scholarly journals Evaluation of WRF Model Simulations of Tornadic and Nontornadic Outbreaks Occurring in the Spring and Fall

2010 ◽  
Vol 138 (11) ◽  
pp. 4098-4119 ◽  
Author(s):  
Chad M. Shafer ◽  
Andrew E. Mercer ◽  
Lance M. Leslie ◽  
Michael B. Richman ◽  
Charles A. Doswell
Keyword(s):  
Wrf Model ◽  
Severe Weather ◽  
Weather Research And Forecasting ◽  
Synoptic Scale ◽  
Sea Level Pressure ◽  
Significant Events ◽  
Skill Scores ◽  
Time Of Year ◽  
Tornado Outbreaks ◽  
Thermodynamic Instability

Abstract Recent studies, investigating the ability to use the Weather Research and Forecasting (WRF) model to distinguish tornado outbreaks from primarily nontornadic outbreaks when initialized with synoptic-scale data, have suggested that accurate discrimination of outbreak type is possible up to three days in advance of the outbreaks. However, these studies have focused on the most meteorologically significant events without regard to the season in which the outbreaks occurred. Because tornado outbreaks usually occur during the spring and fall seasons, whereas the primarily nontornadic outbreaks develop predominantly during the summer, the results of these studies may have been influenced by climatological conditions (e.g., reduced shear, in the mean, in the summer months), in addition to synoptic-scale processes. This study focuses on the impacts of choosing outbreaks of severe weather during the same time of year. Specifically, primarily nontornadic outbreaks that occurred during the summer have been replaced with outbreaks that do not occur in the summer. Subjective and objective analyses of the outbreak simulations indicate that the WRF’s capability of distinguishing outbreak type correctly is reduced when the seasonal constraints are included. However, accuracy scores exceeding 0.7 and skill scores exceeding 0.5 using 1-day simulation fields of individual meteorological parameters, show that precursor synoptic-scale processes play an important role in the occurrence or absence of tornadoes in severe weather outbreaks. Low-level storm-relative helicity parameters and synoptic parameters, such as geopotential heights and mean sea level pressure, appear to be most helpful in distinguishing outbreak type, whereas thermodynamic instability parameters are noticeably both less accurate and less skillful.

scholarly journals Evaluation of WRF Forecasts of Tornadic and Nontornadic Outbreaks When Initialized with Synoptic-Scale Input

2009 ◽  
Vol 137 (4) ◽  
pp. 1250-1271 ◽  
Author(s):  
Chad M. Shafer ◽  
Andrew E. Mercer ◽  
Charles A. Doswell ◽  
Michael B. Richman ◽  
Lance M. Leslie
Keyword(s):  
Mesoscale Model ◽  
Wrf Model ◽  
Severe Weather ◽  
Synoptic Scale ◽  
Model Simulations ◽  
Resolution Model ◽  
Weather Parameters ◽  
Lifting Condensation Level ◽  
Tornado Outbreaks ◽  
Tornado Outbreak

Abstract Uncertainty exists concerning the links between synoptic-scale processes and tornado outbreaks. With continuously improving computer technology, a large number of high-resolution model simulations can be conducted to study these outbreaks to the storm scale, to determine the degree to which synoptic-scale processes appear to influence the occurrence of tornado outbreaks, and to determine how far in advance these processes are important. To this end, 50 tornado outbreak simulations are compared with 50 primarily nontornadic outbreak simulations initialized with synoptic-scale input using the Weather Research and Forecasting (WRF) mesoscale model to determine if the model is able to distinguish the outbreak type 1, 2, and 3 days in advance of the event. The model simulations cannot resolve tornadoes explicitly; thus, the use of meteorological covariates (in the form of numerous severe-weather parameters) is necessary to determine whether or not the model is predicting a tornado outbreak. Results indicate that, using the covariates, the WRF model can discriminate outbreak type consistently at least up to 3 days in advance. The severe-weather parameters that are most helpful in discriminating between outbreak types include low-level and deep-layer shear variables and the lifting condensation level. An analysis of the spatial structures and temporal evolution, as well as the magnitudes, of the severe-weather parameters is critical to diagnose the outbreak type correctly. Thermodynamic instability parameters are not helpful in distinguishing the outbreak type, primarily because of a strong seasonal dependence and convective modification in the simulations.

