scholarly journals Evaluating a lightning parameterization based on cloud-top height for mesoscale numerical model simulations

2012 ◽  
Vol 5 (4) ◽  
pp. 3493-3531 ◽  
Author(s):  
J. Wong ◽  
M. C. Barth ◽  
D. Noone
Keyword(s):  
Spectral Characteristics ◽  
Wrf Model ◽  
Calibration Factor ◽  
Weather Research And Forecasting ◽  
Base Case ◽  
Lightning Flash ◽  
Grid Spacing ◽  
Core Density ◽  
Flash Rate ◽  
Mesoscale Simulations

Abstract. The Price and Rind lightning parameterization based on cloud-top height is a commonly used method for predicting flash rate in global chemistry models. As mesoscale simulations begin to implement flash rate predictions at resolutions that partially resolve convection, it is necessary to validate and understand the behavior of this method within such regime. In this study, we tested the flash rate parameterization, intra-cloud/cloud-to-ground (IC:CG) partitioning parameterization, and the associated resolution dependency "calibration factor" by Price and Rind using the Weather Research and Forecasting (WRF) model running at 36 km, 12 km, and 4 km grid spacings within the continental United States. Our results show that while the integrated flash count is consistent with observation when model biases in convection are taken into account, an erroneous frequency distribution is simulated. When the spectral characteristics of lightning flash rate is a concern, we recommend the use of prescribed IC:CG values. In addition, using cloud-top from convective parameterization, the "calibration factor" is also shown to be insufficient in reconciling the resolution dependency at the tested grid spacing used in this study. We recommend scaling by areal ratio relative to a base-case grid spacing determined by convective core density.

scholarly journals Evaluating a lightning parameterization based on cloud-top height for mesoscale numerical model simulations

2013 ◽  
Vol 6 (2) ◽  
pp. 429-443 ◽  
Author(s):  
J. Wong ◽  
M. C. Barth ◽  
D. Noone
Keyword(s):  
Spectral Characteristics ◽  
Wrf Model ◽  
Calibration Factor ◽  
Weather Research And Forecasting ◽  
Base Case ◽  
Lightning Flash ◽  
Grid Spacing ◽  
Core Density ◽  
Flash Rate ◽  
Mesoscale Simulations

Abstract. The Price and Rind lightning parameterization based on cloud-top height is a commonly used method for predicting flash rate in global chemistry models. As mesoscale simulations begin to implement flash rate predictions at resolutions that partially resolve convection, it is necessary to validate and understand the behavior of this method within such a regime. In this study, we tested the flash rate parameterization, intra-cloud/cloud-to-ground (IC:CG) partitioning parameterization, and the associated resolution dependency "calibration factor" by Price and Rind using the Weather Research and Forecasting (WRF) model running at 36 km, 12 km, and 4 km grid spacings within the continental United States. Our results show that while the integrated flash count is consistent with observations when model biases in convection are taken into account, an erroneous frequency distribution is simulated. When the spectral characteristics of lightning flash rate are a concern, we recommend the use of prescribed IC:CG values. In addition, using cloud-top from convective parameterization, the "calibration factor" is also shown to be insufficient in reconciling the resolution dependency at the tested grid spacing used in this study. We recommend scaling by areal ratio relative to a base-case grid spacing determined by convective core density.

scholarly journals Forecasting Lightning Threat Using Cloud-Resolving Model Simulations

2009 ◽  
Vol 24 (3) ◽  
pp. 709-729 ◽  
Author(s):  
Eugene W. McCaul ◽  
Steven J. Goodman ◽  
Katherine M. LaCasse ◽  
Daniel J. Cecil
Keyword(s):  
Numerical Models ◽  
Wrf Model ◽  
Phase Region ◽  
General Character ◽  
Lightning Flash ◽  
Flash Rate ◽  
Spatial Coverage ◽  
Physically Based ◽  
Cloud Resolving Model ◽  
Near Term

