Christopher A. Kerr

Cooperative Institute for Mesoscale Meteorological Studies

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Brian C. Matilla

Cooperative Institute for Mesoscale Meteorological Studies

Nusrat Yussouf

NOAA National Severe Storms Laboratory

Patrick S. Skinner

NOAA National Severe Storms Laboratory

Xuguang Wang

University of Oklahoma

Derek R. Stratman

Cooperative Institute for Mesoscale Meteorological Studies

Yunheng Wang

Cooperative Institute for Mesoscale Meteorological Studies

Thomas A. Jones

NOAA National Severe Storms Laboratory

Louis J. Wicker

NOAA National Severe Storms Laboratory

J.E. Castillo Guerra

NOAA National Severe Storms Laboratory

Pamela L. Heinselman

University of Oklahoma

Cooperative Institutions

University of Oklahoma

Cooperative Institute for Mesoscale Meteorological Studies

NOAA National Severe Storms Laboratory

National Oceanic and Atmospheric Administration

NOAA Storm Prediction Center

NOAA National Weather Service

NOAA National Centers for Environmental Prediction

NOAA Earth System Research Laboratory

NSF National Center for Atmospheric Research

Pennsylvania State University

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Results from a Pseudo-Real-Time Next-Generation 1-km Warn-on-Forecast System Prototype

Weather and Forecasting (2022)

Christopher A. Kerr Brian C. Matilla Ya Ping Wang Derek R. Stratman Thomas A. Jones

Abstract Since 2017, the Warn-on-Forecast System (WoFS) has been tested and evaluated during the Hazardous Weather Testbed Spring Forecasting Experiment (SFE) and summer convective seasons. The system has shown promise in predicting high temporal and spatial specificity of individual evolving thunderstorms. However, this baseline version of the WoFS has a 3-km horizontal grid spacing and cannot resolve some convective processes. Efforts are under way to develop a WoFS prototype at a 1-km grid spacing (WoFS-1km) with the hope to improve forecast accuracy. This requires extensive changes to data assimilation specifications and observation processing parameters. A preliminary version of WoFS-1km nested within WoFS at 3 km (WoFS-3km) was developed, tested, and run during the 2021 SFE in pseudo–real time. Ten case studies were successfully completed and provided simulations of a variety of convective modes. The reflectivity and rotation storm objects from WoFS-1km are verified against both WoFS-3km and 1-km forecasts initialized from downscaled WoFS-3km analyses using both neighborhood- and object-based techniques. Neighborhood-based verification suggests WoFS-1km improves reflectivity bias but not spatial placement. The WoFS-1km object-based reflectivity forecast accuracy is higher in most cases, leading to a net improvement. Both the WoFS-1km and downscaled forecasts have ideal reflectivity object frequency biases while the WoFS-3km overpredicts the number of reflectivity objects. The rotation object verification is ambiguous as many cases are negatively impacted by 1-km data assimilation. This initial evaluation of a WoFS-1km prototype is a solid foundation for further development and future testing. Significance Statement This study investigates the impacts of performing data assimilation directly on a 1-km WoFS model grid. Most previous studies have only initialized 1-km WoFS forecasts from coarser analyses. The results demonstrate some improvements to reflectivity forecasts through data assimilation on a 1-km model grid although finer resolution data assimilation did not improve rotation forecasts.

10.1175/waf-d-22-0080.1

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Citations (1)

Ensemble-Based Targeted Observation Method Applied to Radar Radial Velocity Observations on Idealized Supercell Low-Level Rotation Forecasts: A Proof of Concept

Monthly Weather Review (2019)

Christopher A. Kerr Xuguang Wang

Abstract The potential future installation of a multifunction phased-array radar (MPAR) network will provide capabilities of case-specific adaptive scanning. Knowing the impacts adaptive scanning may have on short-term forecasts will influence scanning strategy decision-making in hopes to produce the most optimal ensemble forecast while also benefiting human severe weather warning decision-making. An ensemble-based targeted observation algorithm is applied to an observing system simulation experiment (OSSE) where the impacts of synthetic idealized supercell radial velocity observations are estimated before the observations are “collected” and assimilated. The forecast metric of interest is the low-level rotation forecast metric (0–1-km updraft helicity), a surrogate for tornado prediction. It is found that the ensemble-based targeted observation approach can reasonably estimate the true error variance reduction when an effective method that treats sampling error is applied, the period of model forecast is associated with less degrees of nonlinearity, and the observation information content relative to the background forecast is larger. In some scenarios, a subset of a full-volume scan assimilation produces better forecasts than all observations within the full volume. Assimilating the full-volume scan increases the number of potential spurious correlations arising between the forecast metric and radial velocity observation induced state perturbations, which may degrade the forecast metric accuracy.