scholarly journals Roles of the Synoptic-Scale Wave Train, the Intraseasonal Oscillation, and High-Frequency Eddies in the Genesis of Typhoon Manyi (2001)*

2014 ◽  
Vol 71 (10) ◽  
pp. 3706-3722 ◽  
Author(s):  
Yamei Xu ◽  
Tim Li ◽  
Melinda Peng
Keyword(s):  
High Frequency ◽  
Development Process ◽  
Wave Train ◽  
Wrf Model ◽  
Intraseasonal Oscillation ◽  
Wave Activity ◽  
Weather Research And Forecasting ◽  
Synoptic Scale ◽  
Energy Dispersion ◽  
Multiple Episodes

Abstract Experiments using the Weather Research and Forecasting (WRF) Model were conducted to investigate the effects of multiscale motions on the genesis of Typhoon Manyi (2001) in the western North Pacific. The precursor signal associated with this typhoon genesis was identified as a northwest–southeast-oriented synoptic-scale wave train (SWT). The model successfully simulated the genesis of the typhoon in the wake of the SWT. Further experiments were conducted to isolate the effects of the SWT, the intraseasonal oscillation (ISO), and high-frequency (shorter than 3 days) eddies in the typhoon formation. Removing the SWT in the initial and boundary conditions eliminates the typhoon genesis. This points out the importance of the SWT in the typhoon genesis. It was noted that the SWT strengthened the wake cyclone through southeastward energy dispersion. The strengthening wake cyclone triggered multiple episodes of strong sustained convective updrafts, leading to aggregation of vertical vorticity and formation of a self-amplified mesoscale core vortex through a “bottom up” development process. Removing the ISO flow eliminates the typhoon genesis, as the ISO significantly modulated the strength of the SWT through accumulation of wave activity. In the absence of SWT–ISO-scale interaction, the southeastward energy dispersion was weakened significantly, and thus the strengthening of the wake cyclone did not occur. As a result, the successive strong sustained convective updrafts disappeared. Removing the high-frequency eddies did not eliminate the typhoon genesis but postponed the genesis for about 36 h.

scholarly journals Mountain Waves Analysis in the Vicinity of the Madrid-Barajas Airport Using the WRF Model

2020 ◽  
Vol 2020 ◽  
pp. 1-17
Author(s):  
Javier Díaz-Fernández ◽  
Lara Quitián-Hernández ◽  
Pedro Bolgiani ◽  
Daniel Santos-Muñoz ◽  
Ángel García Gago ◽  
...  
Keyword(s):  
Wrf Model ◽  
Weather Research And Forecasting ◽  
Mountain Range ◽  
Aircraft Icing ◽  
Microphysics Scheme ◽  
Mountain Waves ◽  
Skill Scores ◽  
Stable Conditions ◽  
Pbl Scheme ◽  
Wind Velocities

Turbulence and aircraft icing associated with mountain waves are weather phenomena potentially affecting aviation safety. In this paper, these weather phenomena are analysed in the vicinity of the Adolfo Suárez Madrid-Barajas Airport (Spain). Mountain waves are formed in this area due to the proximity of the Guadarrama mountain range. Twenty different weather research and forecasting (WRF) model configurations are evaluated in an initial analysis. This shows the incompetence of some experiments to capture the phenomenon. The two experiments showing the best results are used to simulate thirteen episodes with observed mountain waves. Simulated pseudosatellite images are validated using satellite observations, and an analysis is performed through several skill scores applied to brightness temperature. Few differences are found among the different skill scores. Nevertheless, the Thompson microphysics scheme combined with the Yonsei university PBL scheme shows the best results. The simulations produced by this scheme are used to evaluate the characteristic variables of the mountain wave episodes at windward and leeward and over the mountain. The results show that north-northwest wind directions, moderate wind velocities, and neutral or slightly stable conditions are the main features for the episodes evaluated. In addition, a case study is analysed to evidence the WRF ability to properly detect turbulence and icing associated with mountain waves, even when there is no visual evidence available.

