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

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.

An Automated Mobile Phone Photo Relay and Display Concept Applicable to Operational Severe Weather Monitoring

2015 ◽  
Vol 32 (7) ◽  
pp. 1356-1363 ◽  
Author(s):  
Scott Longmore ◽  
Steven Miller ◽  
Dan Bikos ◽  
Daniel Lindsey ◽  
Edward Szoke ◽  
...  
Keyword(s):  
Mobile Phones ◽  
Distribution System ◽  
Architectural Design ◽  
Processing System ◽  
Value Added ◽  
Weather Forecast ◽  
Severe Weather ◽  
Digital Cameras ◽  
Weather Forecasters

AbstractThe increasing use of mobile phones (MPs) equipped with digital cameras and the ability to post images and information to the Internet in real time has significantly improved the ability to report events almost instantaneously. From the perspective of weather forecasters responsible for issuing severe weather warnings, the old adage holds that a picture is indeed worth a thousand words; a single digital image conveys significantly more information than a simple web-submitted text or phone-relayed report. Timely, quality-controlled, and value-added photography allows the forecaster to ascertain the validity and quality of storm reports. The posting of geolocated, time-stamped storm report photographs utilizing an MP application to U.S. National Weather Service (NWS) Weather Forecast Office (WFO) social media pages has generated recent positive feedback from forecasters. This study establishes the conceptual framework, architectural design, and pathway toward implementation of a formalized photo report (PR) system composed of 1) an MP application, 2) a processing and distribution system, and 3) the Advanced Weather Interactive Processing System II (AWIPS II) data plug-in software. The requirements and anticipated appearance of such a PR system are presented, along with considerations for possible additional features and applications that extend the utility of the system beyond the realm of severe weather applications.

When Weather Forecasters Are Wrong: Image Repair and Public Rhetoric After Severe Weather

2018 ◽  
Vol 40 (6) ◽  
pp. 778-788 ◽  
Author(s):  
Josh Compton
Keyword(s):  
Science Communication ◽  
Rhetorical Analysis ◽  
Severe Weather ◽  
Winter Storm ◽  
Image Repair ◽  
Unique Type ◽  
Public Rhetoric ◽  
Weather Forecasts ◽  
Weather Forecasters ◽  
Insight Into

Weather forecasts are a unique type of prediction rhetoric—science communication with inherent uncertainty and multiple potential interpretations from diverse audiences. When forecasts are wrong, audiences often turn their ire toward the weather forecaster. This rhetorical analysis considers image repair efforts of a meteorologist following a botched winter storm forecast. Implications for communication efforts of weather forecasters are offered, in addition to consideration of insight into the larger realm of the rhetoric of science.

scholarly journals Weather Forecasting in Brazil: A Concise Historical Review

2021 ◽  
Vol 44 ◽  
Author(s):  
Valdo Da Silva Marques ◽  
Claudine Dereczynski
Keyword(s):  
Weather Forecasting ◽  
Technological Development ◽  
Historical Review ◽  
Weather Forecast ◽  
Weather Forecasts ◽  
Weather Forecasters ◽  
Survey Results ◽  
Formation And Evolution ◽  
Climate Studies ◽  
And Training

The main objective of this article is to describe the factors and issues responsible for the evolution of the weather forecast in Brazil.This is done based on a historical review of the formation and evolution of the national meteorological services in the last 170 yearsand on the development of weather forecasting methods. Changes in the routines of weather forecasting services in two centenaryBrazilian institutions, the National Institute of Meteorology and the Brazilian Navy, since the creation of the first subjective forecaststo the present day, are highlighted. Information about the 14 undergraduate courses in Meteorology in Brazil is given, which supportthe technological development of this science, through scientific research and training of human resources. The introduction ofmeteorological radar in the 1970s, and its current networks, as well as the elaboration of the first numerical weather predictions (NWP)by the Center for Weather Forecasting and Climate Studies (Centro de Previsão do Tempo e Estudos Climáticos do Instituto Nacionalde Pesquisas Espaciais – CPTEC/INPE), in 1995, are also described. To complement, a survey is presented, showing the currentworking conditions of weather forecasters. The survey results reveal that 45% of the 102 meteorologists interviewed use the CzechRepublic Windy application to prepare their weather forecasts operationally and almost 60% use the Wyoming University website toobtain data from radiosondes launched in Brazil. It is important to highlight that, since the introduction of NWP by CPTEC/INPE, at theend of the 1990s, there has been a great advance in the field of weather forecasting. Moreover, observational networks have undergonea great expansion, with a significant increase in the number of weather stations in recent decades. Despite all the progress achieved,there is still a need for the integration of observational networks and databases of various institutions. Finally, the development ofapplications that meet the demand of young meteorologists in the operational centers is advisable.

scholarly journals PENERAPAN ALGORITMA EVOLVING NEURAL NETWORK UNTUK PREDIKSI CURAH HUJAN

