scholarly journals Improving Communication of Uncertainty and Risk of High-Impact Weather Through Innovative Forecaster Workshops

2021 ◽  
pp. 1-18
Author(s):  
Brian A. Colle ◽  
Rosemary Auld ◽  
Kenneth Johnson ◽  
Christine O’Connell ◽  
Temis G. Taylor ◽  
...  
Keyword(s):  
Emergency Services ◽  
High Impact ◽  
Decision Makers ◽  
The Novel ◽  
The Public ◽  
Probabilistic Information ◽  
Weather Events ◽  
Tv Media ◽  
High Impact Weather ◽  
Oral Presentations

AbstractIt is challenging to communicate uncertainty for high-impact weather events to the public and decision makers. As a result, there is an increased emphasis and training within the National Weather Service (NWS) for “impact-based decision support.” A Collaborative Science, Technology, And Research (CSTAR) project led by Stony Brook University (SBU) in collaboration with the Alan Alda Center for Communicating Science, several NWS forecast offices, and NWS operational centers held two workshops at SBU on effective forecast communication of probabilistic information for high-impact weather. Trainers in two 1.5-day workshops helped 15-20 forecasters learn to distill their messages, engage audiences, and more effectively communicate risk and uncertainty to decision makers, media, and the general public. The novel aspect of the first workshop focused on using improvisational techniques to connect with audiences along with exercises to improve communication skills using short, clear, conversational statements. The same forecasters participated in the second workshop, which focused on matching messages to intended audiences and stakeholder interaction. Using a recent high-impact weather event, representatives in emergency management, TV media, departments of transportation, and emergency services provided feedback on the forecaster oral presentations (2-3 minute) and a visual slide. This article describes our innovative workshop approach, illustrates some of the techniques used, and highlights participant feedback.

scholarly journals Influence of targeted observations on short-term forecasts of high-impact weather events in the Mediterranean

2013 ◽  
Vol 1 (3) ◽  
pp. 2781-2829
Author(s):  
J. Campins ◽  
B. Navascués ◽  
C. Santos ◽  
A. Amo-Baladrón
Keyword(s):  
Satellite Data ◽  
Positive Impact ◽  
High Impact ◽  
Limited Area ◽  
The Novel ◽  
Structure Amplitude ◽  
Weather Events ◽  
High Impact Weather ◽  
Field Campaigns ◽  
Targeted Observations

Abstract. The influence of targeted observations on short-range forecasts is tested over two different periods of PREVIEW (2008) and MEDEX (2009) Data Targeting field campaigns for a set of Mediterranean high-impact weather events. As targeted observations we have used not only extra radiosondes, but also enhanced satellite data observed in Singular Vectors (SVs) based sensitive regions. Three parallel observing system experiments, based on the High Resolution Limited-Area Model (HIRLAM) data assimilation and forecast system, have been conducted. Forecasts of the three experiments have been assessed using both verifying analyses for upper air fields, and surface observations for several meteorological parameters. Furthermore, quantitative precipitation forecasts (QPF) have been objectively verified using the novel feature oriented Structure–Amplitude–Location (SAL) method. The results obtained show that targeted observations have a generally positive impact. When enhanced satellite data are assimilated, the overall forecast skill is doubled. However, a distinct behaviour is found between PREVIEW and MEDEX cases. While for MEDEX cases the improvement is slight, for PREVIEW cases it is significant. It is suggested that this is due to the location of the target areas and the spatial distribution of the composite observing system and to the different atmospheric predictability in these two periods.

scholarly journals A methodology to conduct wind damage field surveys for high-impact weather events of convective origin

2020 ◽  
Vol 20 (5) ◽  
pp. 1513-1531 ◽  
Author(s):  
Oriol Rodríguez ◽  
Joan Bech ◽  
Juan de Dios Soriano ◽  
Delia Gutiérrez ◽  
Salvador Castán
Keyword(s):  
Field Studies ◽  
High Impact ◽  
Strong Wind ◽  
Field Surveys ◽  
Weather Events ◽  
Straight Line ◽  
High Impact Weather ◽  
Strong Winds ◽  
Wind Events ◽  
The One

Abstract. Post-event damage assessments are of paramount importance to document the effects of high-impact weather-related events such as floods or strong wind events. Moreover, evaluating the damage and characterizing its extent and intensity can be essential for further analysis such as completing a diagnostic meteorological case study. This paper presents a methodology to perform field surveys of damage caused by strong winds of convective origin (i.e. tornado, downburst and straight-line winds). It is based on previous studies and also on 136 field studies performed by the authors in Spain between 2004 and 2018. The methodology includes the collection of pictures and records of damage to human-made structures and on vegetation during the in situ visit to the affected area, as well as of available automatic weather station data, witness reports and images of the phenomenon, such as funnel cloud pictures, taken by casual observers. To synthesize the gathered data, three final deliverables are proposed: (i) a standardized text report of the analysed event, (ii) a table consisting of detailed geolocated information about each damage point and other relevant data and (iii) a map or a KML (Keyhole Markup Language) file containing the previous information ready for graphical display and further analysis. This methodology has been applied by the authors in the past, sometimes only a few hours after the event occurrence and, on many occasions, when the type of convective phenomenon was uncertain. In those uncertain cases, the information resulting from this methodology contributed effectively to discern the phenomenon type thanks to the damage pattern analysis, particularly if no witness reports were available. The application of methodologies such as the one presented here is necessary in order to build homogeneous and robust databases of severe weather cases and high-impact weather events.