scholarly journals Impact of Coupling an Ocean Model to WRF Nor’easter Simulations

2015 ◽  
Vol 143 (12) ◽  
pp. 4997-5016 ◽  
Author(s):  
Stephen D. Nicholls ◽  
Steven G. Decker
Keyword(s):  
Storm Track ◽  
Wrf Model ◽  
Ocean Model ◽  
Weather Research And Forecasting ◽  
Model Coupling ◽  
Sea Level Pressure ◽  
Model Simulations ◽  
Simulated Precipitation ◽  
Ocean Atmosphere ◽  
The Impact

Abstract The impact of ocean–atmosphere coupling and its possible seasonal dependence upon Weather Research and Forecasting (WRF) Model simulations of seven, wintertime cyclone events was investigated. Model simulations were identical aside from the degree of ocean model coupling (static SSTs, 1D mixed layer model, full-physics 3D ocean model). Both 1D and 3D ocean model coupling simulations show that SSTs following the passage of a nor’easter did tend to cool more strongly during the early season (October–December) and were more likely to warm late in the season (February–April). Model simulations produce SST differences of up to 1.14 K, but this change did not lead to significant changes in storm track (<100 km), maximum 10-m winds (<2 m s−1), or minimum sea level pressure (≤5 hPa). Simulated precipitation showed little sensitivity to model coupling, but all simulations did tend to overpredict precipitation extent (bias > 1) and have low-to-moderate threat scores (0.31–0.59). Analysis of the storm environment and the overall simulation failed to reveal any statistically significant differences in model error attributable to ocean–atmosphere coupling. Despite this result, ocean model coupling can reduce dynamical field error at a single level by up to 20%, and this was slightly greater (1%–2%) with 3D ocean model coupling as compared to 1D ocean model coupling. Thus, while 3D ocean model coupling tended to generally produce more realistic simulations, its impact would likely be more profound for longer-term simulations.

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 Forecasting Tornadoes Using Convection-Permitting Ensembles

2016 ◽  
Vol 31 (1) ◽  
pp. 273-295 ◽  
Author(s):  
Burkely T. Gallo ◽  
Adam J. Clark ◽  
Scott R. Dembek
Keyword(s):  
Operating Characteristic ◽  
Wrf Model ◽  
Environmental Parameters ◽  
Environmental Information ◽  
Severe Weather ◽  
Weather Research And Forecasting ◽  
Critical Success ◽  
Weather Parameters ◽  
Critical Success Index ◽  
Success Index

Abstract Hourly maximum fields of simulated storm diagnostics from experimental versions of convection-permitting models (CPMs) provide valuable information regarding severe weather potential. While past studies have focused on predicting any type of severe weather, this study uses a CPM-based Weather Research and Forecasting (WRF) Model ensemble initialized daily at the National Severe Storms Laboratory (NSSL) to derive tornado probabilities using a combination of simulated storm diagnostics and environmental parameters. Daily probabilistic tornado forecasts are developed from the NSSL-WRF ensemble using updraft helicity (UH) as a tornado proxy. The UH fields are combined with simulated environmental fields such as lifted condensation level (LCL) height, most unstable and surface-based CAPE (MUCAPE and SBCAPE, respectively), and multifield severe weather parameters such as the significant tornado parameter (STP). Varying thresholds of 2–5-km updraft helicity were tested with differing values of σ in the Gaussian smoother that was used to derive forecast probabilities, as well as different environmental information, with the aim of maximizing both forecast skill and reliability. The addition of environmental information improved the reliability and the critical success index (CSI) while slightly degrading the area under the receiver operating characteristic (ROC) curve across all UH thresholds and σ values. The probabilities accurately reflected the location of tornado reports, and three case studies demonstrate value to forecasters. Based on initial tests, four sets of tornado probabilities were chosen for evaluation by participants in the 2015 National Oceanic and Atmospheric Administration’s Hazardous Weather Testbed Spring Forecasting Experiment from 4 May to 5 June 2015. Participants found the probabilities useful and noted an overforecasting tendency.