Spurious relationship

Supercell

Forecast verification

10.1175/mwr-d-19-0197.1

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Citations (10)

Storm-Scale Predictability and Analysis of the 13 April 2020 Central Savannah River Area Tornado Outbreak

Weather and Forecasting (2022)

Christopher A. Kerr Frank Alsheimer

Abstract An early morning tornado outbreak occurred on 13 April 2020 in the Central Savannah River Area. Multiple significant tornadoes were reported, resulting in fatalities and injuries. While the operational tornado warnings had positive lead times, the convective mode (quasi-linear convective system) increased the warning decision complexity. The timing of the event [0500–0600 local time (LT)] also made NWS-to-public communication difficult. The experimental NSSL Warn-on-Forecast System (WoFS) was run retrospectively for this case. The WoFS consists of 3–6-h ensemble forecasts initialized every 30 min, and the goals of the system are to bridge the gap between severe weather watches and warnings and to increase warning lead times. Multiple WoFS forecasts were initialized leading up to the first tornado report; those initialized prior to tornado warning issuance have high ensemble probabilities of low-level rotation in the appropriate areas based on subsequent tornado reports. This case highlights another example of the usefulness of WoFS before its eventual transition to operations. Using the WoFS forecasts, kinematic and thermodynamic storm–environment relationships are analyzed using ensemble sensitivity analysis (ESA). The analyses suggest variations in the mesoscale environmental vertical wind profile are not as influential on mesovortex intensity as variations in the thermodynamic environment. Surface observations recorded prior to the tornado outbreak reveal subtle temperature and moisture gradients that may be the impetus for mesovortex intensification and tornadogenesis.

Predictability

Convective storm detection

Severe weather

10.1175/waf-d-21-0185.1

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Citations (7)

Diagnosing Convective Dependencies on Near-Storm Environments Using Ensemble Sensitivity Analyses

Monthly Weather Review (2018)

Christopher A. Kerr David J. Stensrud Xuguang Wang

Abstract Convection intensity and longevity is highly dependent on the surrounding environment. Ensemble sensitivity analysis (ESA), which quantitatively and qualitatively interprets impacts of initial conditions on forecasts, is applied to very short-term (1–2 h) convective-scale forecasts for three cases during the Mesoscale Predictability Experiment (MPEX) in 2013. The ESA technique reveals several dependencies of individual convective storm evolution on their nearby environments. The three MPEX cases are simulated using a previously verified 36-member convection-allowing model (Δx = 3 km) ensemble created via the Weather Research and Forecasting (WRF) Model. Radar and other conventional observations are assimilated using an ensemble adjustment Kalman filter. The three cases include a mesoscale convective system (MCS) and both nontornadic and tornadic supercells. Of the many ESAs applied in this study, one of the most notable is the positive sensitivity of supercell updraft helicity to increases in both storm inflow region deep and shallow vertical wind shear. This result suggests that larger values of vertical wind shear within the storm inflow yield higher values of storm updraft helicity. Results further show that the supercell storms quickly enhance the environmental vertical wind shear within the storm inflow region. Application of ESA shows that these storm-induced perturbations then affect further storm evolution, suggesting the presence of storm–environment feedback cycles where perturbations affect future mesocyclone strength. Overall, ESA can provide insight into convection dependencies on the near-storm environment.

Supercell

Convective storm detection

Inflow

Mesocyclone

Mesoscale convective system

Severe weather

10.1175/mwr-d-18-0140.1

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Citations (12)

Using near-storm soundings from the Mesoscale Predictability Experiment to analyze convection permitting model accuracy: 28 May 2013

27th Conference on Severe Local Storms (2014)

Christopher A. Kerr

Predictability

Source

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Citations (0)

Evaporative controls on afternoon convection in the NARR dataset and in GFDL models

AGU Fall Meeting Abstracts (2010)

Kirsten L. Findell Pierre Gentine Benjamin R. Lintner Christopher A. Kerr

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Updraft-Based Adaptive Assimilation of Radial Velocity Observations in a Warn-on-Forecast System

Weather and Forecasting (2020)

