Search Results

You are looking at 1 - 9 of 9 items for

  • Author or Editor: Matthew Wilson x
  • Refine by Access: All Content x
Clear All Modify Search
Matthew B. Wilson
and
Matthew S. Van Den Broeke

Abstract

Supercell thunderstorms often have pronounced signatures of hydrometeor size sorting within their forward-flank regions, including an arc-shaped region of high differential reflectivity (Z DR) along the inflow edge of the forward flank known as the Z DR arc and a clear horizontal separation between this area of high Z DR values and an area of enhanced K DP values deeper into the storm core. Recent work has indicated that Z DR arc and K DPZ DR separation signatures in supercell storms may be related to environmental storm-relative helicity and low-level shear. Thus, characteristics of these signatures may be helpful to indicate whether a given storm is likely to produce a tornado. Although Z DR arc and K DPZ DR separation signatures are typically easy to qualitatively identify in dual-polarization radar fields, quantifying their characteristics can be time-consuming and makes research into these signatures and their potential operational applications challenging. To address this problem, this paper introduces an automated Python algorithm to objectively identify and track these signatures in Weather Surveillance Radar-1988 Doppler (WSR-88D) data and quantify their characteristics. This paper will discuss the development of the algorithm, demonstrate its performance through comparisons with manually generated time series of Z DR arc and K DPZ DR separation signature characteristics, and briefly explore potential uses of this algorithm in research and operations.

Full access
Matthew J. Bunkers
,
Matthew B. Wilson
,
Matthew S. Van Den Broeke
, and
Devon J. Healey

Abstract

In this exploratory study, storm-motion deviations are examined for concurrent tornadic and nontornadic supercells using 171 cases. This deviation, or “delta,” is defined as the shear-orthogonal distance between the observed supercell motion and a baseline supercell-motion prediction. Larger deltas—representing supercells moving farther right (in a shear-relative sense) compared to the baseline prediction—are hypothesized as more likely to be associated with tornadoes than nearby supercells with smaller deltas, consistent with recent research. Automated radar tracking is used to calculate supercell motion every scan, which then is compared to a model-derived hourly supercell-motion prediction to calculate the deltas. Tornadic supercells have larger average deltas (by 1.9–2.0 m s−1) than nearby nontornadic supercells when using 20- and 30-min storm-motion calculations, and the deltas are larger for the tornadic versus nontornadic supercells ∼80% of the time. Average delta trends also are positive 62%–70% of the time prior to tornadogenesis. The supercell-motion deltas show a modest positive correlation with EF-scale damage rating, indicating a possible relationship between tornado rating and storm deviation. The relative delta differences between tornadic and nontornadic supercells appear more meaningful than the absolute delta magnitudes (i.e., about 70% of tornadic cases with negative average deltas had deltas that were less negative compared to concurrent nontornadic supercells). This concept shows promise as a potential tool to assist operational forecasters in tornado warning decisions.

Significance Statement

Supercells are rotating thunderstorms, and these storms produce the most destructive tornadoes. However, it has been challenging to forecast which supercells will produce tornadoes. In this exploratory study to help better forecast supercell tornadoes, we looked at how the observed supercell motion compared to the predicted motion, based on a commonly used method. We found tornadic supercells tend to move somewhat differently from the predicted motion—compared to nearby nontornadic supercells. This unusual movement often starts prior to tornadogenesis, potentially providing lead time to tornado formation. Pending further validation, development, and testing of real-time analysis tools, this storm-motion behavior could be used by operational forecasters as a factor to help determine when (or when not) to issue a tornado warning for a supercell thunderstorm, thus providing better information to the public.

Restricted access
Sean Arms
,
Julien Chastang
,
Maxwell Grover
,
Jon Thielen
,
Matthew Wilson
, and
Douglas Dirks
Free access
Rita D. Roberts
,
Amanda R. S. Anderson
,
Eric Nelson
,
Barbara G. Brown
,
James W. Wilson
,
Matthew Pocernich
, and
Thomas Saxen

