Severe weather phenomena research with the WRF model

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.

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Examining Terrain Effects on an Upstate New York Tornado Event Utilizing a High-Resolution Model Simulation

Weather and Forecasting ◽

10.1175/waf-d-21-0018.1 ◽

2021 ◽

Author(s):

Luke J. LeBel ◽

Brian H. Tang ◽

Ross A. Lazear

Keyword(s):

New York ◽

High Resolution ◽

Wind Shear ◽

Model Simulation ◽

Wrf Model ◽

Severe Weather ◽

Streamwise Vorticity ◽

Low Level ◽

Severe Convection ◽

The Impact

AbstractThe complex terrain at the intersection of the Mohawk and Hudson valleys of New York has an impact on the development and evolution of severe convection in the region. Specifically, previous research has concluded that terrain-channeled flow in the Mohawk and Hudson valleys likely contributes to increased low-level wind shear and instability in the valleys during severe weather events such as the historic 31 May 1998 event that produced a strong (F3) tornado in Mechanicville, New York.The goal of this study is to further examine the impact of terrain channeling on severe convection by analyzing a high-resolution WRF model simulation of the 31 May 1998 event. Results from the simulation suggest that terrain-channeled flow resulted in the localized formation of an enhanced low-level moisture gradient, resembling a dryline, at the intersection of the Mohawk and Hudson valleys. East of this boundary, the environment was characterized by stronger low-level wind shear and greater low-level moisture and instability, increasing tornadogenesis potential. A simulated supercell intensified after crossing the boundary, as the larger instability and streamwise vorticity of the low-level inflow was ingested into the supercell updraft. These results suggest that terrain can have a key role in producing mesoscale inhomogeneities that impact the evolution of severe convection. Recognition of these terrain-induced boundaries may help in anticipating where the risk of severe weather may be locally enhanced.

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Examination of Convection-Allowing Configurations of the WRF Model for the Prediction of Severe Convective Weather: The SPC/NSSL Spring Program 2004

Weather and Forecasting ◽

10.1175/waf906.1 ◽

2006 ◽

Vol 21 (2) ◽

pp. 167-181 ◽

Cited By ~ 148

Author(s):

John S. Kain ◽

S. J. Weiss ◽

J. J. Levit ◽

M. E. Baldwin ◽

D. R. Bright

Keyword(s):

Weather Forecasting ◽

Wrf Model ◽

Value Added ◽

Severe Weather ◽

Convective Initiation ◽

A Value ◽

Human Decision ◽

Forecast Models ◽

Convective Weather ◽

The Impact

Abstract Convection-allowing configurations of the Weather Research and Forecast (WRF) model were evaluated during the 2004 Storm Prediction Center–National Severe Storms Laboratory Spring Program in a simulated severe weather forecasting environment. The utility of the WRF forecasts was assessed in two different ways. First, WRF output was used in the preparation of daily experimental human forecasts for severe weather. These forecasts were compared with corresponding predictions made without access to WRF data to provide a measure of the impact of the experimental data on the human decision-making process. Second, WRF output was compared directly with output from current operational forecast models. Results indicate that human forecasts showed a small, but measurable, improvement when forecasters had access to the high-resolution WRF output and, in the mean, the WRF output received higher ratings than the operational Eta Model on subjective performance measures related to convective initiation, evolution, and mode. The results suggest that convection-allowing models have the potential to provide a value-added benefit to the traditional guidance package used by severe weather forecasters.

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A comparison between 4D-VAR and cycling 3D-VAR methods for the simulation of a severe weather event in Central Italy. Preliminary results.

10.5194/egusphere-egu2020-7228 ◽

2020 ◽

Author(s):

Vincenzo Mazzarella ◽

Rossella Ferretti

Keyword(s):