Abstract Two new approaches are proposed and developed for making time- and space-dependent, quantitative short-term forecasts of lightning threats, and a blend of these approaches is devised that capitalizes on the strengths of each. The new methods are distinctive in that they are based entirely on the ice-phase hydrometeor fields generated by regional cloud-resolving numerical simulations, such as those produced by the Weather Research and Forecasting (WRF) model. These methods are justified by established observational evidence linking aspects of the precipitating ice hydrometeor fields to total flash rates. The methods are straightforward and easy to implement, and offer an effective near-term alternative to the incorporation of complex and costly cloud electrification schemes into numerical models. One method is based on upward fluxes of precipitating ice hydrometeors in the mixed-phase region at the −15°C level, while the second method is based on the vertically integrated amounts of ice hydrometeors in each model grid column. Each method can be calibrated by comparing domain-wide statistics of the peak values of simulated flash-rate proxy fields against domain-wide peak total lightning flash-rate density data from observations. Tests show that the first method is able to capture much of the temporal variability of the lightning threat, while the second method does a better job of depicting the areal coverage of the threat. The blended solution proposed in this work is designed to retain most of the temporal sensitivity of the first method, while adding the improved spatial coverage of the second. Simulations of selected diverse North Alabama cases show that the WRF can distinguish the general character of most convective events, and that the methods employed herein show promise as a means of generating quantitatively realistic fields of lightning threat. However, because the models tend to have more difficulty in predicting the instantaneous placement of storms, forecasts of the detailed location of the lightning threat based on single simulations can be in error. Although these model shortcomings presently limit the precision of lightning threat forecasts from individual runs of current generation models, the techniques proposed herein should continue to be applicable as newer and more accurate physically based model versions, physical parameterizations, initialization techniques, and ensembles of forecasts become available.

scholarly journals Comparison of Simulated Precipitation over East Asia in Two Regional Models with Hydrostatic and Nonhydrostatic Dynamical Cores

2016 ◽  
Vol 144 (10) ◽  
pp. 3579-3590 ◽  
Author(s):  
Jihyeon Jang ◽  
Song-You Hong
Keyword(s):  
Wrf Model ◽  
Local Maximum ◽  
Weather Research And Forecasting ◽  
Model Program ◽  
Grid Spacing ◽  
Dynamical Core ◽  
Simulated Precipitation ◽  
Precipitation Area ◽  
Hydrostatic Limit ◽  
Regional Forecasts

This study examines the characteristics of a nonhydrostatic dynamical core compared to a corresponding hydrostatic dynamical core in the Regional Model Program (RMP) of the Global/Regional Integrated Model system (GRIMs), a spectral model for regional forecasts, focusing on simulated precipitation over Korea. This kind of comparison is also executed in the Weather Research and Forecasting (WRF) finite-difference model with the same physics package used in the RMP. Overall, it is found that the nonhydrostatic dynamical core experiment accurately reproduces the heavy rainfall near Seoul, South Korea, on a 3-km grid, relative to the results from the hydrostatic dynamical core in both models. However, the characteristics of nonhydrostatic effects on the simulated precipitation differ between the RMP and WRF Model. The RMP with the nonhydrostatic dynamical core improves the local maximum, which is exaggerated in the hydrostatic simulation. The hydrostatic simulation of the WRF Model displaces the major precipitation area toward the mountainous region along the east coast of the peninsula, which is shifted into the observed area in the nonhydrostatic simulation. In the simulation of a summer monsoonal rainfall, these nonhydrostatic effects are negligible in the RMP, but the simulated monsoonal rainfall is still influenced by the dynamical core in the WRF Model even at a 27-km grid spacing. One of the reasons for the smaller dynamical core effect in the RMP seems to be the relatively strong horizontal diffusion, resulting in a smaller grid size of the hydrostatic limit.

scholarly journals Capabilities and Limitations of Convection-Permitting WRF Simulations of Lake-Effect Systems over the Great Salt Lake

2015 ◽  
Vol 30 (6) ◽  
pp. 1711-1731 ◽  
Author(s):  
John D. McMillen ◽  
W. James Steenburgh
Keyword(s):  
Salt Lake ◽  
Numerical Models ◽  
Great Salt Lake ◽  
Wrf Model ◽  
Weather Research And Forecasting ◽  
Grid Spacing ◽  
Convective Storm ◽  
Lake Effect ◽  
Object Based ◽  
The Right