Abstract

A forecaster-interactive capability was added to an automated convective storm nowcasting system [Auto-Nowcaster (ANC)] to allow forecasters to enhance the performance of 1-h nowcasts of convective storm initiation and evolution produced every 6 min. This Forecaster-Over-The-Loop (FOTL-ANC) system was tested at the National Weather Service Fort Worth–Dallas, Texas, Weather Forecast Office during daily operations from 2005 to 2010. The forecaster’s role was to enter the locations of surface convergence boundaries into the ANC prior to dissemination of nowcasts to the Center Weather Service Unit. Verification of the FOTL-ANC versus ANC (no human) nowcasts was conducted on the convective scale. Categorical verification scores were computed for 30 subdomains within the forecast domain. Special focus was placed on subdomains that included convergence boundaries for evaluation of forecaster involvement and impact on the FOTL-ANC nowcasts. The probability of detection of convective storms increased by 20%–60% with little to no change observed in the false-alarm ratios. Bias values increased from 0.8–1.0 to 1.0–3.0 with human involvement. The accuracy of storm nowcasts notably improved with forecaster involvement; critical success index (CSI) values increased from 0.15–0.25 (ANC) to 0.2–0.4 (FOTL-ANC). Over short time periods, CSI values as large as 0.6 were also observed. This study demonstrated definitively that forecaster involvement led to positive improvement in the nowcasts in most cases while causing no degradation in other cases; a few exceptions are noted. Results show that forecasters can play an important role in the production of rapidly updated, convective storm nowcasts for end users.

Full access
Jay H. Lawrimore
,
David Wuertz
,
Anna Wilson
,
Scott Stevens
,
Matthew Menne
,
Bryant Korzeniewski
,
Michael A. Palecki
,
Ronald D. Leeper
, and
Thomas Trunk

Abstract

The National Oceanic and Atmospheric Administration (NOAA) has operated a network of Fischer & Porter gauges providing hourly and subhourly precipitation observations as part of the U.S. Cooperative Observer Program since the middle of the twentieth century. A transition from punched paper recording to digital recording was completed by NOAA’s National Weather Service in 2013. Subsequently, NOAA’s National Centers for Environmental Information (NCEI) upgraded its quality assurance and data stewardship processes to accommodate the new digital record, better assure the quality of the data, and improve the timeliness by which hourly precipitation observations are made available to the user community. Automated methods for removing noise, detecting diurnal variations, and identifying malfunctioning gauges are described along with quality control algorithms that are applied on hourly and daily time scales. The quality of the hourly observations during the digital era is verified by comparison with hourly observations from the U.S. Climate Reference Network and summary of the day precipitation totals from the Global Historical Climatology Network dataset.

Free access
Minghua Zheng
,
Luca Delle Monache
,
Xingren Wu
,
F. Martin Ralph
,
Bruce Cornuelle
,
Vijay Tallapragada
,
Jennifer S. Haase
,
Anna M. Wilson
,
Matthew Mazloff
,
Aneesh Subramanian
, and
Forest Cannon

Abstract

Conventional observations of atmospheric rivers (ARs) over the northeastern Pacific Ocean are sparse. Satellite radiances are affected by the presence of clouds and heavy precipitation, which impact their distribution in the lower atmosphere and in precipitating areas. The goal of this study is to document a data gap in existing observations of ARs in the northeastern Pacific, and to investigate how a targeted field campaign called AR Reconnaissance (AR Recon) can effectively fill this gap. When reconnaissance data are excluded, there is a gap in AR regions from near the surface to the middle troposphere (below 450 hPa), where most water vapor and its transport are concentrated. All-sky microwave radiances provide data within the AR object, but their quality is degraded near the AR core and its leading edge, due to the existence of thick clouds and precipitation. AR Recon samples ARs and surrounding areas to improve downstream precipitation forecasts over the western United States. This study demonstrates that despite the apparently extensive swaths of modern satellite radiances, which are critical to estimate large-scale flow, the data collected during 15 AR Recon cases in 2016, 2018, and 2019 supply about 99% of humidity, 78% of temperature, and 45% of wind observations in the critical maximum water vapor transport layer from the ocean surface to 700 hPa in ARs. The high-vertical-resolution dropsonde observations in the lower atmosphere over the northeastern Pacific Ocean can significantly improve the sampling of low-level jets transporting water vapor to high-impact precipitation events in the western United States.

Full access
Sarah A. Tessendorf
,
Roelof T. Bruintjes
,
Courtney Weeks
,
James W. Wilson
,
Charles A. Knight
,
Rita D. Roberts
,
Justin R. Peter
,
Scott Collis
,
Peter R. Buseck
,
Evelyn Freney
,
Michael Dixon
,
Matthew Pocernich
,
Kyoko Ikeda
,
Duncan Axisa
,
Eric Nelson
,
Peter T. May
,
Harald Richter
,
Stuart Piketh
,
Roelof P. Burger
,
Louise Wilson
,
Steven T. Siems
,
Michael Manton
,
Roger C. Stone
,
Acacia Pepler
,
Don R. Collins
,
V. N. Bringi
,
M. Thurai
,
Lynne Turner
, and
David McRae

