This study applies analog ensemble (AnEn) postprocessing to improve subseasonal 2-meter temperature (T2m) forecasts in the Sierra Nevada during the spring snowmelt season, demonstrating significant accuracy enhancements, particularly at higher elevations, compared to dynamical benchmarks and basic bias correction methods.

Objective

• To improve the accuracy of fine-resolution (4 km) daily subseasonal 2-meter temperature (T2m) forecasts in the complex terrain of the Sierra Nevada during the spring snowmelt season (April–July) using analog ensemble (AnEn) postprocessing.

Study Configuration

• Spatial Scale: Sierra Nevada region; 4 km spatial resolution for T2m forecasts.
• Temporal Scale: Daily forecasts; subseasonal lead times (up to 15 days); spring snowmelt season (April–July).

Methodology and Data

• Models used: Analog ensemble (AnEn) postprocessing; NOAA Global Ensemble Forecast System (GEFS) reforecasts (as input for AnEn); Ensemble Model Output Statistics (EMOS) for comparison.
• Data sources: Parameter-Elevation Regressions on Independent Slopes Model (PRISM) dataset (ground truth); NOAA Global Ensemble Forecast System (GEFS) reforecasts.

Main Results

• During the spring snowmelt season (April–July), AnEn reduced T2m forecast root-mean-square error (RMSE) by 1 °C (60% for 1-day lead, 20% for 15-day lead) and increased correlation by approximately 11%.
• AnEn extended forecast skill by an additional week beyond dynamical benchmarks.
• Improvements were more pronounced at higher elevations (e.g., 3000–3500 m), where RMSE was reduced by 4 °C, correlation rose from 0.1 to 0.9, and skill was extended by 2 weeks.
• AnEn demonstrated added value beyond a basic bias correction method (Ensemble Model Output Statistics).

Contributions

• Provides a novel application of analog ensemble (AnEn) postprocessing for fine-resolution subseasonal temperature forecasting in complex mountainous terrain, an area with limited prior research.
• Significantly enhances the accuracy of 2-meter temperature forecasts in the Sierra Nevada, particularly at high elevations crucial for snowmelt and runoff.
• Offers a method to improve the precision of snowmelt and streamflow predictions for operational systems like Bulletin 120 and CNRFC-HEFS, supporting improved water resource management.

Funding

Not specified in the abstract.

Citation

@article{Yang2025Improving,
  author = {Yang, Zhiqi and Hu, Weiming and Sengupta, Agniv and Monache, Luca Delle and DeFlorio, Michael J. and Ghazvinian, Mohammadvaghef and Xiao, Mu and Pan, Ming and Kollen, Jacob and Reising, Andrew and Fabbiani-Leon, Angelique and Rizzardo, David and Kalansky, Julie},
  title = {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},
  journal = {Journal of Hydrometeorology},
  year = {2025},
  doi = {10.1175/jhm-d-25-0012.1},
  url = {https://doi.org/10.1175/jhm-d-25-0012.1}
}