Abstract Although previous studies suggest that the Weather Research and Forecasting (WRF) Model can produce physically realistic banded Great Salt Lake–effect (GSLE) precipitation features, the accuracy and reliability of these simulations for forecasting applications remains unquantified. The ability of the WRF to simulate nonbanded GSLE features is also unknown. This paper uses subjective, traditional, and object-based verification to evaluate convection-permitting (1.33-km grid spacing) WRF simulations of 11 banded and 8 nonbanded GSLE events. In all simulations, the WRF was configured with the Thompson microphysics and the Yonsei University (YSU) planetary boundary layer parameterizations. Subjectively, a majority of the simulations of banded GSLE events produce physically realistic precipitation features. In contrast, simulations of nonbanded GSLE events rarely produce physically realistic precipitation features and sometimes erroneously produce banded precipitation features. Simulations of banded GSLE events produce equitable threat scores (ETSs) comparable to other convective-storm verification studies, whereas simulations of nonbanded events exhibit lower ETSs. Object-based verification shows that the WRF tends to generate precipitation to the right (relative to the flow) and downstream of observed. These results, although based on a specific WRF parameterization suite, suggest that deterministic prediction of GSLE using convection-permitting models will prove challenging in practice with current numerical models. In addition, identifying and addressing the causes of the rightward and downstream precipitation bias is necessary to achieve optimal performance from future probabilistic and/or deterministic high-resolution forecast systems.

Fire weather simulation skill by the Weather Research and Forecasting (WRF) model over south-east Australia from 1985 to 2009

2013 ◽  
Vol 22 (6) ◽  
pp. 739 ◽  
Author(s):  
Hamish Clarke ◽  
Jason P. Evans ◽  
Andrew J. Pitman
Keyword(s):  
Initial Conditions ◽  
Full Range ◽  
Wrf Model ◽  
Weather Research And Forecasting ◽  
Fire Weather ◽  
Grid Spacing ◽  
Skill Scores ◽  
Forest Fire Danger ◽  
Environmental Prediction ◽  
Weather Research

The fire weather of south-east Australia from 1985 to 2009 has been simulated using the Weather Research and Forecasting (WRF) model. The US National Oceanic and Atmospheric Administration Centers for Environmental Prediction and National Center for Atmospheric Research reanalysis supplied the lateral boundary conditions and initial conditions. The model simulated climate and the reanalysis were evaluated against station-based observations of the McArthur Forest Fire Danger Index (FFDI) using probability density function skill scores, annual cumulative FFDI and days per year with FFDI above 50. WRF simulated the main features of the FFDI distribution and its spatial variation, with an overall positive bias. Errors in average FFDI were caused mostly by errors in the ability of WRF to simulate relative humidity. In contrast, errors in extreme FFDI values were driven mainly by WRF errors in wind speed simulation. However, in both cases the quality of the observed data is difficult to ascertain. WRF run with 50-km grid spacing did not consistently improve upon the reanalysis statistics. Decreasing the grid spacing to 10km led to fire weather that was generally closer to observations than the reanalysis across the full range of evaluation metrics used here. This suggests it is a very useful tool for modelling fire weather over the entire landscape of south-east Australia.

scholarly journals A Comparison of Precipitation Forecast Skill between Small Convection-Allowing and Large Convection-Parameterizing Ensembles

2009 ◽  
Vol 24 (4) ◽  
pp. 1121-1140 ◽  
Author(s):  
Adam J. Clark ◽  
William A. Gallus ◽  
Ming Xue ◽  
Fanyou Kong
Keyword(s):  
Wrf Model ◽  
Precipitation Forecast ◽  
Convective Precipitation ◽  
Weather Research And Forecasting ◽  
Lead Times ◽  
Grid Spacing ◽  
Ensemble Forecasts ◽  
Probabilistic Forecasts ◽  
Central United States ◽  
Statistical Consistency