scholarly journals Objective Classification of Tornadic and Nontornadic Severe Weather Outbreaks

2009 ◽  
Vol 137 (12) ◽  
pp. 4355-4368 ◽  
Author(s):  
Andrew E. Mercer ◽  
Chad M. Shafer ◽  
Charles A. Doswell ◽  
Lance M. Leslie ◽  
Michael B. Richman
Keyword(s):  
Statistical Methods ◽  
Lead Time ◽  
Weather Prediction ◽  
The United States ◽  
Severe Weather ◽  
Probability Of Detection ◽  
Support Vector ◽  
Synoptic Scale ◽  
Skill Scores ◽  
Objective Classification

Abstract Tornadoes often strike as isolated events, but many occur as part of a major outbreak of tornadoes. Nontornadic outbreaks of severe convective storms are more common across the United States but pose different threats than do those associated with a tornado outbreak. The main goal of this work is to distinguish between significant instances of these outbreak types objectively by using statistical modeling techniques on numerical weather prediction output initialized with synoptic-scale data. The synoptic-scale structure contains information that can be utilized to discriminate between the two types of severe weather outbreaks through statistical methods. The Weather Research and Forecast model (WRF) is initialized with synoptic-scale input data (the NCEP–NCAR reanalysis dataset) on a set of 50 significant tornado outbreaks and 50 nontornadic severe weather outbreaks. Output from the WRF at 18-km grid spacing is used in the objective classification. Individual severe weather parameters forecast by the model near the time of the outbreak are analyzed from simulations initialized at 24, 48, and 72 h prior to the outbreak. An initial candidate set of 15 variables expected to be related to severe storms is reduced to a set of 6 or 7, depending on lead time, that possess the greatest classification capability through permutation testing. These variables serve as inputs into two statistical methods, support vector machines and logistic regression, to classify outbreak type. Each technique is assessed based on bootstrap confidence limits of contingency statistics. An additional backward selection of the reduced variable set is conducted to determine which variable combination provides the optimal contingency statistics. Results for the contingency statistics regarding the verification of discrimination capability are best at 24 h; at 48 h, modest degradation is present. By 72 h, the contingency statistics decline by up to 15%. Overall, results are encouraging, with probability of detection values often exceeding 0.8 and Heidke skill scores in excess of 0.7 at 24-h lead time.

scholarly journals Horizontal divergence of typhoon-generated gravity waves in the upper troposphere and lower stratosphere (UTLS) and its influence on typhoon evolution

2014 ◽  
Vol 14 (7) ◽  
pp. 3175-3182 ◽  
Author(s):  
S. H. Kim ◽  
H.-Y. Chun ◽  
W. Jang
Keyword(s):  
Gravity Waves ◽  
Lower Stratosphere ◽  
Inner Core ◽  
Wrf Model ◽  
Weather Research And Forecasting ◽  
Sea Level Pressure ◽  
Upper Troposphere ◽  
Convective Clouds ◽  
Power Spectral ◽  
Spiral Rainbands

