Leveraging Different Visual Designs for Communication of Severe Weather Events and their Uncertainty

Effective communication of potential weather hazards and its uncertainty to the general public is key to prevent and mitigate negative outcomes from weather hazards. The general public needs effective tools at hand that can allow them to make the best decision as possible during a severe weather event. Currently, there are many approaches for weather forecast visualization, such as contour and thematic maps [5]. However, guidelines and best practices in visualization can help to improve these designs and make them more effective [1, 2].In this work, we present several interactive visual designs for mobile visualization of severe weather events for the communication of weather hazards, their risks, uncertainty, and recommended actions. Our approach is based on previous work on uncertainty visualization [5], cognitive science [6], and decision sciences for risk management [3, 4]. We propose six configurations that vary the ratio of text vs graphics used in the visual display, and the interaction workflow needed for a non-expert user to make an informed decision and effective actions. Our goal is to test how efficient these configurations are and to what degree they are suitable to communicate weather hazards, associated uncertainty, risk, and recommended actions to non-experts. Future steps include two cycle of evaluations, consisting of a first pilot to rapidly test the prototype with a small number of participants, collect actionable insights, and incorporate potential improvements. In a second user study, we will perform a crowd-sourced extensive evaluation of the visualization prototypes.References[1] A. Diehl, A. Abdul-Rahman, M. El-Assady, B. Bach, D. A. Keim, and M. Chen. Visguides: A forum for discussing visualization guidelines. In Proceedings of the EuroVis Short Papers, pages 61&#8211;65, 2018.[2]&#160; A. Diehl, E. E. Firat, T. Torsney-Weir, A. Abdul-Rahman, B. Bach, R. S. Laramee, R. Pajarola, and M. Chen. VisGuided: A community-driven approach for education in visualization.&#160; In Proceedings Eurographics Education Papers, to appear, 2021.[3] N. Fleischhut and S. M. Herzog. Wie laesst sich die unsicherheit von vorhersagen sinnvoll kommu- nizieren? In Wetterwarnungen: Von der Extremereignisinformation zu Kommunikation und Handlung. Beitr&#228;ge aus dem Forschungsprojekt WEXICOM, pages 63&#8211;81. 2019.[4] G. Gigerenzer, R. Hertwig, E. Van Den Broek, B. Fasolo, and K. V. Katsikopoulos. &#8220;A 30% chance of rain tomorrow&#8221;: How does the public understand probabilistic weather forecasts? Risk Analysis: An International Journal, 25(3):623&#8211;629, 2005.[5] I. K&#252;bler, K.-F. Richter, and S. I. Fabrikant. Against all odds: multicriteria decision making with hazard prediction maps depicting uncertainty. Annals of the American Association of Geographers, 110(3):661&#8211;683, 2020.[6] L. M. Padilla, I. T. Ruginski, and S. H. Creem-Regehr. Effects of ensemble and summary displays on interpretations of geospatial uncertainty data. Cognitive research: principles and implications, 2(1):1&#8211;16, 2017.

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Crowdsourcing of weather observations at national meteorological and hydrological services in Europe

Advances in Science and Research ◽

10.5194/asr-15-71-2018 ◽

2018 ◽

Vol 15 ◽

pp. 71-76 ◽

Cited By ~ 9

Author(s):

Thomas Krennert ◽

Georg Pistotnik ◽

Rainer Kaltenberger ◽

Christian Csekits

Keyword(s):

General Public ◽

Ground Truth ◽

The United States ◽

Severe Weather ◽

Weather Forecasts ◽

Weather Events ◽

Weather Observations ◽

Crucial Information ◽

Hydrological Services ◽

Ground Truth Information

Abstract. National Meteorological and Hydrological Services (NMHSs) increase their efforts to deliver impact-based weather forecasts and warnings. At the same time, a desired increase in cost-efficiency prompts these services to automatize their weather station networks and to reduce the number of human observers, which leads to a lack of “ground truth” information about weather phenomena and their impact. A possible alternative is to encourage the general public to submit weather observations, which may include crucial information especially in high-impact situations. We wish to provide an overview of the state and properties of existing collaborations between NMHSs and voluntary weather observers or storm spotters across Europe. For that purpose, we performed a survey among 30 European NMHSs, from which 22 NMHSs returned our questionnaire. This study summarizes the most important findings and evaluates the use of “crowdsourced” information. 86 % of the surveyed NMHSs utilize information provided by the general public, 50 % have established official collaborations with spotter groups, and 18 % have formalized them. The observations are most commonly used for a real-time improvement of severe weather warnings, their verification, and an establishment of a climatology of severe weather events. The importance of these volunteered weather and impact observations has strongly risen over the past decade. We expect that this trend will continue and that storm spotters will become an essential part in severe weather warning, like they have been for decades in the United States of America. A rising number of incoming reports implies that quality management will become an increasing issue, and we finally discuss an idea how to handle this challenge.

