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Derek R. Stratman and Keith A. Brewster

Abstract

On 24 May 2011, Oklahoma experienced an outbreak of tornadoes, including one rated EF5 on the enhanced Fujita (EF) scale and two rated EF4. The extensive observation network in this area makes this an ideal case to examine the impact of using five different microphysics parameterization schemes, including single-, double-, and triple-moment microphysics, in an efficient high-resolution data assimilation system suitable for nowcasting and short-term forecasting with low latencies. Additionally, the real-time configuration of the 1-km ARPS, which assimilated increments produced by 3DVAR with cloud analysis using incremental analysis updating (IAU), had success providing a good baseline forecast. ARPS forecasts of 0–2 h are verified using observation-point, neighborhood, and object-based verification techniques. The object-based verification technique uses updraft helicity fields to represent mesocyclone centers, which are verified against tornado locations from three supercells of interest. Varying levels of success in the forecasts are found and appear to be dependent on the complexity of the storm interaction, with early forecasts of isolated storms exhibiting the most success. Verification scores indicate that the multimoment microphysics schemes tend to produce better forecasts of tornadic supercells. However, some of the forecasts from the single-moment microphysics schemes perform as well as or better than the forecasts from the multimoment microphysics schemes.

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Derek R. Stratman, Corey K. Potvin, and Louis J. Wicker

Abstract

A goal of Warn-on-Forecast (WoF) is to develop forecasting systems that produce accurate analyses and forecasts of severe weather to be utilized in operational warning settings. Recent WoF-related studies have indicated the need to alleviate storm displacement errors in both analyses and forecasts. A potential solution to reduce these errors is the feature alignment technique (FAT), which mitigates displacement errors between observations and model fields while satisfying constraints. This study merges the FAT with a local ensemble transform Kalman filter (LETKF) and uses observing system simulation experiments (OSSEs) to vet the FAT as a potential alleviator of forecast errors arising from storm displacement errors. An idealized truth run of a supercell on a 250-m grid is used to generate pseudoradar observations, which are assimilated onto a 2-km grid using a 50-member ensemble to produce analyses and forecasts of the supercell. The FAT uses composite reflectivity to generate a 2D field of displacement vectors that is used to align the model variables with the observations prior to each analysis cycle. The FAT is tested by displacing the initial model background fields from the observations or modifying the environmental wind profile to create a storm motion bias in the forecast cycles. The FAT–LETKF performance is evaluated and compared to that of the LETKF alone. The FAT substantially reduces errors in storm intensity, location, and structure during data assimilation and subsequent forecasts. These supercell OSSEs provide the foundation for future experiments with real data and more complex events.

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Derek R. Stratman, Michael C. Coniglio, Steven E. Koch, and Ming Xue

Abstract

This study uses both traditional and newer verification methods to evaluate two 4-km grid-spacing Weather Research and Forecasting Model (WRF) forecasts: a “cold start” forecast that uses the 12-km North American Mesoscale Model (NAM) analysis and forecast cycle to derive the initial and boundary conditions (C0) and a “hot start” forecast that adds radar data into the initial conditions using a three-dimensional variational data assimilation (3DVAR)/cloud analysis technique (CN). These forecasts were evaluated as part of 2009 and 2010 NOAA Hazardous Weather Test Bed (HWT) Spring Forecasting Experiments. The Spring Forecasting Experiment participants noted that the skill of CN’s explicit forecasts of convection estimated by some traditional objective metrics often seemed large compared to the subjectively determined skill. The Gilbert skill score (GSS) reveals CN scores higher than C0 at lower thresholds likely due to CN having higher-frequency biases than C0, but the difference is negligible at higher thresholds, where CN’s and C0’s frequency biases are similar. This suggests that if traditional skill scores are used to quantify convective forecasts, then higher (>35 dBZ) reflectivity thresholds should be used to be consistent with expert’s subjective assessments of the lack of forecast skill for individual convective cells. The spatial verification methods show that both CN and C0 generally have little to no skill at scales <8–12Δx starting at forecast hour 1, but CN has more skill at larger spatial scales (40–320 km) than C0 for the majority of the forecasting period. This indicates that the hot start provides little to no benefit for forecasts of convective cells, but that it has some benefit for larger mesoscale precipitation systems.

