Nathan Snook

Spring and Summer Severe Weather Reports over the Midwest as a Function of Convective Mode: A Preliminary Study

Radar data during the period 1 April–31 August 2002 were used to classify all convective storms occurring in a 10-state region of the central United States into nine predominant morphologies, and the severe weather reports associated with each morphology were then analyzed. The morphologies included three types of cellular convection (individual cells, clusters of cells, and broken squall lines), five types of linear systems (bow echoes, squall lines with trailing stratiform rain, lines with leading stratiform rain, lines with parallel stratiform rain, and lines with no stratiform rain), and nonlinear systems. Because linear systems with leading and line-parallel stratiform rainfall were relatively rare in the 2002 sample of 925 events, 24 additional cases of these morphologies from 1996 and 1997 identified by Parker and Johnson were included in the sample. All morphologies were found to pose some risk of severe weather, but substantial differences existed between the number and types of severe weather reports and the different morphologies. Normalizing results per event, nonlinear systems produced the fewest reports of hail, and were relatively inactive for all types of severe weather compared to the other morphologies. Linear systems generated large numbers of reports from all categories of severe weather. Among linear systems, the hail and tornado threat was particularly enhanced in systems having leading and line-parallel stratiform rain. Bow echoes were found to produce far more severe wind reports than any other morphology. The flooding threat was largest in broken lines and linear systems having trailing and line-parallel stratiform rain. Cellular storms, despite much smaller areal coverage, also were abundant producers of severe hail and tornadoes, particularly in broken squall lines.

Analysis of a Tornadic Mesoscale Convective Vortex Based on Ensemble Kalman Filter Assimilation of CASA X-Band and WSR-88D Radar Data

AbstractOne of the goals of the National Science Foundation Engineering Research Center (ERC) for Collaborative Adaptive Sensing of the Atmosphere (CASA) is to improve storm-scale numerical weather prediction (NWP) by collecting data with a dense X-band radar network that provides high-resolution low-level coverage, and by assimilating such data into NWP models. During the first spring storm season after the deployment of four radars in the CASA Integrated Project-1 (IP-1) network in southwest Oklahoma, a tornadic mesoscale convective system (MCS) was captured by CASA and surrounding Weather Surveillance Radars-1988 Doppler (WSR-88Ds) on 8–9 May 2007. The MCS moved across northwest Texas and western and central Oklahoma; two tornadoes rated as category 1 on the enhanced Fujita scale (EF-1) and one tornado of EF-0 intensity were reported during the event, just to the north of the IP-1 network. This was the first tornadic convective system observed by CASA.To quantify the impacts of CASA radar data in storm...

Ensemble Probabilistic Forecasts of a Tornadic Mesoscale Convective System from Ensemble Kalman Filter Analyses Using WSR-88D and CASA Radar Data

AbstractThis study examines the ability of a storm-scale numerical weather prediction (NWP) model to predict precipitation and mesovortices within a tornadic mesoscale convective system that occurred over Oklahoma on 8–9 May 2007, when the model is initialized from ensemble Kalman filter (EnKF) analyses including data from four Engineering Research Center for Collaborative Adaptive Sensing of the Atmosphere (CASA) X-band and five Weather Surveillance Radar-1988 Doppler (WSR-88D) S-band radars. Ensemble forecasts are performed and probabilistic forecast products generated, focusing on prediction of radar reflectivity (a proxy of quantitative precipitation) and mesovortices (an indication of tornado potential).Assimilating data from both the CASA and WSR-88D radars for the ensemble and using a mixed-microphysics ensemble during data assimilation produces the best probabilistic mesovortex forecast. The use of multiple microphysics schemes within the ensemble aims to address at least partially the model physi...

The Analysis and Prediction of Microphysical States and Polarimetric Radar Variables in a Mesoscale Convective System Using Double-Moment Microphysics, Multinetwork Radar Data, and the Ensemble Kalman Filter

AbstractDoppler radar data are assimilated with an ensemble Kalman Filter (EnKF) in combination with a double-moment (DM) microphysics scheme in order to improve the analysis and forecast of microphysical states and precipitation structures within a mesoscale convective system (MCS) that passed over western Oklahoma on 8–9 May 2007. Reflectivity and radial velocity data from five operational Weather Surveillance Radar-1988 Doppler (WSR-88D) S-band radars as well as four experimental Collaborative and Adaptive Sensing of the Atmosphere (CASA) X-band radars are assimilated over a 1-h period using either single-moment (SM) or DM microphysics schemes within the forecast ensemble. Three-hour deterministic forecasts are initialized from the final ensemble mean analyses using a SM or DM scheme, respectively. Polarimetric radar variables are simulated from the analyses and compared with polarimetric WSR-88D observations for verification. EnKF assimilation of radar data using a multimoment microphysics scheme for ...

