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Jacob T. Radford
,
Gary M. Lackmann
,
Jean Goodwin
,
James Correia Jr.
, and
Kirstin Harnos

Abstract

We developed five prototype convection-allowing model ensemble visualization products with the goal of improving depictions of the timing of winter weather hazards. These products are interactive, web-based plots visualizing probabilistic onset times and durations of intense snowfall rates, probabilities of heavy snow at rush hour, periods of heightened impacts, and mesoscale snowband probabilities. Prototypes were evaluated in three experimental groups coordinated by the Weather Prediction Center (WPC) Hydrometeorological Testbed (HMT), with a total of 53 National Weather Service (NWS) forecasters. Forecasters were asked to complete a simple forecast exercise for a snowfall event, with a control group using the Storm Prediction Center’s (SPC) High-Resolution Ensemble Forecast (HREF) system viewer, and an experimental group using both the HREF viewer and the five experimental graphics. Forecast accuracy was similar between the groups, but the experimental group exhibited smaller mean absolute error for snowfall duration forecasts. A series of Likert-scale questions saw participants respond favorably to all of the products and indicated that they would use them in operational forecasts and in communicating information to core partners. Forecasters also felt that the new products improved their comprehension of ensemble spread and reduced the time required to complete the forecasting exercise. Follow-up plenary discussions reiterated that there is a high demand for ensemble products of this type, though a number of potential improvements, such as greater customizability, were suggested. Ultimately, we demonstrated that social science methods can be effectively employed in the atmospheric sciences to yield improved visualization products.

Open access
Jacob T. Radford
,
Gary M. Lackmann
,
Jean Goodwin
,
James Correia Jr.
, and
Kirstin Harnos

Abstract

We applied social science research principles to develop a suite of probabilistic winter weather forecasting visualizations for High-Resolution Ensemble Forecast (HREF) system output. This was achieved through an iterative, dialogic process with U.S. National Weather Service (NWS) forecasters to design nine new web-based, interactive products aimed toward improving visualizations of winter weather event magnitudes, characteristics, and timing. These products were influenced by feedback from a preliminary focus group, which emphasized the importance of product credibility, contextualization, and scalability. In a follow-up discussion, winter weather forecasting experts found the event timing products to have the greatest utility due to their association with impact-decision support services (IDSS). Furthermore, forecasters assessed snowfall rates as the most impactful variable rather than snowfall totals and radar reflectivity. The timing products include plots of probabilistic snowfall onset time and duration, rush hour intersection probabilities, and a combination meteogram. The onset and duration plots visualize the ensemble-average onset time and duration of a specified snowfall rate, as demonstrated in previous works, but with the addition of uncertainty information by visualizing the earliest, most likely, and latest potential onset times as well as the shortest, most likely, and longest potential durations. The rush hour product visualizes the probability of exceeding a specified snowfall rate during local commutes, and the combination meteogram allows rapid identification of high-impact periods by encoding probabilities of precipitation, precipitation-type probabilities, and average rates into one graphical tool. Examples of these interactive products are maintained on our companion website: www.visweather.com/bams2023.

Open access
Linda O. Mearns
,
Melissa S. Bukovsky
,
Ruby Leung
,
Yun Qian
,
Ray Arritt
,
William Gutowski
,
Eugene S. Takle
,
Sébastien Biner
,
Daniel Caya
,
James Correia Jr.
,
Richard Jones
,
Lisa Sloan
, and
Mark Snyder
Full access
Linda O. Mearns
,
Ray Arritt
,
Sébastien Biner
,
Melissa S. Bukovsky
,
Seth McGinnis
,
Stephan Sain
,
Daniel Caya
,
James Correia Jr.
,
Dave Flory
,
William Gutowski
,
Eugene S. Takle
,
Richard Jones
,
Ruby Leung
,
Wilfran Moufouma-Okia
,
Larry McDaniel
,
Ana M. B. Nunes
,
Yun Qian
,
John Roads
,
Lisa Sloan
, and
Mark Snyder

The North American Regional Climate Change Assessment Program (NARCCAP) is an international effort designed to investigate the uncertainties in regional-scale projections of future climate and produce highresolution climate change scenarios using multiple regional climate models (RCMs) nested within atmosphere–ocean general circulation models (AOGCMs) forced with the Special Report on Emission Scenarios (SRES) A2 scenario, with a common domain covering the conterminous United States, northern Mexico, and most of Canada. The program also includes an evaluation component (phase I) wherein the participating RCMs, with a grid spacing of 50 km, are nested within 25 years of National Centers for Environmental Prediction–Department of Energy (NCEP–DOE) Reanalysis II.