Positive Impact ◽

Central Italy ◽

Wrf Model ◽

Severe Weather ◽

Precipitation Forecast ◽

Mountain Range ◽

Weather Event ◽

Maximum Rainfall ◽

The Impact ◽

Severe Weather Event

Nowadays, the use of 4D-VAR assimilation technique has been investigated in several scientific papers with the aim of improving the localization and timing of precipitation in complex orography regions. The results show the positive impact in rainfall forecast but, the need to resolve the tangent linear and adjoint model makes the 4D-VAR computationally too expensive. Hence, it is used in operationally only in large forecast centres. To the aim of exploring a more reasonable method, a comparison between a cycling 3D-VAR, that needs less computational resources, and 4D-VAR techniques is performed for a severe weather event occurred in Central Italy. A cut-off low (992 hPa), located in western side of Sicily region, was associated with a strong south-easterly flow over Central Adriatic region, which supplied a large amount of warm and moist air. This mesoscale configuration, coupled with the Apennines mountain range that further increased the air column instability, produced heavy rainfall in Abruzzo region (Central Italy).The numerical simulations are carried out using the Weather Research and Forecasting (WRF) model. In-situ surface and upper-air observations are assimilated in combination with radar reflectivity and radial velocity data over a high-resolution domain. Several experiments have been performed in order to evaluate the impact of 4D-VAR and cycling 3D-VAR in the precipitation forecast. In addition, a statistical analysis has been carried out to objectively compare the simulations. Two different verification approaches are used: Receiver Operating Characteristic (ROC) curve and Fraction Skill Score (FSS). Both statistical scores are calculated for different threshold values in the study area and in the sub-regions where the maximum rainfall occurred.

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Forecasting Tornadoes Using Convection-Permitting Ensembles

Weather and Forecasting ◽

10.1175/waf-d-15-0134.1 ◽

2016 ◽

Vol 31 (1) ◽

pp. 273-295 ◽

Cited By ~ 36

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.

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Evaluation of WRF Model Output for Severe Weather Forecasting from the 2008 NOAA Hazardous Weather Testbed Spring Experiment

Weather and Forecasting ◽

10.1175/2009waf2222258.1 ◽

2010 ◽

Vol 25 (2) ◽

pp. 408-427 ◽

Cited By ~ 46

Author(s):

Michael C. Coniglio ◽

Kimberly L. Elmore ◽

John S. Kain ◽

Steven J. Weiss ◽

Ming Xue ◽

...

Keyword(s):

Weather Forecasting ◽

Mesoscale Model ◽

Wrf Model ◽

Vertical Wind Shear ◽

The United States ◽

Severe Weather ◽

High Plains ◽

Cold Bias ◽

Deterministic Framework ◽

Hazardous Weather

Abstract This study assesses forecasts of the preconvective and near-storm environments from the convection-allowing models run for the 2008 National Oceanic and Atmospheric Administration (NOAA) Hazardous Weather Testbed (HWT) spring experiment. Evaluating the performance of convection-allowing models (CAMs) is important for encouraging their appropriate use and development for both research and operations. Systematic errors in the CAM forecasts included a cold bias in mean 2-m and 850-hPa temperatures over most of the United States and smaller than observed vertical wind shear and 850-hPa moisture over the high plains. The placement of airmass boundaries was similar in forecasts from the CAMs and the operational North American Mesoscale (NAM) model that provided the initial and boundary conditions. This correspondence contributed to similar characteristics for spatial and temporal mean error patterns. However, substantial errors were found in the CAM forecasts away from airmass boundaries. The result is that the deterministic CAMs do not predict the environment as well as the NAM. It is suggested that parameterized processes used at convection-allowing grid lengths, particularly in the boundary layer, may be contributing to these errors. It is also shown that mean forecasts from an ensemble of CAMs were substantially more accurate than forecasts from deterministic CAMs. If the improvement seen in the CAM forecasts when going from a deterministic framework to an ensemble framework is comparable to improvements in mesoscale model forecasts when going from a deterministic to an ensemble framework, then an ensemble of mesoscale model forecasts could predict the environment even better than an ensemble of CAMs. Therefore, it is suggested that the combination of mesoscale (convection parameterizing) and CAM configurations is an appropriate avenue to explore for optimizing the use of limited computer resources for severe weather forecasting applications.

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Initial results of the project SINOPTICA (Satellite-borne and IN-situ Observations to Predict The Initiation of Convection for ATM)

10.5194/ems2021-189 ◽

2021 ◽

Author(s):

Laura Esbri ◽

Maria Carmen Llasat ◽

Tomeu Rigo ◽

Massimo Milelli ◽

Vincenzo Mazzarella ◽

...