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Generating Probabilistic Next-Day Severe Weather Forecasts from Convection-Allowing Ensembles Using Random Forests

Weather and Forecasting ◽

10.1175/waf-d-19-0258.1 ◽

2020 ◽

Vol 35 (4) ◽

pp. 1605-1631

Author(s):

Eric D. Loken ◽

Adam J. Clark ◽

Christopher D. Karstens

Keyword(s):

Random Forests ◽

Weather Forecast ◽

Severe Weather ◽

Decision Makers ◽

Limited Information ◽

Weather Forecasts ◽

Skill Scores ◽

Weather Forecasters ◽

Forecast Data ◽

Better Than

AbstractExtracting explicit severe weather forecast guidance from convection-allowing ensembles (CAEs) is challenging since CAEs cannot directly simulate individual severe weather hazards. Currently, CAE-based severe weather probabilities must be inferred from one or more storm-related variables, which may require extensive calibration and/or contain limited information. Machine learning (ML) offers a way to obtain severe weather forecast probabilities from CAEs by relating CAE forecast variables to observed severe weather reports. This paper develops and verifies a random forest (RF)-based ML method for creating day 1 (1200–1200 UTC) severe weather hazard probabilities and categorical outlooks based on 0000 UTC Storm-Scale Ensemble of Opportunity (SSEO) forecast data and observed Storm Prediction Center (SPC) storm reports. RF forecast probabilities are compared against severe weather forecasts from calibrated SSEO 2–5-km updraft helicity (UH) forecasts and SPC convective outlooks issued at 0600 UTC. Continuous RF probabilities routinely have the highest Brier skill scores (BSSs), regardless of whether the forecasts are evaluated over the full domain or regional/seasonal subsets. Even when RF probabilities are truncated at the probability levels issued by the SPC, the RF forecasts often have BSSs better than or comparable to corresponding UH and SPC forecasts. Relative to the UH and SPC forecasts, the RF approach performs best for severe wind and hail prediction during the spring and summer (i.e., March–August). Overall, it is concluded that the RF method presented here provides skillful, reliable CAE-derived severe weather probabilities that may be useful to severe weather forecasters and decision-makers.

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Perception and Comprehension of the Extended Forecast Graphic: A Survey of Broadcast Meteorologists and the Public

Bulletin of the American Meteorological Society ◽

10.1175/bams-d-19-0078.1 ◽

2020 ◽

Vol 101 (2) ◽

pp. E221-E236 ◽

Cited By ~ 1

Author(s):

Jacob R. Reed ◽

Jason C. Senkbeil

Keyword(s):

Risk Communication ◽

Low Temperatures ◽

General Public ◽

Weather Forecast ◽

The Public ◽

Weather Events ◽

Public Data ◽

Adverse Weather ◽

High And Low Temperatures ◽

Probability Of Precipitation

Abstract There have been multiple efforts in recent years to simplify visual weather forecast products, with the goal of more efficient risk communication for the general public. Many meteorological forecast products, such as the cone of uncertainty, storm surge graphics, warning polygons, and Storm Prediction Center (SPC) convective outlooks, have created varying levels of public confusion resulting in revisions, modifications, and improvements. However, the perception and comprehension of private weather graphics produced by television stations has been largely overlooked in peer-reviewed research. The goal of this study is to explore how the extended forecast graphic, more commonly known as the 7, 10 day, etc., is utilized by broadcasters and understood by the public. Data were gathered from surveys with the general public and also from broadcast meteorologists. Results suggest this graphic is a source of confusion and highlights a disconnect between the meteorologists producing the graphic and the content prioritized by their audiences. Specifically, timing and intensity of any precipitation or adverse weather events are the two most important variables to consider from the viewpoint of the public. These variables are generally absent from the extended forecast graphic, thus forcing the public to draw their own conclusions, which may differ from what the meteorologist intends to convey. Other results suggest the placement of forecast high and low temperatures, use of probability of precipitation, icon inconsistency, and length of time the graphic is shown also contribute to public confusion and misunderstanding.