A role of high impact weather events in waterborne disease outbreaks in Canada, 1975 – 2001

2006 ◽  
Vol 16 (3) ◽  
pp. 167-180 ◽  
Author(s):  
Kate M. Thomas ◽  
Dominique F. Charron ◽  
David Waltner-Toews ◽  
Corinne Schuster ◽  
Abdel R. Maarouf ◽  
...  
Keyword(s):  
Disease Outbreaks ◽  
High Impact ◽  
Waterborne Disease ◽  
Weather Events ◽  
High Impact Weather

Diagnosing factors in parameterised and resolved convection with physical tendency output in AROME-Arctic during a Cold-Air Outbreak event

2020 ◽  
Author(s):  
Marvin Kähnert ◽  
Teresa M. Valkonen ◽  
Harald Sodemann
Keyword(s):  
Weather Prediction ◽  
Cloud Microphysics ◽  
High Impact ◽  
Surface Fluxes ◽  
The Arctic ◽  
Shallow Convection ◽  
Time Step ◽  
Cold Air ◽  
Weather Events ◽  
High Impact Weather

<p>Numerical weather prediction (NWP) models generally display comparatively low predictive skill in the Arctic. Particularly, the large impact of sub-grid scale, parameterised processes, such as surface fluxes, radiation or cloud microphysics during high-latitude weather events pose a substantial challenge for numerical modelling. Such processes are most influential during mesoscale weather events, such as polar lows, often embedded in cold air outbreaks (CAO), some of which cause high impact weather. Uncertainty in Arctic weather forecasts is thus critically dependent on parameterised processes. The strong influence from several parameterised processes also makes model forecasts particularly susceptible to compensation of errors from different parameterisations, which potentially limits model improvement.<br>Here we analyse model output of individual parameterised tendencies of wind, temperature and humidity during Arctic high-impact weather in AROME-Arctic, the operational NWP model used by the Norwegian Meteorological Institute Norway for the European Arctic. Individual tendencies describe the contribution of each applied physical parameterisation to a respective variable per model time step. We study a CAO-event taking place during 24 - 27 December 2015. This intense and widespread CAO event, reaching from the Fram Straight to Norway and affecting a particularly large portion of the Nordic seas at a time, was characterised by strong heat fluxes along the sea ice edge. <br>Model intern definitions for boundary layer type become apparent as a decisive factor in tendency contributions. Especially the interplay between the dual mass flux and the turbulence scheme is of essence here. Furthermore, sensitivity experiments, featuring a run without shallow convection and a run with a new statistical cloud scheme, show how a physically similar result is obtained by substantially different tendencies in the model.</p>

scholarly journals Towards the operational use of tweets data in high impact weather scenarios: data mining and analytics in Basque Country.

2021 ◽  
Author(s):  
Santiago Gaztelumendi
Keyword(s):  
Data Mining ◽  
Social Media ◽  
Real Time ◽  
Basque Country ◽  
Local Level ◽  
High Impact ◽  
Severe Weather ◽  
Weather Events ◽  
The World ◽  
High Impact Weather

<p>Although social media industry is now a very congested Marketplace, Twitter continues to maintain its status as a popular social media platform. There are 330 million monthly active users and 145 million daily active users on Twitter sending more than 6,000 tweets every second in the world. In Spain case 85% population are social media users, with around 5 million tweeter profiles for a population around 47 million. In the autonomous community of Basque country (2.17 million inhabitants) around 20% of citizens use Twitter.</p><p>Twitter is a social tool that enables users to post messages (tweets) of up to 280 characters supporting a wide variety of social communication practices including photo and video attach. The Basque Meteorology Agency @Euskalmet with more than 115,3 K followers is one of the most popular accounts in Basque Country. Twitter is not only an opportunity to instantaneous spread messages to people without intermediaries, but also as a potential platform for valuable data acquisition using tweeter API capabilities. In this contribution, we present a study of different aspects related to the operational use of Twitter data in the context of high impact weather scenarios at local level.</p><p>The most important activity in Euskalmet are actions in severe weather events. Before the event, mainly centered in forecast and communication, during the event in nowcast, surveillance and impact monitoring and after the event in post-event analysis. During all these complex processes real time tweets posted by local users offer a huge amount of data that conveniently processed could be useful for different purposes. For operational staff, working at office during severe weather episodes, is critical to understand the local effects that an adverse phenomenon is causing and the correct perception of the extent of impact and social alarm. For this purposes, among others, different information associated with posted tweets can be extracted and exploited conveniently. In this work, we present some results that demonstrate how different data mining and advances analytics techniques can be used in order to include social media data information for different tasks and particularly during high impact weather events.</p><p>In this paper we summarize our experience during a proof of concept project for automatic real time tweeter analysis and the development of an operational tool for tweeter API data exploitation in the Basque Country. We present the main challenges and problems that we have had to face, including how to deal with the lack of geolocation information, since in the case of the Basque country, as in other parts of the world, tweets containing geotags are the exception, not the rule.</p>

scholarly journals Influence of targeted observations on short-term forecasts of high-impact weather events in the Mediterranean