Keyword(s):

Weather Prediction ◽

Wrf Model ◽

Remote Sensing Data ◽

Weather Forecast ◽

Relevant Information ◽

Air Traffic ◽

Severe Weather ◽

Radar Images ◽

Weather Events ◽

Adverse Weather

In the framework of the SINOPTICA project (EU H2020 SESAR, 2020 &#8211; 2022), different meteorological forecasting techniques are being tested to better nowcast severe weather events affecting Air Traffic Management (ATM) operations. Short-range severe weather forecasts with very high spatial resolution will be obtained starting from radar images, through an application of nowcasting techniques combined with Numerical Weather Prediction (NWP) model with data assimilation. The final goal is to integrate compact nowcast information into an Arrival Manager to support Air Traffic Controllers (ATCO) when sequencing and guiding approaching aircraft even in adverse weather situations. The guidance-support system will enable the visualization of dynamic weather information on the radar display of the controller, and the 4D-trajectory calculation for diversion coordination around severe weather areas. This meteorological information must be compact and concise to not interfere with other relevant information on the radar display of the controller.Three severe weather events impacting different Italian airports have been selected for a preliminary radar analysis. Some products are considered for obtaining the best radar approach to characterize the severity of the events for ATM interests. Combining the Vertical Integrated Liquid and the Echo Top Maximum products, hazard thresholds are defined for different domains around the airports. The Weather Research and Forecasting (WRF) model has been used to simulate the formation and development of the aforementioned convective events. In order to produce a more accurate very short-term weather forecast (nowcasting), remote sensing data (e.g. radar, GNSS) and conventional observations are assimilated by using a cycling three-dimensional variational technique. This contribution presents some preliminary results on the progress of the project.

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A Real-Time Convection-Allowing Ensemble Prediction System Initialized by Mesoscale Ensemble Kalman Filter Analyses

Weather and Forecasting ◽

10.1175/waf-d-15-0013.1 ◽

2015 ◽

Vol 30 (5) ◽

pp. 1158-1181 ◽

Cited By ~ 41

Author(s):

Craig S. Schwartz ◽

Glen S. Romine ◽

Morris L. Weisman ◽

Ryan A. Sobash ◽

Kathryn R. Fossell ◽

...

Keyword(s):

Kalman Filter ◽

Real Time ◽

Ensemble Kalman Filter ◽

Wrf Model ◽

Severe Weather ◽

Ensemble Prediction ◽

Limited Area ◽

Ensemble Forecasts ◽

Ensemble Prediction System ◽

Weather Events

Abstract In May and June 2013, the National Center for Atmospheric Research produced real-time 48-h convection-allowing ensemble forecasts at 3-km horizontal grid spacing using the Weather Research and Forecasting (WRF) Model in support of the Mesoscale Predictability Experiment field program. The ensemble forecasts were initialized twice daily at 0000 and 1200 UTC from analysis members of a continuously cycling, limited-area, mesoscale (15 km) ensemble Kalman filter (EnKF) data assimilation system and evaluated with a focus on precipitation and severe weather guidance. Deterministic WRF Model forecasts initialized from GFS analyses were also examined. Subjectively, the ensemble forecasts often produced areas of intense convection over regions where severe weather was observed. Objective statistics confirmed these subjective impressions and indicated that the ensemble was skillful at predicting precipitation and severe weather events. Forecasts initialized at 1200 UTC were more skillful regarding precipitation and severe weather placement than forecasts initialized 12 h earlier at 0000 UTC, and the ensemble forecasts were typically more skillful than GFS-initialized forecasts. At times, 0000 UTC GFS-initialized forecasts had temporal distributions of domain-average rainfall closer to observations than EnKF-initialized forecasts. However, particularly when GFS analyses initialized WRF Model forecasts, 1200 UTC forecasts produced more rainfall during the first diurnal maximum than 0000 UTC forecasts. This behavior was mostly attributed to WRF Model initialization of clouds and moist physical processes. The success of these real-time ensemble forecasts demonstrates the feasibility of using limited-area continuously cycling EnKFs as a method to initialize convection-allowing ensemble forecasts, and future real-time high-resolution ensemble development leveraging EnKFs seems justified.

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4DVAR assimilation of GNSS zenith path delays and precipitable water into a numerical weather prediction model WRF

Atmospheric Measurement Techniques ◽

10.5194/amt-12-345-2019 ◽

2019 ◽

Vol 12 (1) ◽

pp. 345-361 ◽

Cited By ~ 8

Author(s):

Witold Rohm ◽

Jakub Guzikowski ◽

Karina Wilgan ◽

Maciej Kryza

Keyword(s):

