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Christopher A. Kerr and 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.

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Christopher A. Kerr and 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 (5-6 AM local time) 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-hour ensemble forecasts initialized every 30 minutes, 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.

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Yoshio Kurihara, Christopher L. Kerr, and Morris A. Bender

Abstract

A numerical scheme proposed by Kurihara and Bender is modified so as to improve the behavior of open lateral boundaries of a regional model. In the new scheme, both the local values and the gradients of fields from a larger model are used to define the time-dependent reference values toward which the boundary gridpoint values of the regional model prediction are relaxed at each step of the model integration. Use of the gradients in the boundary forcing imposes constraints on the vorticity, divergence and baroclinicity fields for the regional model. The relaxation time of forcing is set to be short for the normal component of wind. For other variables, the relaxation time at a given boundary gridpoint depends on the wind direction at that gridpoint, with a minimum at a point of normal inflow and a maximum at a point of normal outflow. The forcing strength is reduced in the planetary boundary layer so that the boundary layer structure is determined mainly by the surface condition of the regional model. Also, a simple method to control the total mass in the regional model is described. Numerical results from 96-hour integrations with the improved scheme are compared with those from the previous scheme for the cases of the propagations of a wave and a vortex. The behavior of the model at the lateral boundary was noticeably improved with the use of the new scheme, while the solution in the interior domain was little affected by the scheme modification.

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Christopher A. Kerr, David J. Stensrud, and Xuguang Wang

Abstract

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 (Δx = 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.

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Christopher A. Kerr, Louis J. Wicker, and 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.

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Christopher A. Kerr, David J. Stensrud, and 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.

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Christopher A. Kerr, David J. Stensrud, and Xuguang Wang

Abstract

The Geostationary Operational Environmental Satellite-R Series will provide cloud-top observations on the convective scale at roughly the same frequency as Doppler radar observations. To evaluate the potential value of cloud-top temperature observations for data assimilation, an imperfect-model observing system simulation experiment is used. Synthetic cloud-top temperature observations from an idealized splitting supercell created using the Weather Research and Forecasting Model are assimilated along with synthetic radar reflectivity and radial velocity using an ensemble Kalman filter. Observations are assimilated every 5 min for 2.5 h with additive noise used to maintain ensemble spread.

Four experiments are conducted to explore the relative value of cloud-top temperature and radar observations. One experiment only assimilates satellite data, another only assimilates radar data, and two more experiments assimilate both radar and satellite observations, but with the observation types assimilated in different order. Results show a rather weak correlation between cloud-top temperature and horizontal winds, whereas larger correlations are found between cloud-top temperature and microphysics variables. However, the assimilation of cloud-top temperature data alone produces a supercell storm in the ensemble, although the resulting ensemble has much larger spread compared to the ensembles of radar inclusive experiments. The addition of radar observations greatly improves the storm structure and reduces the overprediction of storm extent. Results further show that assimilating cloud-top temperature observations in addition to radar data does not lead to an improved forecast. However, assimilating cloud-top temperature can produce reasonable forecasts for areas lacking radar coverage.

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Adam J. Clark, Israel L. Jirak, Burkely T. Gallo, Brett Roberts, Andrew R. Dean, Kent H. Knopfmeier, Louis J. Wicker, Makenzie Krocak, Patrick S. Skinner, Pamela L. Heinselman, Katie A. Wilson, Jake Vancil, Kimberly A. Hoogewind, Nathan A. Dahl, Gerald J. Creager, Thomas A. Jones, Jidong Gao, Yunheng Wang, Eric D. Loken, Montgomery Flora, Christopher A. Kerr, Nusrat Yussouf, Scott R. Dembek, William Miller, Joshua Martin, Jorge Guerra, Brian Matilla, David Jahn, David Harrison, David Imy, and Michael C. Coniglio
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Adam J. Clark, Israel L. Jirak, Burkely T. Gallo, Kent H. Knopfmeier, Brett Roberts, Makenzie Krocak, Jake Vancil, Kimberly A. Hoogewind, Nathan A. Dahl, Eric D. Loken, David Jahn, David Harrison, David Imy, Patrick Burke, Louis J. Wicker, Patrick S. Skinner, Pamela L. Heinselman, Patrick Marsh, Katie A. Wilson, Andrew R. Dean, Gerald J. Creager, Thomas A. Jones, Jidong Gao, Yunheng Wang, Montgomery Flora, Corey K. Potvin, Christopher A. Kerr, Nusrat Yussouf, Joshua Martin, Jorge Guerra, Brian C. Matilla, and Thomas J. Galarneau
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