Toward Unifying Short-term and Next-day CONVECTION-ALLOWING Ensemble Forecast Systems With A Continuously Cycling 3-km Ensemble Kalman Filter Over The Entire Conterminous United States

Abstract

Using the Weather Research and Forecasting Model, 80-member ensemble Kalman filter (EnKF) analyses with 3-km horizontal grid spacing were produced over the entire conterminous United States (CONUS) for 4 weeks using 1-h continuous cycling. For comparison, similarly configured EnKF analyses with 15-km horizontal grid spacing were also produced. At 0000 UTC, 15- and 3-km EnKF analyses initialized 36-h, 3-km, 10-member ensemble forecasts that were verified with a focus on precipitation. Additionally, forecasts were initialized from operational Global Ensemble Forecast System (GEFS) initial conditions (ICs) and experimental “blended” ICs produced by combining large scales from GEFS ICs with small scales from EnKF analyses using a low-pass filter. The EnKFs had stable climates with generally small biases, and precipitation forecasts initialized from 3-km EnKF analyses were more skillful and reliable than those initialized from downscaled GEFS and 15-km EnKF ICs through 12–18 and 6–12 h, respectively. Conversely, after 18 h, GEFS-initialized precipitation forecasts were better than EnKF-initialized precipitation forecasts. Blended 3-km ICs reflected the respective strengths of both GEFS and high-resolution EnKF ICs and yielded the best performance considering all times: blended 3-km ICs led to short-term forecasts with similar or better skill and reliability than those initialized from unblended 3-km EnKF analyses and ~18–36-h forecasts possessing comparable quality as GEFS-initialized forecasts. This work likely represents the first time a convection-allowing EnKF has been continuously cycled over a region as large as the entire CONUS, and results suggest blending high-resolution EnKF analyses with low-resolution global fields can potentially unify short-term and next-day convection-allowing ensemble forecast systems under a common framework.