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Derek R. Stratman, Nusrat Yussouf, Youngsun Jung, Timothy A. Supinie, Ming Xue, Patrick S. Skinner, and Bryan J. Putnam

Abstract

A potential replacement candidate for the aging operational WSR-88D infrastructure currently in place is the phased array radar (PAR) system. The current WSR-88Ds take ~5 min to produce a full volumetric scan of the atmosphere, whereas PAR technology allows for full volumetric scanning of the same atmosphere every ~1 min. How this increase in temporal frequency of radar observations might affect the National Severe Storms Laboratory’s (NSSL) Warn-on-Forecast system (WoFS), which is a storm-scale ensemble data assimilation and forecast system for severe convective weather, is unclear. Since radar data assimilation is critical for the WoFS, this study explores the optimal temporal frequency of PAR observations for storm-scale data assimilation using the 31 May 2013 El Reno, Oklahoma, tornadic supercell event. The National Severe Storms Laboratory’s National Weather Radar Testbed PAR in Norman, Oklahoma, began scanning this event more than an hour before the first (and strongest) tornado developed near El Reno, and scanned most of the tornadic supercell’s evolution. Several experiments using various cycling and data frequencies to synchronously and asynchronously assimilate these PAR observations are conducted to produce analyses and very short-term forecasts of the El Reno supercell. Forecasts of low-level reflectivity and midlevel updraft helicity are subjectively evaluated and objectively verified using spatial and object-based techniques. Results indicate that assimilating more frequent PAR observations can lead to more accurate analyses and probabilistic forecasts of the El Reno supercell at longer lead times. Hence, PAR is a promising radar platform for WoFS.

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Bryan J. Putnam, Youngsun Jung, Nusrat Yussouf, Derek Stratman, Timothy A. Supinie, Ming Xue, Charles Kuster, and Jonathan Labriola

Abstract

Assimilation of dual-polarization (dual-pol) observations provides more accurate storm-scale analyses to initialize forecasts of severe convective thunderstorms. This study investigates the impact assimilating experimental sector-scan dual-pol observations has on storm-scale ensemble forecasts and how this impact changes over different data assimilation (DA) windows using the ensemble Kalman filter (EnKF). Ensemble forecasts are initialized after 30, 45, and 60 minutes of DA for two sets of experiments that assimilate either reflectivity and radial velocity only (EXPZ) or reflectivity and radial velocity plus differential reflectivity (EXPZZDR). This study uses the 31 May 2013 Oklahoma event which included multiple storms that produced tornadoes and severe hail, with focus placed on two storms that impacted El Reno and Stillwater during the event.

The earliest initialized forecast of EXPZZDR better predicts the evolution of the El Reno storm than EXPZ, but the two sets of experiments become similar at subsequent forecast times. However, the later EXPZZDR forecasts of the Stillwater storm, which organized towards the end of the DA window, produce improved results compared to EXPZ, in which the storm is less intense and weakens. Evaluation of forecast products for supercell mesocyclones (updraft helicity [UH]) and hail show similar results with earlier EXPZZDR forecasts better predicting the UH swaths of the El Reno storm and later forecasts producing improved UH and hail swaths for the Stillwater storm. The results indicate that the assimilation of ZDR over fewer DA cycles can produce improved forecasts when DA windows sufficiently cover storms during their initial development and organization.

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Derek R. Stratman, Nusrat Yussouf, Youngsun Jung, Timothy A. Supinie, Ming Xue, Patrick S. Skinner, and Bryan J. Putnam

