Short-Range Ensemble Forecasting of Quantitative Precipitation

Abstract

The impact of initial condition uncertainty (ICU) on quantitative precipitation forecasts (QPFs) is examined for a case of explosive cyclogenesis that occurred over the contiguous United States and produced widespread, substantial rainfall. The Pennsylvania State University-National Center for Atmospheric Research (NCAR) Mesoscale Model Version 4 (MM4), a limited-area model, is run at 80-km horizontal resolution and 15 layers to produce a 25-member, 36-h forecast ensemble. Lateral boundary conditions for MM4 are provided by ensemble forecasts from a global spectral model, the NCAR Community Climate Model Version 1 (CCM1). The initial perturbations of the ensemble members possess a magnitude and spatial decomposition that closely match estimates of global analysis error, but they are not dynamically conditioned. Results for the 80-km ensemble forecast are compared to forecasts from the then operational Nested Grid Model (NGM), a single 40-km/15layer MM4 forecast, a single 80-km/29-layer MM4 forecast, and a second 25-member MM4 ensemble based on a different cumulus parameterization and slightly different unperturbed initial conditions. Large sensitivity to ICU marks ensemble QPF. Extrema in 6-h accumulations at individual grid points vary by as much as 3.00Љ. Ensemble averaging reduces the root-mean-square error (rmse) for QPF. Nearly 90% of the improvement is obtainable using ensemble sizes as small as 8-10. Ensemble averaging can adversely affect the bias and equitable threat scores, however, because of its smoothing nature. Probabilistic forecasts for five mutually exclusive, completely exhaustive categories are found to be skillful relative to a climatological forecast. Ensemble sizes of approximately 10 can account for 90% of improvement in categorical forecasts relative to that for the average of individual forecasts. The improvements due to short-range ensemble forecasting (SREF) techniques exceed any due to doubling the resolution, and the error growth due to ICU greatly exceeds that due to different resolutions. If the authors' results are representative, they indicate that SREF can now provide useful QPF guidance and increase the accuracy of QPF when used with current analysis-forecast systems.

Evaluation of a Short-Range Multimodel Ensemble System

“…Prior studies (Du et al 1997;Hamill and Colucci 1998;Stensrud et al 1999) found that skill for a short-range ensemble was comparable to or greater than skill for a single run of the higher-resolution model. Here we compare the performance of the 80-km ensembles and 29-km Meso-Eta Model for 24-h PQPFs.…”

“…There is reason to expect that smaller-scale features, precipitation, and mechanisms that strongly modulate precipitation may have shorter predictability limits than the synoptic and planetary fields that are often examined in this context (e.g., Lorenz 1982;Baumhefner 1984;Anthes 1986;Stamus et al 1992;Mullen et al 1999). Du et al (1997) examined the impact of IC errors on quantitative precipitation forecasts for a case of strong cyclogenesis with two 25-member mesoscale ensembles. They found that 90% of the reduction in rootmean-square error afforded by ensemble averaging could be realized with as few as 8-10 members.…”

“…Added to the initial analyses are randomly generated perturbations that contain amplitudes and scale-dependent, spatially correlated structures consistent with prior estimates of analysis uncertainty by Daley and Mayer (1986). More thorough descriptions of the scheme and examples of the perturbation structures can be found in Errico and Baumhefner (1987), Baumhefner (1988, 1989), and Du et al (1997).…”

The Central European limited‐area ensemble forecasting system: ALADIN‐LAEF

“…comm. ), the University of Washington Mesoscale Ensemble (Eckel and Mass, 2005), and the approach of random initial conditions perturbation followed by Du et al (1997) and Stensrud et al (2000). Chen et al (2005) proposed a method for initial perturbations using a Different Physical Mode Method (DPMM) for dealing with the initial uncertainties on different scales.…”

Probabilistic forecasting of wind power production losses in cold climates: A case study

“…The HarmonEPS ensemble consists of 10 perturbed members and one control member. The number of ensemble members were chosen based on a short-range EPS study, by Du et al (1997), where it was shown that 8-10 ensemble members are sufficient for at least 90 % of the possible benefit of using an EPS. Since 20 an EPS is computationally demanding to run, 10 members were therefore considered to be sufficient for the present study.…”