The diurnal cycles of rainfall in 5-km grid-spacing convection-resolving and 22-km grid-spacing nonconvection-resolving configurations of the Weather Research and Forecasting (WRF) model are compared to see if significant improvements can be obtained by using fine enough grid spacing to explicitly resolve convection. Diurnally averaged Hovmöller diagrams, spatial correlation coefficients computed in Hovmöller space, equitable threat scores (ETSs), and biases for forecasts conducted from 1 April to 25 July 2005 over a large portion of the central United States are used for the comparisons. A subjective comparison using Hovmöller diagrams of diurnally averaged rainfall show that the diurnal cycle representation in the 5-km configuration is clearly superior to that in the 22-km configuration during forecast hours 24-48. The superiority of the 5-km configuration is validated by much higher spatial correlation coefficients than in the 22-km configuration. During the first 24 forecast hours the 5-km model forecasts appear to be more adversely affected by model "spinup" processes than the 22-km model forecasts, and it is less clear, subjectively, which configuration has the better diurnal cycle representation, although spatial correlation coefficients are slightly higher in the 22-km configuration. ETSs in both configurations have diurnal oscillations with relative maxima occurring in both configurations at forecast hours corresponding to 0000-0300 LST, while biases also have diurnal oscillations with relative maxima (largest errors) in the 22-km (5-km) configuration occurring at forecast hours corresponding to 1200 (1800) LST. At all forecast hours, ETSs from the 22-km configuration are higher than those in the 5-km configuration. This inconsistency with some of the results obtained using the aforementioned spatial correlation coefficients reinforces discussion in past literature that cautions against using "traditional" verification statistics, such as ETS, to compare high-to low-resolution forecasts. ABSTRACTThe diurnal cycles of rainfall in 5-km grid-spacing convection-resolving and 22-km grid-spacing nonconvection-resolving configurations of the Weather Research and Forecasting (WRF) model are compared to see if significant improvements can be obtained by using fine enough grid spacing to explicitly resolve convection. Diurnally averaged Hovmöller diagrams, spatial correlation coefficients computed in Hovmöller space, equitable threat scores (ETSs), and biases for forecasts conducted from 1 April to 25 July 2005 over a large portion of the central United States are used for the comparisons. A subjective comparison using Hovmöller diagrams of diurnally averaged rainfall show that the diurnal cycle representation in the 5-km configuration is clearly superior to that in the 22-km configuration during forecast hours 24-48. The superiority of the 5-km configuration is validated by much higher spatial correlation coefficients than in the 22-km configuration. During the first 24 forecast hours the 5-km model for...
An experiment is described that is designed to examine the contributions of model, initial condition (IC), and lateral boundary condition (LBC) errors to the spread and skill of precipitation forecasts from two regional eight-member 15-km grid-spacing Weather Research and Forecasting (WRF) ensembles covering a 1575 km X 1800 km domain. It is widely recognized that a skillful ensemble [i.e., an ensemble with a probability distribution function (PDF) that generates forecast probabilities with high resolution and reliability] should account for both error sources. Previous work suggests that model errors make a larger contribution than IC and LBC errors to forecast uncertainty in the short range before synoptic-scale error growth becomes nonlinear. However, in a regional model with unperturbed LBCs, the infiltration of the lateral boundaries will negate increasing spread. To obtain a better understanding of the contributions to the forecast errors in precipitation and to examine the window of forecast lead time before unperturbed ICs and LBCs begin to cause degradation in ensemble forecast skill, the "perfect model" assumption is made in an ensemble that uses perturbed ICs and LBCs (PILB ensemble), and the "perfect analysis" assumption is made in another ensemble that uses mixed physics-dynamic cores (MP ensemble), thus isolating the error contributions. For the domain and time period used in this study, unperturbed ICs and LBCs in the MP ensemble begin to negate increasing spread around forecast hour 24, and ensemble forecast skill as measured by relative operating characteristic curves (ROC scores) becomes lower in the MP ensemble than in the PILB ensemble, with statistical significance beginning after forecast hour 69. However, degradation in forecast skill in the MP ensemble relative to the PILB ensemble is not observed in an analysis of deterministic forecasts calculated from each ensemble using the probability matching method. Both ensembles were found to lack statistical consistency (i.e., to be underdispersive), with the PILB ensemble (MP ensemble) exhibiting more (less) statistical consistency with respect to forecast lead time. Spread ratios in the PILB ensemble are greater than those in the MP ensemble at all forecast lead times and thresholds examined; however, ensemble variance in the MP ensemble is greater than that in the PILB ensemble during the first 24 h of the forecast. This discrepancy in spread measures likely results from greater bias in the MP ensemble leading to an increase in ensemble variance and decrease in spread ratio relative to the PILB ensemble.Keywords boundary conditions, boundary value problems, degradation, distribution functions, error analysis, errors, probability, probability distributions, random processes, research, risk assessment, statistical methods, sulfate minterals, weather forecasting, deterministic forecasting, ensemble forecasting ABSTRACT An experiment is described that is designed to examine the contributions of model, initial condition (IC), and lateral b...
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.