Abstract An experiment has been designed to evaluate and compare precipitation forecasts from a 5-member, 4-km grid-spacing (ENS4) and a 15-member, 20-km grid-spacing (ENS20) Weather Research and Forecasting (WRF) model ensemble, which cover a similar domain over the central United States. The ensemble forecasts are initialized at 2100 UTC on 23 different dates and cover forecast lead times up to 33 h. Previous work has demonstrated that simulations using convection-allowing resolution (CAR; dx ∼ 4 km) have a better representation of the spatial and temporal statistical properties of convective precipitation than coarser models using convective parameterizations. In addition, higher resolution should lead to greater ensemble spread as smaller scales of motion are resolved. Thus, CAR ensembles should provide more accurate and reliable probabilistic forecasts than parameterized-convection resolution (PCR) ensembles. Computation of various precipitation skill metrics for probabilistic and deterministic forecasts reveals that ENS4 generally provides more accurate precipitation forecasts than ENS20, with the differences tending to be statistically significant for precipitation thresholds above 0.25 in. at forecast lead times of 9–21 h (0600–1800 UTC) for all accumulation intervals analyzed (1, 3, and 6 h). In addition, an analysis of rank histograms and statistical consistency reveals that faster error growth in ENS4 eventually leads to more reliable precipitation forecasts in ENS4 than in ENS20. For the cases examined, these results imply that the skill gained by increasing to CAR outweighs the skill lost by decreasing the ensemble size. Thus, when computational capabilities become available, it will be highly desirable to increase the ensemble resolution from PCR to CAR, even if the size of the ensemble has to be reduced.

scholarly journals Assessment of the Weather Research and Forecasting (WRF) model for simulation of extreme rainfall events in the upper Ganga Basin

2018 ◽  
Vol 22 (2) ◽  
pp. 1095-1117 ◽  
Author(s):  
Ila Chawla ◽  
Krishna K. Osuri ◽  
Pradeep P. Mujumdar ◽  
Dev Niyogi
Keyword(s):  
Land Surface ◽  
Heavy Rainfall ◽  
Wrf Model ◽  
Extreme Rainfall ◽  
Weather Research And Forecasting ◽  
Grid Spacing ◽  
Ganga Basin ◽  
Extreme Rainfall Events ◽  
Rainfall Events ◽  
Heavy Rainfall Events

Abstract. Reliable estimates of extreme rainfall events are necessary for an accurate prediction of floods. Most of the global rainfall products are available at a coarse resolution, rendering them less desirable for extreme rainfall analysis. Therefore, regional mesoscale models such as the advanced research version of the Weather Research and Forecasting (WRF) model are often used to provide rainfall estimates at fine grid spacing. Modelling heavy rainfall events is an enduring challenge, as such events depend on multi-scale interactions, and the model configurations such as grid spacing, physical parameterization and initialization. With this background, the WRF model is implemented in this study to investigate the impact of different processes on extreme rainfall simulation, by considering a representative event that occurred during 15–18 June 2013 over the Ganga Basin in India, which is located at the foothills of the Himalayas. This event is simulated with ensembles involving four different microphysics (MP), two cumulus (CU) parameterizations, two planetary boundary layers (PBLs) and two land surface physics options, as well as different resolutions (grid spacing) within the WRF model. The simulated rainfall is evaluated against the observations from 18 rain gauges and the Tropical Rainfall Measuring Mission Multi-Satellite Precipitation Analysis (TMPA) 3B42RT version 7 data. From the analysis, it should be noted that the choice of MP scheme influences the spatial pattern of rainfall, while the choice of PBL and CU parameterizations influences the magnitude of rainfall in the model simulations. Further, the WRF run with Goddard MP, Mellor–Yamada–Janjic PBL and Betts–Miller–Janjic CU scheme is found to perform best in simulating this heavy rain event. The selected configuration is evaluated for several heavy to extremely heavy rainfall events that occurred across different months of the monsoon season in the region. The model performance improved through incorporation of detailed land surface processes involving prognostic soil moisture evolution in Noah scheme compared to the simple Slab model. To analyse the effect of model grid spacing, two sets of downscaling ratios – (i) 1 : 3, global to regional (G2R) scale and (ii) 1 : 9, global to convection-permitting scale (G2C) – are employed. Results indicate that a higher downscaling ratio (G2C) causes higher variability and consequently large errors in the simulations. Therefore, G2R is adopted as a suitable choice for simulating heavy rainfall event in the present case study. Further, the WRF-simulated rainfall is found to exhibit less bias when compared with the NCEP FiNaL (FNL) reanalysis data.