Data Assimilation ◽

Numerical Weather Prediction ◽

Vertical Profile ◽

Weather Prediction ◽

Wrf Model ◽

Precipitable Water ◽

Severe Weather ◽

Atmospheric Measurements ◽

Numerical Weather ◽

Gnss Data

Abstract. The GNSS data assimilation is currently widely discussed in the literature with respect to the various applications for meteorology and numerical weather models. Data assimilation combines atmospheric measurements with knowledge of atmospheric behavior as codified in computer models. With this approach, the “best” estimate of current conditions consistent with both information sources is produced. Some approaches also allow assimilating the non-prognostic variables, including remote sensing data from radar or GNSS (global navigation satellite system). These techniques are named variational data assimilation schemes and are based on a minimization of the cost function, which contains the differences between the model state (background) and the observations. The variational assimilation is the first choice for data assimilation in the weather forecast centers, however, current research is consequently looking into use of an iterative, filtering approach such as an extended Kalman filter (EKF). This paper shows the results of assimilation of the GNSS data into numerical weather prediction (NWP) model WRF (Weather Research and Forecasting). The WRF model offers two different variational approaches: 3DVAR and 4DVAR, both available through the WRF data assimilation (WRFDA) package. The WRFDA assimilation procedure was modified to correct for bias and observation errors. We assimilated the zenith total delay (ZTD), precipitable water (PW), radiosonde (RS) and surface synoptic observations (SYNOP) using a 4DVAR assimilation scheme. Three experiments have been performed: (1) assimilation of PW and ZTD for May and June 2013, (2) assimilation of PW alone; PW, with RS and SYNOP; ZTD alone; and finally ZTD, with RS and SYNOP for 5–23 May 2013, and (3) assimilation of PW or ZTD during severe weather events in June 2013. Once the initial conditions were established, the forecast was run for 24 h. The major conclusion of this study is that for all analyzed cases, there are two parameters significantly changed once GNSS data are assimilated in the WRF model using GPSPW operator and these are moisture fields and rain. The GNSS observations improves forecast in the first 24 h, with the strongest impact starting from a 9 h lead time. The relative humidity forecast in a vertical profile after assimilation of ZTD shows an over 20 % decrease of mean error starting from 2.5 km upward. Assimilation of PW alone does not bring such a spectacular improvement. However, combination of PW, SYNOP and radiosonde improves distribution of humidity in the vertical profile by maximum of 12 %. In the three analyzed severe weather cases PW always improved the rain forecast and ZTD always reduced the humidity field bias. Binary rain analysis shows that GNSS parameters have significant impact on the rain forecast in the class above 1 mm h−1.

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Satellite-Based Study and Numerical Forecasting of Two Tornado Outbreaks in the Ural Region in June 2017

Atmosphere ◽

10.3390/atmos11111146 ◽

2020 ◽

Vol 11 (11) ◽

pp. 1146

Author(s):

Alexander Chernokulsky ◽

Andrey Shikhov ◽

Alexey Bykov ◽

Igor Azhigov

Keyword(s):

Weather Prediction ◽

Wrf Model ◽

Atmospheric Model ◽

Polar Front ◽

Severe Weather ◽

Ural Region ◽

Scale Analysis ◽

Ural Mountains ◽

Synoptic Conditions ◽

Tornado Outbreaks

Strong tornadoes are common for the European part of Russia but happen rather rare east of the Urals. June 2017 became an exceptional month when two tornado outbreaks occurred in the Ural region of Russia, yielded $3 million damage, and resulted in 1 fatality and 14 injuries. In this study, we performed detailed analysis of these outbreaks with different data. Tornadoes and tornado-related environments were diagnosed with news and eyewitness reports, ground-based meteorological observations, sounding data, global numerical weather prediction (NWP) models data, synoptic charts, satellite images, and data of specially conducted aerial imaging. We also estimated the accuracy of short-term forecasting of outbreaks with the WRF-ARW mesoscale atmospheric model, which was run in convection-permitting mode. We determined the formation of 28 tornadoes during the first outbreak (3 June 2017) and 9 tornadoes during the second outbreak (18 June 2017). We estimated their intensity using three different approaches and confirmed that, based on the International Fujita scale (IF), one of the tornadoes had the IF4 intensity, being the first IF4 tornado in Russia in the 21st century and the first-ever IF4 tornado reported beyond the Ural Mountains. The synoptic-scale analysis revealed the similarity of two outbreaks, which both formed near the polar front in the warm part of deepening southern cyclones. Such synoptic conditions yield mostly weak tornadoes in European Russia; however, our analysis indicates that these conditions are likely favorable for strong tornadoes over the Ural region. Meso-scale analysis indicates that the environments were favorable for tornado formation in both cases, and most severe-weather indicators exceeded their critical values. Our analysis demonstrates that for the Ural region, like for other regions of the world, combined use of the global NWP model outputs indicating high values of severe-weather indices and the WRF model forecast outputs explicitly simulating tornadic storm formation could be used to predict the high probability of strong tornado formation. For both analyzed events, the availability of such tornado warning forecast could help local authorities to take early actions on population protection.

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