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Colorful Language: Investigating Public Interpretation of the Storm Prediction Center Convective Outlook

Weather and Forecasting ◽

10.1175/waf-d-21-0001.1 ◽

2021 ◽

Author(s):

Sean Ernst ◽

Joe Ripberger ◽

Makenzie J. Krocak ◽

Hank Jenkins-Smith ◽

Carol Silva

Keyword(s):

Statistical Tests ◽

Weather Forecast ◽

Severe Weather ◽

Logistic Regression Models ◽

Public Interpretation ◽

Severe Thunderstorms ◽

Weather Events ◽

The Us ◽

Storm Prediction ◽

Future Work

AbstractAlthough severe weather forecast products, such as the Storm Prediction Center (SPC) convective outlook, are much more accurate than climatology at day-to-week time scales, tornadoes and severe thunderstorms claim dozens of lives and cause billions of dollars in damage every year. While the accuracy of this outlook has been well documented, less work has been done to explore the comprehension of the product by non-expert users like the general public. This study seeks to fill this key knowledge gap by collecting data from a representative survey of U.S. adults in the lower 48 states about their use and interpretation of the SPC convective outlook. Participants in this study were asked to rank the words and colors used in the outlook from least to greatest risk, and their answers were compared through visualizations and statistical tests across multiple demographics. Results show that the US public ranks the outlook colors similarly to their ordering in the outlook but switch the positions of several of the outlook words as compared to the operational product. Logistic regression models also reveal that more numerate individuals more correctly rank the SPC outlook words and colors. These findings suggest that the words used in the convective outlook may confuse non-expert users, and that future work should continue to use input from public surveys to test potential improvements in the choice of outlook words. Using more easily understood words may help to increase the outlook’s decision support value and potentially reduce the harm caused by severe weather events.

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Advanced MUlti-GNSS Array for Monitoring Severe Weather Events (AMUSE): Project overview

10.5194/egusphere-egu2020-9669 ◽

2020 ◽

Author(s):

Karina Wilgan ◽

Jens Wickert ◽

Galina Dick ◽

Florian Zus ◽

Torsten Schmidt ◽

...

Keyword(s):

Water Vapor ◽

Severe Weather ◽

Precipitation Forecast ◽

Spatiotemporal Resolution ◽

Weather Forecasts ◽

Precise Knowledge ◽

Weather Events ◽

Data Products ◽

New Generation ◽

The Impact

Global Navigation Satellite Systems (GNSS) have revolutionized positioning, navigation, and timing, becoming a common part of our everyday life. Aside from these well-known civilian and commercial applications, GNSS is currently established as a powerful and versatile observation tool for geosciences. An outstanding application in this context is the operational monitoring of atmospheric water vapor with high spatiotemporal resolution. The water vapor is the most abundant greenhouse gas, which accounts for about 70% of atmospheric warming and plays a key role in the atmospheric energy exchange. The precise knowledge of its highly variable spatial and temporal distribution is a precondition for precise modeling of the atmospheric state as a base for numerical weather forecasts especially with focus to the strong precipitation and severe weather events.The data from European GNSS networks are widely operationally used to improve regional weather forecasts in several countries. However, the impact of the currently provided data products to the forecast systems is still limited due to the exclusively focusing on GPS-only based data products; to the limited atmospheric information content, which is provided mostly in the zenith direction and to the time delay between measurement and providing the data products, which is currently about one hour.AMUSE is a recent research project, funded by the DFG (German Research Council) and performed in close cooperation of TUB, GFZ and DWD during 2020-2022. The project foci are the major limitations of currently operationally used generation of GNSS-based water vapor data. AMUSE will pioneer the development of next generation data products. Main addressed innovations are: &#160;1) Developments to provide multi-GNSS instead of GPS-only data, including GLONASS, Galileo and BeiDou; 2) Developments to provide high quality slant observations, containing water vapor information along the line-of-sight from the respective ground stations; 3) Developments to shorten the delay between measurements and the provision of the products to the meteorological services.This GNSS-focused work of AMUSE will be complemented by the contribution of German Weather Service DWD to investigate in detail and to quantify the forecast improvement, which can be reached by the new generation GNSS-based meteorology data. Several dedicated forecast experiments will be conducted with focus on one of the most challenging issues, the precipitation forecast in case of severe weather events. These studies will support the future assimilation of the new generation data to the regional forecast system of DWD and potentially also to other European weather services.

