An Objective Methodology for Configuring and Down-Selecting an NWP Ensemble for Low-Level Wind Prediction

Lee, Jared A.; Kolczynski, Walter C.; McCandless, Tyler; Haupt, Sue Ellen

doi:10.1175/mwr-d-11-00065.1

Cited by 39 publications

(35 citation statements)

References 32 publications

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“…To quantify the uncertainty due to model physics, we use 12 members of the ensemble described by Lee et al (2012) (Table 1), all employing WRF-ARW, version 3.2. WRF-ARW is a widely used open-source mesoscale numerical weather prediction model.…”

Section: B Meteorological Model: Wrfmentioning

confidence: 99%

“…WRF-ARW is a widely used open-source mesoscale numerical weather prediction model. The differences among the WRF-ARW ensemble members are that each uses a different combination of land surface model and ABL parameterization; all of the ensemble members from Lee et al (2012) that use the Grell-Devenyi cumulus parameterization (Grell and Devenyi 2002) are included here. Varying the physics used to represent both the ABL and the land surface that forces the ABL provides variability in ABL depth, as is desired in this study focusing on ABL depth variability.…”

Section: B Meteorological Model: Wrfmentioning

confidence: 99%

“…These include NWP model initialization, model numerics and physics, and the sampling used when transferring meteorological model output into the ATD model (e.g., temporal and spatial resolutions, choice of meteorological model variables for input into the ATD model, etc.). Most prior studies (e.g., Warner et al 2002;Lee et al 2009;Peltier et al 2010) have focused on the impact of uncertainty in the boundary layer winds on uncertainty of the concentration field. Here, we wish to assess uncertainty in the modeled concentration due to the uncertainty in the boundary layer depth.…”

Section: Introductionmentioning

confidence: 99%

“…The variance among ensemble members has been shown to be related to the error variance of the ensemble mean (Eckel and Mass 2005;Kolczynski et al 2009;Kolczynski et al 2011); that is, variation among ensemble members represents uncertainty in its results. Methods of utilizing meteorological ensembles in ATD modeling (e.g., Galmarini et al 2004) include using each member of a meteorological ensemble to drive a single member of an ATD ensemble [explicit ensemble method; e.g., Warner et al (2002)], using the mean of the meteorological ensemble to drive a single ATD simulation [ensemble mean method; e.g., Warner et al (2002); Lee et al (2009)], and choosing the meteorological ensemble member best matching observations [best member or most representative member method; e.g., Warner et al (2002); Warner and Sheu (2000); Lee et al (2009)]. …”

Section: Introductionmentioning

confidence: 99%

“…The windcomponent variances (s u and s y ; SCIPUFF refers to these as UUE and VVE) and covariance (s uy ; SCIPUFF refers to this as UVE) among meteorological ensemble members at each grid point are used by SCIPUFF to increase diffusion (e.g., Lee et al 2009;Peltier et al 2010). Although these current parameterizations deal solely with uncertainty in the wind fields, future applications could include additional sources of meteorological uncertainty.…”

Section: Introductionmentioning

confidence: 99%

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Uncertainty in Contaminant Concentration Fields Resulting from Atmospheric Boundary Layer Depth Uncertainty

Reen

Schmehl

Young

et al. 2014

Journal of Applied Meteorology and Climatology

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The relationship between atmospheric boundary layer (ABL) depth uncertainty and uncertainty in atmospheric transport and dispersion (ATD) simulations is investigated by examining profiles of predicted concentrations of a contaminant. Because ensembles are an important method for quantifying uncertainty in ATD simulations, this work focuses on the utilization and analysis of ensemble members' ABL structures for ATD simulations. A 12-member physics ensemble of meteorological model simulations drives a 12-member explicit ensemble of ATD simulations. The relationship between ABL depth and plume depth is investigated using ensemble members, which vary both the relevant model physics and the numerical methods used to diagnose ABL depth. New analysis methods are used to analyze ensemble output within an ABL-depth relative framework. Uncertainty due to ABL depth calculation methodology is investigated via a four-member mini-ensemble. When subjected to a continuous tracer release, concentration variability among the ensemble members is largest near the ABL top during the daytime, apparently because of uncertainty in ABL depth. This persists to the second day of the simulation for the 4-member diagnosis mini-ensemble, which varies only the ABL depth, but for the 12-member physics ensemble the concentration variability is large throughout the daytime ABL. This suggests that the increased within-ABL concentration variability on the second day is due to larger differences among the ensemble members' predicted meteorological conditions rather than being solely due to differences in the ABL depth diagnosis methods. This work demonstrates new analysis methods for the relationship between ABL depth and plume depth within an ensemble framework and provides motivation for directly including ABL depth uncertainty from a meteorological model into an ATD model.