Christopher A. Kerr Louis J. Wicker Patrick S. Skinner

Abstract The Warn-on-Forecast system (WoFS) provides short-term, probabilistic forecasts of severe convective hazards including tornadoes, hail, and damaging winds. WoFS initial conditions are created through frequent assimilation of radar (reflectivity and radial velocity), satellite, and in situ observations. From 2016 to 2018, 5-km radial velocity Cressman superob analyses were created to reduce the observation counts and subsequent assimilation computational costs. The superobbing procedure smooths the radial velocity and subsequently fails to accurately depict important storm-scale features such as mesocyclones. This study retrospectively assimilates denser, 3-km radial velocity analyses in lieu of the 5-km analyses for eight case studies during the spring of 2018. Although there are forecast improvements during and shortly after convection initiation, 3-km analyses negatively impact forecasts initialized when convection is ongoing, as evidenced by model failure and initiation of spurious convection. Therefore, two additional experiments are performed using adaptive assimilation of 3-km radial velocity observations. Initially, an updraft variance mask is applied that limits radial velocity assimilation to areas where the observations are more likely to be beneficial. This experiment reduces spurious convection as well as the number of observations assimilated, in some cases even below that of the 5-km analysis experiments. The masking, however, eliminates an advantage of 3-km radial velocity assimilation for convection initiation timing. This problem is mitigated by additionally assimilating 3-km radial velocity observations in locations where large differences exist between the observed and ensemble-mean reflectivity fields, which retains the benefits of the denser radial velocity analyses while reducing the number of observations assimilated.

Radial velocity

Spurious relationship

Assimilation (phonology)

10.1175/waf-d-19-0251.1

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Citations (4)

Significance of environmental vertical wind shear in convective upscale growth: Observations and model analyses

16th Conference on Mesoscale Processes (2015)

Christopher A. Kerr

Thermal wind

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Verification of Convection-Allowing Model Ensemble Analyses of Near-Storm Environments Using MPEX Upsonde Observations

Monthly Weather Review (2016)

Christopher A. Kerr David J. Stensrud Xuguang Wang

The Mesoscale Predictability Experiment (MPEX) conducted during the spring of 2013 included frequent coordinated sampling of near-storm environments via upsondes. These unique observations were taken to better understand the upscale effects of deep convection on the environment, and are used to validate the accuracy of convection-allowing (Δ x = 3 km) model ensemble analyses. A 36-member ensemble was created with physics diversity using the Weather Research and Forecasting Model, and observations were assimilated via the Data Assimilation Research Testbed using an ensemble adjustment Kalman filter. A 4-day sequence of convective events from 28 to 31 May 2013 in the south-central United States was analyzed by assimilating Doppler radar and conventional observations. No MPEX upsonde observations were assimilated. Since the ensemble mean analyses produce an accurate depiction of the storms, the MPEX observations are used to verify the accuracy of the analyses of the near-storm environment. A total of 81 upsondes were released over the 4-day period, sampling different regions of near-storm environments including storm inflow, outflow, and anvil. The MPEX observations reveal modest analysis errors overall when considering all samples, although specific environmental regions reveal larger errors in some state fields. The ensemble analyses underestimate cold pool depth, and storm inflow meridional winds have a pronounced northerly bias that results from an underprediction of inflow wind speed magnitude. Most bias distributions are Gaussian-like, with a few being bimodal owing to systematic biases of certain state fields and environmental regions.

Inflow

Convective storm detection

Predictability

Outflow

Ensemble forecasting

Severe weather

10.1175/mwr-d-16-0287.1

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Citations (8)

Impact of Assimilating Future Clear-Air Radial Velocity Observations from Phased-Array Radar on a Supercell Thunderstorm Forecast: An Observing System Simulation Experiment Study

Monthly Weather Review (2020)

Yongjie Huang Xuguang Wang Christopher A. Kerr Andrew Mahre Tian‐You Yu

Abstract Phased-array radar (PAR) technology offers the flexibility of sampling the storm and clear-air regions with different update times. As such, the radial velocity from clear-air regions, typically with a lower signal-to-noise ratio, can be measured more accurately. In this work, observing system simulation experiments are conducted to explore the potential value of assimilating clear-air radial velocity observations to improve numerical prediction of supercell thunderstorms. Synthetic PAR observations of a splitting supercell are assimilated at different life cycle stages using an ensemble Kalman filter. Results show that assimilating environmental clear-air radial velocity can reduce wind errors in the near-storm environment and within the precipitation region. Improvements in the forecast are seen at different stages, especially for the forecast after 30 min. After assimilating clear-air radial velocity observations, the probabilities of updraft helicity and precipitation within the corresponding swaths of the truth simulation increase up to 30%–40%. Additional diagnostics suggest that the more accurate track forecast, stronger vertical motion, and better-maintained supercell can be attributed to the better analysis and prediction of the mean environmental winds and linear and nonlinear dynamic forces. Consequently, assimilating clear-air radial velocity produces accurate storm structure (rotating updrafts), updraft size, and storm track, and improves the surface accumulated precipitation forecast. The performance of forecasts with a higher frequency of assimilating clear-air radial velocity does not show systematic improvement. These results highlight the potential of assimilating clear-air radial velocity observations to improve numerical weather prediction forecasts of supercell thunderstorms.

Supercell

Radial velocity

Mesocyclone

10.1175/mwr-d-19-0391.1

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Citations (7)