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Weather Forecast Use for Winter Recreation*

Weather Climate and Society ◽

10.1175/wcas-d-13-00052.1 ◽

2014 ◽

Vol 6 (3) ◽

pp. 293-306 ◽

Cited By ~ 26

Author(s):

Michelle Rutty ◽

Jean Andrey

Keyword(s):

Decision Making ◽

Mobile Devices ◽

General Public ◽

Leisure Activities ◽

Weather Forecast ◽

Weather Forecasts ◽

Empirical Basis ◽

Weather Information ◽

Winter Recreation ◽

Context Specific

Abstract Recent studies have begun to address the importance of weather information for leisure activities. This paper contributes to the understanding of how weather information is sourced, perceived, and used for the discretionary and weather-dependent winter activities of skiing, snowboarding, and snowmobiling. A survey of 1948 Ontario (Canada) skiers/snowboarders and snowmobilers is the empirical basis for the paper, providing insights into how winter recreationists are both similar to and different from the general public with respect to weather information. Results show that virtually all (≥97%) skiers/snowboarders and snowmobilers use weather forecasts when planning an outing, which are primarily (≥95%) sourced through Internet and mobile devices. Skiers/snowboarders and snowmobilers are also highly attentive to rain and freezing rain variables in the forecast, as it negatively affects participation. The results also demonstrate the importance of forecast use for planning travel to snow resorts and snowmobile trails, with poor road conditions likely to result in a postponed or cancelled trip. These findings underscore the differing weather needs of subpopulations, with the need for continued research to examine variations among weather forecast users for context specific decision making.

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Severe weather over the Highveld of South Africa during November 2016

Water SA ◽

10.4314/wsa.v44i1.09 ◽

2018 ◽

Vol 44 (1 January) ◽

Cited By ~ 3

Author(s):

Lee-ann Simpson ◽

Liesl L Dyson

Keyword(s):

South Africa ◽

Prediction Models ◽

Weather Forecast ◽

Severe Weather ◽

Severe Thunderstorm ◽

Weather Events ◽

Circulation Patterns ◽

Conditional Instability ◽

Synoptic Circulation

November months are notorious for severe weather over the Highveld of South Africa. November 2016 was no exception and a large number of severe events occurred. Very heavy rainfall, large hail and tornadoes were reported. The aim of this paper is to compare the synoptic circulation of November 2016 with the long-term mean November circulation and to investigate some sounding derived parameters. Furthermore, a few of the severe weather events are described in detail. The surface temperatures and dewpoint temperatures were found to be higher than normal resulting in increased conditional instability over the Highveld. Low-level moisture originated over the warm Mozambique Channel and the 500 hPa temperature trough was located favourably over the Highveld; further east than normal. The combination of these factors and weak steering winds resulted in flash flooding on the 9th while favourable wind shear conditions caused the development of a tornado on 15 November. The favourable circulation patterns and moisture gave rise to an atmosphere in which severe weather was a possibility, and the awareness of such factors is used as one of many tools when considering the severe weather forecast. The consideration of the daily variables derived from sounding data were good precursors for the prediction of severe thunderstorm development over the Highveld during November 2016. It is recommended that an operational meteorologist incorporates upper air sounding data into the forecasting process and not to rely on numerical prediction models exclusively.

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Initial results of the project SINOPTICA (Satellite-borne and IN-situ Observations to Predict The Initiation of Convection for ATM)

10.5194/ems2021-189 ◽

2021 ◽

Author(s):

Laura Esbri ◽

Maria Carmen Llasat ◽

Tomeu Rigo ◽

Massimo Milelli ◽

Vincenzo Mazzarella ◽

...