2013 ◽  
Vol 13 (11) ◽  
pp. 2891-2910 ◽  
Author(s):  
J. Campins ◽  
B. Navascués ◽  
C. Santos ◽  
A. Amo-Baladrón
Keyword(s):  
Satellite Data ◽  
Positive Impact ◽  
High Impact ◽  
Radiosonde Data ◽  
Limited Area ◽  
Structure Amplitude ◽  
Weather Events ◽  
Average Improvement ◽  
High Impact Weather ◽  
Targeted Observations

Abstract. The influence of targeted observations on short-range forecasts is tested over two different periods of PREVIEW (2008) and MEDEX (2009) data targeting field campaigns for a set of Mediterranean high-impact weather events. As targeted observations we have used not only extra radiosondes, but also enhanced satellite data observed in singular vector (SV)-based sensitive regions. Three parallel observing system experiments, based on the High-Resolution Limited-Area Model (HIRLAM) data assimilation and forecast system, have been conducted. Forecasts of the three experiments have been assessed using both verifying analyses for upper-air fields, and surface observations for several meteorological parameters. Furthermore, quantitative precipitation forecasts (QPF) have been objectively verified using the novel feature oriented Structure–Amplitude–Location (SAL) method. The results obtained show that extra radiosondes have an overall positive impact on the forecasts (average improvement of all upper-air variables and vertical levels studied is 3.6%). When in addition to extra radiosonde data also enhanced satellite data are assimilated, the overall forecast skill is almost doubled. However, a distinct behaviour is found between the PREVIEW and MEDEX cases. While for MEDEX cases the improvement is slight, for PREVIEW cases the improvement is significant (average improvements of 1.7% and 8.9%, respectively, for the experiment with enhanced satellite data). It is suggested that this is due to the location of the target areas and the spatial distribution of the composite observing system and to the different atmospheric predictability in these two periods.

scholarly journals A Proposed Model-Based Methodology for Feature-Specific Prediction for High-Impact Weather

2011 ◽  
Vol 26 (2) ◽  
pp. 243-249 ◽  
Author(s):  
Jacob R. Carley ◽  
Benjamin R. J. Schwedler ◽  
Michael E. Baldwin ◽  
Robert J. Trapp ◽  
John Kwiatkowski ◽  
...  
Keyword(s):  
Prediction Models ◽  
Weather Prediction ◽  
High Impact ◽  
Storm Event ◽  
Weather Events ◽  
Proposed Model ◽  
Forecasting Method ◽  
Specific Prediction ◽  
High Impact Weather ◽  
Verification Methodology

Abstract A feature-specific forecasting method for high-impact weather events that takes advantage of high-resolution numerical weather prediction models and spatial forecast verification methodology is proposed. An application of this method to the prediction of a severe convective storm event is given.

scholarly journals An Evaluation of Thunderstorm Observations in Southern Ontario Using Automated Lightning Detection Data

2015 ◽  
Vol 54 (9) ◽  
pp. 1837-1846 ◽  
Author(s):  
Steven Huryn ◽  
William Gough ◽  
Ken Butler ◽  
Tanzina Mohsin
Keyword(s):  
High Impact ◽  
Small Scale ◽  
Weather Station ◽  
Southern Ontario ◽  
Weather Events ◽  
Lightning Detection ◽  
High Impact Weather ◽  
Weather Stations ◽  
Populated Region ◽  
Over Time

AbstractHigh-impact weather events, such as thunderstorms and their associated hazards, are aspects of a changing climate that are likely to have an adverse effect on society. Southern Ontario is Canada’s most populated region as well as the region of Canada that receives the most thunderstorms. Before completing climatological studies of thunderstorms in southern Ontario, it is important to determine whether historical thunderstorm data are reliable. Archived thunderstorm data are available from eight 24-h-staffed weather stations across southern Ontario. The data may be subject to observer bias. This study compared the manual observations of thunderstorms with automated data from the Canadian Lightning Detection Network. It was found that the data that are based on the manual observations are reliable enough that any significant trends in thunderstorm occurrence over time should be apparent. Because of the small-scale nature of thunderstorms, however, the data may only be valid for small distances (up to 10 km) around each weather station. A diurnal bias was also discovered, with manual thunderstorm observations being slightly better at night.

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