Abstract

A potential replacement candidate for the aging operational WSR-88D infrastructure currently in place is the phased array radar (PAR) system. The current WSR-88Ds take ~5 min to produce a full volumetric scan of the atmosphere, whereas PAR technology allows for full volumetric scanning of the same atmosphere every ~1 min. How this increase in temporal frequency of radar observations might affect the National Severe Storms Laboratory’s (NSSL) Warn-on-Forecast system (WoFS), which is a storm-scale ensemble data assimilation and forecast system for severe convective weather, is unclear. Since radar data assimilation is critical for the WoFS, this study explores the optimal temporal frequency of PAR observations for storm-scale data assimilation using the 31 May 2013 El Reno, Oklahoma, tornadic supercell event. The National Severe Storms Laboratory’s National Weather Radar Testbed PAR in Norman, Oklahoma, began scanning this event more than an hour before the first (and strongest) tornado developed near El Reno, and scanned most of the tornadic supercell’s evolution. Several experiments using various cycling and data frequencies to synchronously and asynchronously assimilate these PAR observations are conducted to produce analyses and very short-term forecasts of the El Reno supercell. Forecasts of low-level reflectivity and midlevel updraft helicity are subjectively evaluated and objectively verified using spatial and object-based techniques. Results indicate that assimilating more frequent PAR observations can lead to more accurate analyses and probabilistic forecasts of the El Reno supercell at longer lead times. Hence, PAR is a promising radar platform for WoFS.

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Burkely T. Gallo, Adam J. Clark, Israel Jirak, John S. Kain, Steven J. Weiss, Michael Coniglio, Kent Knopfmeier, James Correia Jr., Christopher J. Melick, Christopher D. Karstens, Eswar Iyer, Andrew R. Dean, Ming Xue, Fanyou Kong, Youngsun Jung, Feifei Shen, Kevin W. Thomas, Keith Brewster, Derek Stratman, Gregory W. Carbin, William Line, Rebecca Adams-Selin, and Steve Willington

Abstract

Led by NOAA’s Storm Prediction Center and National Severe Storms Laboratory, annual spring forecasting experiments (SFEs) in the Hazardous Weather Testbed test and evaluate cutting-edge technologies and concepts for improving severe weather prediction through intensive real-time forecasting and evaluation activities. Experimental forecast guidance is provided through collaborations with several U.S. government and academic institutions, as well as the Met Office. The purpose of this article is to summarize activities, insights, and preliminary findings from recent SFEs, emphasizing SFE 2015. Several innovative aspects of recent experiments are discussed, including the 1) use of convection-allowing model (CAM) ensembles with advanced ensemble data assimilation, 2) generation of severe weather outlooks valid at time periods shorter than those issued operationally (e.g., 1–4 h), 3) use of CAMs to issue outlooks beyond the day 1 period, 4) increased interaction through software allowing participants to create individual severe weather outlooks, and 5) tests of newly developed storm-attribute-based diagnostics for predicting tornadoes and hail size. Additionally, plans for future experiments will be discussed, including the creation of a Community Leveraged Unified Ensemble (CLUE) system, which will test various strategies for CAM ensemble design using carefully designed sets of ensemble members contributed by different agencies to drive evidence-based decision-making for near-future operational systems.

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Mark Weber, Kurt Hondl, Nusrat Yussouf, Youngsun Jung, Derek Stratman, Bryan Putnam, Xuguang Wang, Terry Schuur, Charles Kuster, Yixin Wen, Juanzhen Sun, Jeff Keeler, Zhuming Ying, John Cho, James Kurdzo, Sebastian Torres, Chris Curtis, David Schvartzman, Jami Boettcher, Feng Nai, Henry Thomas, Dusan Zrnić, Igor Ivić, Djordje Mirković, Caleb Fulton, Jorge Salazar, Guifu Zhang, Robert Palmer, Mark Yeary, Kevin Cooley, Michael Istok, and Mark Vincent

Abstract

This article summarizes research and risk reduction that will inform acquisition decisions regarding NOAA’s future national operational weather radar network. A key alternative being evaluated is polarimetric phased-array radar (PAR). Research indicates PAR can plausibly achieve fast, adaptive volumetric scanning, with associated benefits for severe-weather warning performance. We assess these benefits using storm observations and analyses, observing system simulation experiments, and real radar-data assimilation studies. Changes in the number and/or locations of radars in the future network could improve coverage at low altitude. Analysis of benefits that might be so realized indicates the possibility for additional improvement in severe-weather and flash-flood warning performance, with associated reduction in casualties. Simulations are used to evaluate techniques for rapid volumetric scanning and assess data quality characteristics of PAR. Finally, we describe progress in developing methods to compensate for polarimetric variable estimate biases introduced by electronic beam-steering. A research-to-operations (R2O) strategy for the PAR alternative for the WSR-88D replacement network is presented.

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