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Section: B Meteorological Model: Wrfmentioning

confidence: 99%

Section: B Meteorological Model: Wrfmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Uncertainty in Contaminant Concentration Fields Resulting from Atmospheric Boundary Layer Depth Uncertainty

Reen

Schmehl

Young

et al. 2014

Journal of Applied Meteorology and Climatology

Self Cite

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Uncertainty quantification and predictability of wind speed over the Iberian Peninsula

et al. 2017

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During recent decades, the use of probabilistic forecasting methods has increased markedly. However, these predictions still need improvement in uncertainty quantification and predictability analysis. For this reason, the main aim of this paper is to develop tools for quantifying uncertainty and predictability of wind speed over the Iberian Peninsula. To achieve this goal, several spread indexes extracted from an ensemble prediction system are defined in this paper. Subsequently, these indexes were evaluated with the aim of selecting the most appropriate for the characterization of uncertainty associated to the forecasting. Selection is based on comparison of the average magnitude of ensemble spread (ES) and mean absolute percentage error (MAPE). MAPE is estimated by comparing the ensemble mean with wind speed values from different databases. Later, correlation between MAPE and ES was evaluated. Furthermore, probability distribution functions (PDFs) of spread indexes are analyzed to select the index with greater similarity to MAPE PDFs. Then, the spread index selected as optimal is used to carry out a spatiotemporal analysis of model uncertainty in wind forecasting. The results indicate that mountainous regions and the Mediterranean coast are characterized by strong uncertainty, and the spread increases more rapidly in areas affected by strong winds. Finally, a predictability index is proposed for obtaining a tool capable of providing information on whether the predictability is higher or lower than average. The applications developed may be useful in the forecasting of wind potential several days in advance, with substantial importance for estimating wind energy production.

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Evaluation of model‐generated vertical profiles of meteorological variables: method and initial results

Cogan

2017

Meteorological Applications

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Meteorological centres compare global and large-scale regional model output versus radiosonde data more or less continuously for specific models and also make inter-model comparisons. They normally provide comparisons of meteorological variables at standard pressure levels, and similar comparisons for finer-scale models may be found as output from field tests and other experiments. Additional means to evaluate vertical profiles derived from model output more fully should help provide a more complete evaluation of model output compared with observations. Here, two versions of a program to produce vertical profiles of meteorological variables in terms of user input heights or pressure levels are developed, and integrated mean value profiles of the variables for layers defined by those heights or pressures are generated. Output profiles were entered into spreadsheets for calculation of the derived variables of density and vector wind speed. The level and mean layer values from the Weather Research and Forecasting model (version 3.6.1) were compared with co-incident World Meteorological Organization radiosonde observation data and the differences were computed for several variables. The methods developed here may be used for a variety of model-generated and observed vertical profiles with only minimal changes, primarily to the input function.

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An Objective Methodology for Configuring and Down-Selecting an NWP Ensemble for Low-Level Wind Prediction

Cited by 39 publications

References 32 publications

Uncertainty in Contaminant Concentration Fields Resulting from Atmospheric Boundary Layer Depth Uncertainty

Uncertainty in Contaminant Concentration Fields Resulting from Atmospheric Boundary Layer Depth Uncertainty

Uncertainty quantification and predictability of wind speed over the Iberian Peninsula

Evaluation of model‐generated vertical profiles of meteorological variables: method and initial results

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