Hurricane Vortex Dynamics during Atlantic Extratropical Transition

2008 ◽  
Vol 65 (3) ◽  
pp. 714-736 ◽  
Author(s):  
Christopher A. Davis ◽  
Sarah C. Jones ◽  
Michael Riemer
Keyword(s):  
Vertical Structure ◽  
Wrf Model ◽  
Vertical Wind Shear ◽  
Weather Research And Forecasting ◽  
Synoptic Scale ◽  
Grid Spacing ◽  
Extratropical Transition ◽  
Varying Environments ◽  
Atlantic Hurricanes ◽  
Moving Storm

Abstract Simulations of six Atlantic hurricanes are diagnosed to understand the behavior of realistic vortices in varying environments during the process of extratropical transition (ET). The simulations were performed in real time using the Advanced Research Weather Research and Forecasting (WRF) model (ARW), using a moving, storm-centered nest of either 4- or 1.33-km grid spacing. The six simulations, ranging from 45 to 96 h in length, provide realistic evolution of asymmetric precipitation structures, implying control by the synoptic scale, primarily through the vertical wind shear. The authors find that, as expected, the magnitude of the vortex tilt increases with increasing shear, but it is not until the shear approaches 20 m s−1 that the total vortex circulation decreases. Furthermore, the total vertical mass flux is proportional to the shear for shears less than about 20–25 m s−1, and therefore maximizes, not in the tropical phase, but rather during ET. This has important implications for predicting hurricane-induced perturbations of the midlatitude jet and its consequences on downstream predictability. Hurricane vortices in the sample resist shear by either adjusting their vertical structure through precession (Helene 2006), forming an entirely new center (Irene 2005), or rapidly developing into a baroclinic cyclone in the presence of a favorable upper-tropospheric disturbance (Maria 2005). Vortex resiliency is found to have a substantial diabatic contribution whereby vertical tilt is reduced through reduction of the primary vortex asymmetry induced by the shear. If the shear and tilt are so large that upshear subsidence overwhelms the symmetric vertical circulation of the hurricane, latent heating and precipitation will occur to the left of the tilt vector and slow precession. Such was apparent during Wilma (2005).

scholarly journals Development of the WRF-CO2 4DVar assimilation system

2016 ◽  
Author(s):  
Tao Zheng ◽  
Nancy French ◽  
Martin Baxter
Keyword(s):  
Inverse Modeling ◽  
Particle Trajectory ◽  
Wrf Model ◽  
Weather Research And Forecasting ◽  
Observation Data ◽  
Grid Spacing ◽  
Lagrangian Particle ◽  
Optimization Schemes ◽  
Particle Trajectory Model ◽  
Assimilation System

Abstract. Regional atmospheric CO2 inversions commonly use Lagrangian particle trajectory model simulations to calculate the required influence function. To provide an alternative, we developed an adjoint based four-dimensional variational (4DVar) assimilation system, WRF-CO2 4DVar. This system is developed based on the Weather Research and Forecasting (WRF) model system, including WRF-Chem, WRFPLUS, and WRFDA. In WRF-CO2 4DVR, CO2 is modeled as a tracer and its feedback to meteorology is ignored. This configuration allows most WRF physical parameterizations to be used in the assimilation system without incurring a large amount of code development. WRF-CO2 4DVar solves for the optimized CO2 emission scaling factors in a Bayesian framework. Two variational optimization schemes are implemented for the system: the first uses the L-BFGS-B and the second uses the Lanczos conjugate gradient (CG) in an incremental approach. We modified WRFPLUS forward, tangent linear, and adjoint models to include CO2 related processes. The system is tested by simulations over a domain covering the continental United States at 48 km × 48 km grid spacing. The accuracy of the tangent linear and adjoint models are assessed by comparing against finite difference sensitivity. The system's effectiveness for CO2 inverse modeling is tested using pseudo-observation data. The results of the sensitivity and inverse modeling tests demonstrate the potential usefulness of WRF-CO2 4DVar for regional CO2 inversions.

scholarly journals Comparison of the Diurnal Precipitation Cycle in Convection-Resolving and Non-Convection-Resolving Mesoscale Models

2007 ◽  
Vol 135 (10) ◽  
pp. 3456-3473 ◽  
Author(s):  
Adam J. Clark ◽  
William A. Gallus ◽  
Tsing-Chang Chen
Keyword(s):  
United States ◽  
Spatial Correlation ◽  
Diurnal Cycle ◽  
Wrf Model ◽  
Correlation Coefficients ◽  
Weather Research And Forecasting ◽  
Grid Spacing ◽  
Diurnal Cycles ◽  
Central United States ◽  
Precipitation Cycle

Abstract The diurnal cycles of rainfall in 5-km grid-spacing convection-resolving and 22-km grid-spacing non-convection-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.

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