CW3E's West-WRF 200-member Ensemble

TL;DR

This paper presents a 200-member ensemble forecast system for the Western US, demonstrating improved precipitation prediction skill over existing global systems, with detailed analysis of its performance and sensitivity.

Contribution

Development and evaluation of a high-resolution, multi-physics ensemble forecast system tailored for atmospheric rivers and heavy precipitation in the Western US.

Findings

WW200En outperforms GEFS in precipitation forecast skill across thresholds.

The ensemble shows better spread-skill relationship and reliability than global systems.

Sensitivity analysis offers insights for future improvements of the ensemble.

Abstract

A 200-member ensemble developed at the Center for Western Weather and Water Extremes based on the Weather Research and Forecast atmospheric model tailored for the prediction of atmospheric rivers and associated heavy-to-extreme precipitation events over the Western US (West-WRF) is presented. The ensemble (WW200En) is generated with initial and boundary conditions from the US National Center for Environmental Prediction's Global Ensemble Forecast System (GEFS) and the European Centre for Medium-Range Weather Forecasts' Ensemble Prediction System (EPS), 100 unique combinations of microphysics, planetary boundary layer, and cumulus schemes, as well as perturbations applied to each of the 200 members based on the stochastic kinetic-energy backscatter scheme. Each member is run with 9-km horizontal increments and 60 vertical levels for a 10-month period spanning two winters. The performance of WW200En is compared to GEFS and EPS for probabilistic forecasts of 24-h precipitation, integrated water vapor transport (IVT), and for several thresholds including high percentiles of the observed climatological distribution. The WW200En precipitation forecast skill is better than GEFS at nearly all thresholds and lead times, and comparable or better than the EPS. For larger rainfall thresholds WW200En typically exhibits the best forecast skill. Additionally, WW200En has a better spread-skill relationship than the global systems, and an improved overall reliability and resolution of the probabilistic prediction. The results for IVT are qualitatively similar to those for precipitation forecasts. A sensitivity analysis of the physics parameterizations and the number of ensemble members provides insights into possible future developments of WW200En.