Contributions of Mixed Physics versus Perturbed Initial/Lateral Boundary Conditions to Ensemble-Based Precipitation Forecast Skill

Clark, Adam J.; Gallus, William A.; Chen, Tsing-Chang

doi:10.1175/2007mwr2029.1

Cited by 49 publications

(80 citation statements)

References 44 publications

(74 reference statements)

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…However, with the increasing use of ensembles at fine grid scales (e.g., Xue et al 2007;Clark et al 2009), an incentive exists to determine the best ways of using these techniques to both evaluate and provide better forecast guidance from ensemble forecasts, beyond simple application of the object-based methods to the ensemble mean forecast. Traditional spread and skill measures applied to ensembles, such as variance or mean squared error (MSE) of the ensemble mean, are affected by both bias and small displacements that can complicate interpretation in a manner similar to that with traditional verification measures applied to deterministic forecasts.…”

Section: Introductionmentioning

confidence: 99%

Application of Object-Based Verification Techniques to Ensemble Precipitation Forecasts

Gallus

2010

Weather and Forecasting

Self Cite

View full text Add to dashboard Cite

Both theMethod for Object-basedDiagnostic Evaluation (MODE) and contiguous rain area (CRA) objectbased verification techniques have been used to analyze precipitation forecasts from two sets of ensembles to determine if spread-skill behavior observed using traditional measures can be seen in the object parameters. One set consisted of two eight-member Weather Research and Forecasting (WRF) model ensembles: one having mixed physics and dynamics with unperturbed initial and lateral boundary conditions (Phys) and another using common physics and a dynamic core but with perturbed initial and lateral boundary conditions (IC/LBC). Traditional measures found that spread grows much faster in IC/LBC than in Phys so that after roughly 24 h, better skill and spread are found in IC/LBC. These measures also reflected a strong diurnal signal of precipitation. The other set of ensembles included five members of a 4-km grid-spacing WRF ensemble (ENS4) and five members of a 20-km WRF ensemble (ENS20). Traditional measures suggested that the diurnal signal was better in ENS4 and spread increased more rapidly than in ENS20. Standard deviations (SDs) of four object parameters computed for the first set of ensembles using MODE and CRA showed the trend of enhanced spread growth in IC/LBC compared to Phys that had been observed in traditional measures, with the areal coverage of precipitation exhibiting the greatest growth in spread with time. The two techniques did not produce identical results; although, they did show the same general trends.A diurnal signal could be seen in the SDs of all parameters, especially rain rate, volume, and areal coverage. MODE results also found evidence of a diurnal signal and faster growth of spread in object parameters in ENS4 than in ENS20. Some forecasting approaches based onMODEand CRAoutput are also demonstrated. Forecasts based on averages of object parameters from each ensemble member were more skillful than forecasts based on MODE or CRA applied to an ensemble mean computed using the probability matching technique for areal coverage and volume, but differences in the two techniques were less pronounced for rain rate and displacement. The use of a probability threshold to define objects was also shown to be a valid forecasting approach with MODE. Keywordsareal coverage, diurnal signals, dynamic cores, ensemble members, lateral boundary conditions, object based, precipitation forecast, probability matching, rain rates, standard deviation, verification techniques, weather research and forecasting models, ensemble forecasting, precipitation assessment have been used to analyze precipitation forecasts from two sets of ensembles to determine if spread-skill behavior observed using traditional measures can be seen in the object parameters. One set consisted of two eight-member Weather Research and Forecasting (WRF) model ensembles: one having mixed physics and dynamics with unperturbed initial and lateral boundary conditions (Phys) and another using common physics and a dynamic core but with p...

show abstract

Section: Introductionmentioning

confidence: 99%

Application of Object-Based Verification Techniques to Ensemble Precipitation Forecasts

Gallus

2010

Weather and Forecasting

Self Cite

View full text Add to dashboard Cite

show abstract

“…One method commonly used to gain information about ensemble spread is to isolate the error sources by using different perturbation strategies for a set of forecasts (e.g., Houtekamer et al 1996;Stensrud et al 2000;Clark et al 2008). For example, to isolate model errors, the ''perfect analysis'' assumption can be used, in which identical sets of ICs/LBCs are used to initialize various ensemble members with mixed physics.…”