Multiscale EnKF Assimilation of Radar and Conventional Observations and Ensemble Forecasting for a Tornadic Mesoscale Convective System

AbstractIn recent studies, the authors have successfully demonstrated the ability of an ensemble Kalman filter (EnKF), assimilating real radar observations, to produce skillful analyses and subsequent ensemble-based probabilistic forecasts for a tornadic mesoscale convective system (MCS) that occurred over Oklahoma and Texas on 9 May 2007. The current study expands upon this prior work, performing experiments for this case on a larger domain using a nested-grid EnKF, which accounts for mesoscale uncertainties through the initial ensemble and lateral boundary condition perturbations. In these new experiments, conventional observations (including surface, wind profiler, and upper-air observations) are assimilated in addition to the WSR-88D and the Center for Collaborative Adaptive Sensing of the Atmosphere (CASA) radar data used in the previous studies, better representing meso- and convective-scale features. The relative impacts of conventional and radar data on analyses and forecasts are examined, and bia...

Prediction and Ensemble Forecast Verification of Hail in the Supercell Storms of 20 May 2013

AbstractDespite recent advances in storm-scale ensemble NWP, short-term (0–90 min) explicit forecasts of severe hail remain a major challenge as a result of the fast evolution and short time scales of hail-producing convective storms and the substantial uncertainty associated with the microphysical representation of hail. In this study, 0–90-min ensemble hail forecasts for the supercell storms of 20 May 2013 over central Oklahoma are examined and verified, with the goals of 1) evaluating ensemble forecast performance, 2) comparing the advantages and limitations of different forecast fields potentially suitable for the prediction of hail and severe hail in a Warn-on-Forecast setting, and 3) evaluating the use of dual-polarization radar observations for hail forecast validation. To address the challenges of hail prediction and to produce skillful forecasts, the ensemble uses a two-moment microphysics scheme that explicitly predicts a hail-like rimed-ice category and is run with a grid spacing of 500 m. Rada...

Ensemble Probabilistic Prediction of a Mesoscale Convective System and Associated Polarimetric Radar Variables Using Single-Moment and Double-Moment Microphysics Schemes and EnKF Radar Data Assimilation

AbstractEnsemble-based probabilistic forecasts are performed for a mesoscale convective system (MCS) that occurred over Oklahoma on 8–9 May 2007, initialized from ensemble Kalman filter analyses using multinetwork radar data and different microphysics schemes. Two experiments are conducted, using either a single-moment or double-moment microphysics scheme during the 1-h-long assimilation period and in subsequent 3-h ensemble forecasts. Qualitative and quantitative verifications are performed on the ensemble forecasts, including probabilistic skill scores. The predicted dual-polarization (dual-pol) radar variables and their probabilistic forecasts are also evaluated against available dual-pol radar observations, and discussed in relation to predicted microphysical states and structures.Evaluation of predicted reflectivity (Z) fields shows that the double-moment ensemble predicts the precipitation coverage of the leading convective line and stratiform precipitation regions of the MCS with higher probabiliti...

Ensemble Hail Prediction for the Storms of 10 May 2010 in South-Central Oklahoma Using Single- and Double-Moment Microphysical Schemes

AbstractExplicit prediction of hail using numerical weather prediction models remains a significant challenge; microphysical uncertainties and errors are a significant contributor to this challenge. This study assesses the ability of storm-scale ensemble forecasts using single-moment Lin or double-moment Milbrandt and Yau microphysical schemes in predicting hail during a severe weather event over south-central Oklahoma on 10 May 2010. Radar and surface observations are assimilated using an ensemble Kalman filter (EnKF) at 5-min intervals. Three sets of ensemble forecasts, launched at 15-min intervals, are then produced from EnKF analyses at times ranging from 30 min prior to the first observed hail to the time of the first observed hail. Forty ensemble members are run at 500-m horizontal grid spacing in both EnKF assimilation cycles and subsequent forecasts. Hail forecasts are verified using radar-derived products including information from single- and dual-polarization radar data: maximum estimated size ...

A four-dimensional variational system for skillful operational prediction of convective storms

Forecasting convective storms using NWP models is an important goal and a highly active area of ongoing research. Skillful and reliable NWP of convective storms could allow for severe weather warnings with longer lead times, as operational forecasters begin to incorporate convective-scale forecasts into severe weather forecast operations (Stensrud et al., 2009, 2013). This would then provide vulnerable individuals and industries with more time to seek shelter and/or mitigate the impact of severe weather hazards. The importance of data assimilation (DA) of radar observations for convective-scale NWP is widely acknowledged; consequently, a wide variety of DA methods have been explored and studied. Both variational and ensemble DA methods (as well as hybrid methods), have been successfully applied in convective-scale NWP. Understanding the relative merits and challenges associated with different DA methods, and the relative impacts of different datasets, is vital for advancing the science underpinning the prediction of convective storms. Some of these issues are addressed by Chen et al. (2016) in their new study (Page 1106–1119, this issue), as they investigate the impacts of assimilating both surface and radar observations using a four-dimensional variational (4DVAR) DA system. While 4DVAR requires an adjoint model, which can be difficult to develop and computationally expensive to run, a 4DVAR system has certain advantages over 3DVAR and EnKF (ensemble Kalman filter) systems, including the ability to assimilate asynchronous observations including time correlations (Kalnay et al., 2007). The advantages of 4DVAR make it particularly desirable for convective-scale NWP. In recent years, rapid advances have been made in stormscale DA and prediction. 3DVAR, 4DVAR, and EnKF DA systems have been applied successfully to a wide range of cases and observational datasets, and are a key component of efforts within the United States (and around the world) to develop a warn-on-forecast weather warning system (Stensrud et al., 2013). Assimilation of radar data has been found to be