This paper provides an overview of evaluations of the phase I domain-wide simulations focusing on monthly and seasonal temperature and precipitation, as well as more detailed investigation of four subregions. The overall quality of the simulations is determined, comparing the model performances with each other as well as with other regional model evaluations over North America. The metrics used herein do differentiate among the models but, as found in previous studies, it is not possible to determine a “best” model among them. The ensemble average of the six models does not perform best for all measures, as has been reported in a number of global climate model studies. The subset ensemble of the two models using spectral nudging is more often successful for domain-wide root-mean-square error (RMSE), especially for temperature. This evaluation phase of NARCCAP will inform later program elements concerning differentially weighting the models for use in producing robust regional probabilities of future climate change.

Full access
John S. Kain
,
Michael C. Coniglio
,
James Correia
,
Adam J. Clark
,
Patrick T. Marsh
,
Conrad L. Ziegler
,
Valliappa Lakshmanan
,
Stuart D. Miller Jr.
,
Scott R. Dembek
,
Steven J. Weiss
,
Fanyou Kong
,
Ming Xue
,
Ryan A. Sobash
,
Andrew R. Dean
,
Israel L. Jirak
, and
Christopher J. Melick

The 2011 Spring Forecasting Experiment in the NOAA Hazardous Weather Testbed (HWT) featured a significant component on convection initiation (CI). As in previous HWT experiments, the CI study was a collaborative effort between forecasters and researchers, with equal emphasis on experimental forecasting strategies and evaluation of prototype model guidance products. The overarching goal of the CI effort was to identify the primary challenges of the CI forecasting problem and to establish a framework for additional studies and possible routine forecasting of CI. This study confirms that convection-allowing models with grid spacing ~4 km represent many aspects of the formation and development of deep convection clouds explicitly and with predictive utility. Further, it shows that automated algorithms can skillfully identify the CI process during model integration. However, it also reveals that automated detection of individual convection cells, by itself, provides inadequate guidance for the disruptive potential of deep convection activity. Thus, future work on the CI forecasting problem should be couched in terms of convection-event prediction rather than detection and prediction of individual convection cells.

Full access
Adam J. Clark
,
Steven J. Weiss
,
John S. Kain
,
Israel L. Jirak
,
Michael Coniglio
,
Christopher J. Melick
,
Christopher Siewert
,
Ryan A. Sobash
,
Patrick T. Marsh
,
Andrew R. Dean
,
Ming Xue
,
Fanyou Kong
,
Kevin W. Thomas
,
Yunheng Wang
,
Keith Brewster
,
Jidong Gao
,
Xuguang Wang
,
Jun Du
,
David R. Novak
,
Faye E. Barthold
,
Michael J. Bodner
,
Jason J. Levit
,
C. Bruce Entwistle
,
Tara L. Jensen
, and
James Correia Jr.

The NOAA Hazardous Weather Testbed (HWT) conducts annual spring forecasting experiments organized by the Storm Prediction Center and National Severe Storms Laboratory to test and evaluate emerging scientific concepts and technologies for improved analysis and prediction of hazardous mesoscale weather. A primary goal is to accelerate the transfer of promising new scientific concepts and tools from research to operations through the use of intensive real-time experimental forecasting and evaluation activities conducted during the spring and early summer convective storm period. The 2010 NOAA/HWT Spring Forecasting Experiment (SE2010), conducted 17 May through 18 June, had a broad focus, with emphases on heavy rainfall and aviation weather, through collaboration with the Hydrometeorological Prediction Center (HPC) and the Aviation Weather Center (AWC), respectively. In addition, using the computing resources of the National Institute for Computational Sciences at the University of Tennessee, the Center for Analysis and Prediction of Storms at the University of Oklahoma provided unprecedented real-time conterminous United States (CONUS) forecasts from a multimodel Storm-Scale Ensemble Forecast (SSEF) system with 4-km grid spacing and 26 members and from a 1-km grid spacing configuration of the Weather Research and Forecasting model. Several other organizations provided additional experimental high-resolution model output. This article summarizes the activities, insights, and preliminary findings from SE2010, emphasizing the use of the SSEF system and the successful collaboration with the HPC and AWC.

A supplement to this article is available online (DOI:10.1175/BAMS-D-11-00040.2)

Full access