Abstract. The characteristics of horizontal divergence induced by typhoon-generated gravity waves (HDTGWs) and the influence of HDTGW on typhoon evolution are investigated based on the simulation results of Typhoon Saomai (2006) using the Weather Research and Forecasting (WRF) model. The power spectral density of HDTGW shows dominant powers at horizontal wavelengths of 20–30 km and at periods of less than 1 h. This is associated with gravity waves generated by vigorous convective clouds in an inner core region of the typhoon. However, the domain-averaged HDTGW in the upper troposphere and lower stratosphere had a spectral peak at 24 h, which is well correlated with the minimum sea-level pressure of the typhoon, especially during a rapidly developing period. The 24 h period of the averaged HDTGW stems from the inertia–gravity waves generated by the convective clouds in the spiral rainbands, and showed no clear association with the thermal tides or the diurnal variation of precipitation.

scholarly journals Does Increasing Model Resolution Improve the Real-Time Forecasts of Western North Pacific Tropical Cyclones?

Atmosphere ◽  
2021 ◽  
Vol 12 (6) ◽  
pp. 776
Author(s):  
Jihong Moon ◽  
Jinyoung Park ◽  
Dong-Hyun Cha
Keyword(s):  
Large Scale ◽  
Wrf Model ◽  
Horizontal Resolution ◽  
Lower Latitude ◽  
Weather Research And Forecasting ◽  
Track Forecast ◽  
Forecast Errors ◽  
Test Cases ◽  
Model Resolution ◽  
Sea Level Pressure

In this study, the general impact of high-resolution moving nesting domains on tropical cyclone (TC) intensity and track forecasts was verified, for a total of 107 forecast cases of 33 TCs, using the Weather Research and Forecasting (WRF) model. The experiment, with a coarse resolution of 12 km, could not significantly capture the intensification process, especially for maximum intensities (>60 m s−1). The intense TCs were better predicted by experiments using a moving nesting domain with a horizontal resolution of 4 km. The forecast errors for maximum wind speed and minimum sea-level pressure decreased in the experiment with higher resolution; the forecast of lifetime maximum intensity was improved. For the track forecast, the experiment with a coarser resolution tended to simulate TC tracks deviating rightward to the TC motions in the best-track data; this erroneous deflection was reduced in the experiment with a higher resolution. In particular, the track forecast in the experiment with a higher resolution improved more frequently for intense TCs that were generally distributed at relatively lower latitudes among the test cases. The sensitivity of the track forecast to the model resolution was relatively significant for lower-latitude TCs. On the other hand, the track forecasts of TCs moving to the mid-latitudes, which were primarily influenced by large-scale features, were not sensitive to the resolution.

Sensitivity analysis to WRF parameterizations for mountain waves near Madrid airport (Spain)

2021 ◽  
Author(s):  
Javier Díaz Fernández ◽  
Lara Quitián Hernández ◽  
Pedro Bolgiani ◽  
Daniel Santos Muñoz ◽  
Mariano Sastre ◽  
...  
Keyword(s):  
Sensitivity Analysis ◽  
Brightness Temperature ◽  
Traffic Management ◽  
Satellite Images ◽  
Wrf Model ◽  
Weather Research And Forecasting ◽  
Leeward Side ◽  
Aircraft Icing ◽  
Mountain Waves ◽  
Skill Scores

<p>Aircraft icing and turbulence associated with mountain waves events are adverse meteorological phenomena potentially affecting aviation safety and air traffic management. This study analyzes 13 mountain wave events in the vicinity of the Adolfo Suárez Madrid-Barajas airport (Spain) for two years (from 2017 to 2019). Mountain waves are formed in the leeward side of the Guadarrama mountains when the wind flows perpendicular to this orographic barrier (north-northwest winds). The thirteen events are simulated using several parameterizations from the Weather Research and Forecasting (WRF) model. Simulated pseudo-satellite images are validated using the observed brightness temperature from satellite images. Then, a sensitivity analysis is developed through several skill scores applied to brightness temperature in order to select the schemes best performing to forecast mountain waves. Finally, the best parametrization is used to assess several atmospheric variables involved in mountain waves formation. </p><p> </p>

Sign in / Sign up

Export Citation Format

Share Document