Section: Introductionmentioning

confidence: 99%

Growth of Spread in Convection-Allowing and Convection-Parameterizing Ensembles

Clark

Gallus

Xue

et al. 2010

Weather and Forecasting

Self Cite

View full text Add to dashboard Cite

During the 2007 NOAA Hazardous Weather Testbed Spring Experiment, a 10-member 4-km grid-spacing Storm-Scale Ensemble Forecast (SSEF) system was run in real time to provide experimental severe weather forecasting guidance. Five SSEF system members used perturbed initial and lateral boundary conditions (ICs and LBCs) and mixed physics (ENS4), and five members used only mixed physics (ENS4phys). This ensemble configuration facilitates a comparison of ensemble spread generated by a combination of perturbed ICs/LBCs and mixed physics to that generated by only mixed physics, which is examined herein. In addition, spread growth and spread-error metrics for the two SSEF system configurations are compared to similarly configured 20-km grid-spacing convection-parameterizing ensembles (ENS20 and ENS20phys). Twelve forecast fields are examined for 20 cases. For most fields, ENS4 mean spread growth rates are higher than ENS20 for ensemble configurations with both sets of perturbations, which is expected as smaller scales of motion are resolved at higher resolution. However, when ensembles with only mixed physics are compared, mass-related fields (i.e., geopotential height and mean sea level pressure) in ENS20phys have slightly higher spread growth rates than ENS4phys, likely resulting from the additional physics uncertainty in ENS20phys from varied cumulus parameterizations that were not used at 4-km grid spacing. For 4-and 20-km configurations, the proportion of spread generated by mixed physics in ENS4 and ENS20 increased with increasing forecast lead time. In addition, low-level fields (e.g., 2-m temperature) had a higher proportion of spread generated by mixed physics than mass-related fields. Spread-error analyses revealed that ensemble variance from the current uncalibrated ensemble systems was not a reliable indicator of forecast uncertainty. Furthermore, ENS4 had better statistical consistency than ENS20 for some mass-related fields, wind-related fields, precipitation, and most unstable convective available potential energy (MUCAPE) with no noticeable differences for low-level temperature and dewpoint fields. The variety of results obtained for the different types of fields examined suggests that future ensemble design should give careful consideration to the specific types of forecasts desired by the user. . This ensemble configuration facilitates a comparison of ensemble spread generated by a combination of perturbed ICs/LBCs and mixed physics to that generated by only mixed physics, which is examined herein. In addition, spread growth and spread-error metrics for the two SSEF system configurations are compared to similarly configured 20-km grid-spacing convection-parameterizing ensembles (ENS20 and ENS20 phys ). Twelve forecast fields are examined for 20 cases.For most fields, ENS4 mean spread growth rates are higher than ENS20 for ensemble configurations with both sets of perturbations, which is expected as smaller scales of motion are resolved at higher resolution. However, when ensembles with only mixed...

show abstract

“…Clark et al (2008) found among other results that the ensemble variance in their MPP ensemble was greater than that in a PIC ensemble during the first 24 h of the forecast. In another experiment comparing MPP and a stochastic kinetic-energy backscatter scheme, Berner et al (2011) argued for a combination of both approaches.…”

Section: Introductionmentioning

confidence: 70%

A Comparison of Perturbed Initial Conditions and Multiphysics Ensembles in a Severe Weather Episode in Spain

Tapiador¹,

Tao²,

Shi³

et al. 2012

Journal of Applied Meteorology and Climatology

View full text Add to dashboard Cite

Ensembles of numerical model forecasts are of interest to operational early warning forecasters as the spread of the ensemble provides an indication of the uncertainty of the alerts, and the mean value is deemed to outperform the forecasts of the individual models. This paper explores two ensembles on a severe weather episode in Spain, aiming to ascertain the relative usefulness of each one. One ensemble uses sensible choices of physical parameterizations (precipitation microphysics, land surface physics, and cumulus physics) while the other follows a perturbed initial conditions approach. The results show that, depending on the parameterizations, large differences can be expected in terms of storm location, spatial structure of the precipitation field, and rain intensity. It is also found that the spread of the perturbed initial conditions ensemble is smaller than the dispersion due to physical parameterizations. This confirms that in severe weather situations operational forecasts should address moist physics deficiencies to realize the full benefits of the ensemble approach, in addition to optimizing initial conditions. The results also provide insights into differences in simulations arising from ensembles of weather models using several combinations of different physical parameterizations.

show abstract

Contributions of Mixed Physics versus Perturbed Initial/Lateral Boundary Conditions to Ensemble-Based Precipitation Forecast Skill

Cited by 49 publications

References 44 publications

Application of Object-Based Verification Techniques to Ensemble Precipitation Forecasts

Application of Object-Based Verification Techniques to Ensemble Precipitation Forecasts

Growth of Spread in Convection-Allowing and Convection-Parameterizing Ensembles

A Comparison of Perturbed Initial Conditions and Multiphysics Ensembles in a Severe Weather Episode in Spain

Contact Info

Product

Resources

About