As a response to extreme water shortages in southeast Queensland, Australia, brought about by reduced rainfall and increasing population, the Queensland government decided to explore the potential for cloud seeding to enhance rainfall. The Queensland Cloud Seeding Research Program (QCSRP) was conducted in the southeast Queensland region near Brisbane during the 2008/09 wet seasons. In addition to conducting an initial exploratory, randomized (statistical) cloud seeding study, multiparameter radar measurements and in situ aircraft microphysical data were collected. This comprehensive set of observational platforms was designed to improve the physical understanding of the effects of both ambient aerosols and seeding material on precipitation formation in southeast Queensland clouds. This focus on gaining physical understanding, along with the unique combination of modern observational platforms utilized in the program, set it apart from previous cloud seeding research programs. The overarching goals of the QCSRP were to 1) determine the characteristics of local cloud systems (i.e., weather and climate), 2) document the properties of atmospheric aerosol and their microphysical effects on precipitation formation, and 3) assess the impact of cloud seeding on cloud microphysical and dynamical processes to enhance rainfall. During the course of the program, it became clear that there is great variability in the natural cloud systems in the southeast Queensland region, and understanding that variability would be necessary before any conclusions could be made regarding the impact of cloud seeding. This article presents research highlights and progress toward achieving the goals of the program, along with the challenges associated with conducting cloud seeding research experiments

Full access
Kyle R. Clem
,
Marilyn N. Raphael
,
Susheel Adusumilli
,
Rebecca Baiman
,
Alison F. Banwell
,
Sandra Barreira
,
Rebecca L. Beadling
,
Steve Colwell
,
Lawrence Coy
,
Rajashree T. Datta
,
Jos De Laat
,
Devon Dunmire
,
Ryan L. Fogt
,
Natalie M. Freeman
,
Helen Amanda Fricker
,
Alex S. Gardner
,
Bryan Johnson
,
Linda M. Keller
,
Natalya A. Kramarova
,
Matthew A. Lazzara
,
Jan L. Lieser
,
Michael MacFerrin
,
Graeme A. MacGilchrist
,
Michelle L. MacLennan
,
Robert A. Massom
,
Matthew R. Mazloff
,
Thomas L. Mote
,
Eric R. Nash
,
Paul A. Newman
,
Taylor Norton
,
Irina Petropavlovskikh
,
Michael Pitts
,
Phillip Reid
,
Michelle L. Santee
,
Ted A. Scambos
,
Jia-Rui Shi
,
Sharon Stammerjohn
,
Susan E. Strahan
,
Andrew F. Thompson
,
Jonathan D. Wille
, and
Earle Wilson
Free access
Stephen W. Nesbitt
,
Paola V. Salio
,
Eldo Ávila
,
Phillip Bitzer
,
Lawrence Carey
,
V. Chandrasekar
,
Wiebke Deierling
,
Francina Dominguez
,
Maria Eugenia Dillon
,
C. Marcelo Garcia
,
David Gochis
,
Steven Goodman
,
Deanna A. Hence
,
Karen A. Kosiba
,
Matthew R. Kumjian
,
Timothy Lang
,
Lorena Medina Luna
,
James Marquis
,
Robert Marshall
,
Lynn A. McMurdie
,
Ernani de Lima Nascimento
,
Kristen L. Rasmussen
,
Rita Roberts
,
Angela K. Rowe
,
Juan José Ruiz
,
Eliah F.M.T. São Sabbas
,
A. Celeste Saulo
,
Russ S. Schumacher
,
Yanina Garcia Skabar
,
Luiz Augusto Toledo Machado
,
Robert J. Trapp
,
Adam C. Varble
,
James Wilson
,
Joshua Wurman
,
Edward J. Zipser
,
Ivan Arias
,
Hernán Bechis
, and
Maxwell A. Grover

Abstract

This article provides an overview of the experimental design, execution, education and public outreach, data collection, and initial scientific results from the Remote Sensing of Electrification, Lightning, and Mesoscale/Microscale Processes with Adaptive Ground Observations (RELAMPAGO) field campaign. RELAMPAGO was a major field campaign conducted in the Córdoba and Mendoza provinces in Argentina and western Rio Grande do Sul State in Brazil in 2018–19 that involved more than 200 scientists and students from the United States, Argentina, and Brazil. This campaign was motivated by the physical processes and societal impacts of deep convection that frequently initiates in this region, often along the complex terrain of the Sierras de Córdoba and Andes, and often grows rapidly upscale into dangerous storms that impact society. Observed storms during the experiment produced copious hail, intense flash flooding, extreme lightning flash rates, and other unusual lightning phenomena, but few tornadoes. The five distinct scientific foci of RELAMPAGO—convection initiation, severe weather, upscale growth, hydrometeorology, and lightning and electrification—are described, as are the deployment strategies to observe physical processes relevant to these foci. The campaign’s international cooperation, forecasting efforts, and mission planning strategies enabled a successful data collection effort. In addition, the legacy of RELAMPAGO in South America, including extensive multinational education, public outreach, and social media data gathering associated with the campaign, is summarized.

Full access