Keyword(s):

Weather Prediction ◽

Wrf Model ◽

Remote Sensing Data ◽

Weather Forecast ◽

Relevant Information ◽

Air Traffic ◽

Severe Weather ◽

Radar Images ◽

Weather Events ◽

Adverse Weather

In the framework of the SINOPTICA project (EU H2020 SESAR, 2020 &#8211; 2022), different meteorological forecasting techniques are being tested to better nowcast severe weather events affecting Air Traffic Management (ATM) operations. Short-range severe weather forecasts with very high spatial resolution will be obtained starting from radar images, through an application of nowcasting techniques combined with Numerical Weather Prediction (NWP) model with data assimilation. The final goal is to integrate compact nowcast information into an Arrival Manager to support Air Traffic Controllers (ATCO) when sequencing and guiding approaching aircraft even in adverse weather situations. The guidance-support system will enable the visualization of dynamic weather information on the radar display of the controller, and the 4D-trajectory calculation for diversion coordination around severe weather areas. This meteorological information must be compact and concise to not interfere with other relevant information on the radar display of the controller.Three severe weather events impacting different Italian airports have been selected for a preliminary radar analysis. Some products are considered for obtaining the best radar approach to characterize the severity of the events for ATM interests. Combining the Vertical Integrated Liquid and the Echo Top Maximum products, hazard thresholds are defined for different domains around the airports. The Weather Research and Forecasting (WRF) model has been used to simulate the formation and development of the aforementioned convective events. In order to produce a more accurate very short-term weather forecast (nowcasting), remote sensing data (e.g. radar, GNSS) and conventional observations are assimilated by using a cycling three-dimensional variational technique. This contribution presents some preliminary results on the progress of the project.

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Overview of the first HyMeX Special Observation Period over Italy: observations and model results

Hydrology and Earth System Sciences Discussions ◽

10.5194/hessd-10-11643-2013 ◽

2013 ◽

Vol 10 (9) ◽

pp. 11643-11710 ◽

Cited By ~ 4

Author(s):

R. Ferretti ◽

E. Pichelli ◽

S. Gentile ◽

I. Maiello ◽

D. Cimini ◽

...

Keyword(s):

Central Italy ◽

Weather Forecast ◽

Heavy Precipitation ◽

Western Mediterranean ◽

Target Area ◽

Weather Forecasts ◽

Weather Events ◽

North Eastern ◽

Forecast Models ◽

Observation Period

Abstract. During the first Hymex campaign (5 September–6 November 2012) referred to as Special Observation Period (SOP-1), dedicated to heavy precipitation events and flash floods in Western Mediterranean, three Italian hydro-meteorological monitoring sites were activated: Liguria-Tuscany, North-Eastern Italy and Central Italy. The extraordinary deployment of advanced instrumentation, including instrumented aircrafts, and the use of several different operational weather forecast models has allowed an unprecedented monitoring and analysis of high impact weather events around the Italian hydro-meteorological sites. This activity has seen the strict collaboration between the Italian scientific and operational communities. In this paper, an overview of the Italian organization during the SOP-1 is provided, and selected Intensive Observation Periods (IOPs) are described. A significant event for each Italian target area is chosen for this analysis: IOP2 (12–13 September 2012) in North-Eastern Italy, IOP13 (15–16 October 2012) in Central Italy and IOP19 (3–5 November 2012) in Liguria and Tuscany. For each IOP the meteorological characteristics, together with special observations and weather forecasts, are analyzed with the aim of highlighting strengths and weaknesses of the forecast modeling systems. Moreover, using one of the three events, the usefulness of different operational chains is highlighted.

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Internet of Things Based Weather Forecast Monitoring System

Indonesian Journal of Electrical Engineering and Computer Science ◽

10.11591/ijeecs.v9.i3.pp555-557 ◽

2018 ◽

Vol 9 (3) ◽

pp. 555 ◽

Cited By ~ 3

Author(s):

Atul Kulkarni ◽

Debajyoti Mukhopadhyay

Keyword(s):

Weather Forecasting ◽

Weather Forecast ◽

Severe Weather ◽

Weather Data ◽

Partial Explanation ◽

Significant Function ◽

Weather Events ◽

Weather Stations ◽

Communication Methods ◽

Cost Components

Weather forecasting is a significant function in meteorology and has been one of the most systematically challenging troubles around the world.This scheme deals with the structure of a weather display method using small cost components so that any electronics hobbyist can construct it. As a replacement for using sensors to collect the weather data, the development gets the information from weather stations placed around the world through a global weather data supplier. Severe weather phenomena challengedifficult weather forecast approach with the partial explanation. Weather events have numerous parameters that are not possible to detail and compute. Growing on communication methods enables weather predictsspecialist systems to combine and share possessions and thus hybrid systems have emerged. Still, though these improvements on climate predict, these expert systems can’t be entirely reliable while weather forecast is central problem.

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