2016 ◽  
Vol 1 (1) ◽  
pp. 1-8
Author(s):  
Subhan Panji Cipta
Keyword(s):  
Neural Network ◽  
Flood Control ◽  
Early Stage ◽  
Economic Value ◽  
Weather Forecast ◽  
Decision Makers ◽  
Test Results ◽  
The Public ◽  
Rainfall Prediction ◽  
Better Than

Weather and climate information have contributed as one consideration for decision makers. This arises because the information the weather / climate has economic value in a variety of activities , ranging from agriculture to flood control . From the data obtained implied that the current rainfall prediction not so accurate . Forecasts are often given to the public on a regular basis is the weather forecast , not the amount of rainfall. This study uses an algorithm Evolving Neural Network (ENN) as an approach to predict the rainfall , the data processing and calculations will use MatLab 2009b . The parameters used in this study is time , rainfall , humidity and temperature. The results also compared with the test results and predictions BPNN BMKG. From the results of research conducted from early stage to test and measurement , the application of this ENN has a rainfall prediction with accuracy better than the BPNN and prediction algorithms BMKG.  

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

2021 ◽  
Author(s):  
Ananya Pandya ◽  
Nathalie Popovic ◽  
Alexandra Diehl ◽  
Ian Ruginski ◽  
Sara Fabrikant ◽  
...  
Keyword(s):  
General Public ◽  
User Study ◽  
Multicriteria Decision Making ◽  
Weather Forecast ◽  
Visual Display ◽  
Severe Weather ◽  
Uncertainty Visualization ◽  
Weather Forecasts ◽  
Extensive Evaluation ◽  
Weather Events

<p>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].</p><p>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.</p><p><strong>References</strong></p><p>[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 <em>Proceedings of the EuroVis Short Papers</em>, pages 61–65, 2018.</p><p>[2]  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.  In Proceedings Eurographics Education Papers, to appear, 2021.</p><p>[3] N. Fleischhut and S. M. Herzog. Wie laesst sich die unsicherheit von vorhersagen sinnvoll kommu- nizieren? In <em>Wetterwarnungen: Von der Extremereignisinformation zu Kommunikation und Handlung. Beiträge aus dem Forschungsprojekt WEXICOM</em>, pages 63–81. 2019.</p><p>[4] G. Gigerenzer, R. Hertwig, E. Van Den Broek, B. Fasolo, and K. V. Katsikopoulos. “A 30% chance of rain tomorrow”: How does the public understand probabilistic weather forecasts? <em>Risk Analysis: An International Journal</em>, 25(3):623–629, 2005.</p><p>[5] I. Kübler, K.-F. Richter, and S. I. Fabrikant. Against all odds: multicriteria decision making with hazard prediction maps depicting uncertainty. <em>Annals of the American Association of Geographers</em>, 110(3):661–683, 2020.</p><p>[6] L. M. Padilla, I. T. Ruginski, and S. H. Creem-Regehr. Effects of ensemble and summary displays on interpretations of geospatial uncertainty data. <em>Cognitive research: principles and implications</em>, 2(1):1–16, 2017.</p>

scholarly journals Severe Weather Forecasts and Public Perceptions: An Analysis of the 2011 Super Outbreak in Tuscaloosa, Alabama

2020 ◽  
Vol 12 (3) ◽  
pp. 473-485
Author(s):  
Shadya Sanders ◽  
Terri Adams ◽  
Everette Joseph
Keyword(s):  
Forecast Accuracy ◽  
Weather Forecast ◽  
Severe Weather ◽  
Lead Times ◽  
Protective Measures ◽  
Weather Forecasts ◽  
The Public ◽  
The Face ◽  
Citizen Response

AbstractThis paper uses the “Super Outbreak” of 2011 as a case study to examine the potential gaps between the dissemination of severe weather warnings and the public’s behavioral response to this information. This study focuses on a single tornado track that passed through Tuscaloosa, Alabama. The tornado caused massive damage and destruction and led to a total of 62 fatalities. The threat of severe storms was known days in advance, and forecasts were disseminated to the public. Questions were raised about the forecasts, warning lead times, and the perception of the warnings among residents. This paper examines the potential gaps that exist between the dissemination of tornadic warning information and citizen response. The analysis of data collected through a mixed-method approach suggests that, regardless of weather forecast accuracy, a significant chasm exists between the dissemination of warnings and the personalizing of risks, which results in limited use of protective measures in the face of severe weather threats.

scholarly journals A Novel LSTM Model with Interaction Dual Attention for Radar Echo Extrapolation

2021 ◽  
Vol 13 (2) ◽  
pp. 164
Author(s):  
Chuyao Luo ◽  
Xutao Li ◽  
Yongliang Wen ◽  
Yunming Ye ◽  
Xiaofeng Zhang
Keyword(s):  
Short Term Memory ◽  
Weather Forecast ◽  
Vital Role ◽  
Data Sets ◽  
Short Term ◽  
Learning Techniques ◽  
Radar Echo ◽  
Hidden States ◽  
Better Than

The task of precipitation nowcasting is significant in the operational weather forecast. The radar echo map extrapolation plays a vital role in this task. Recently, deep learning techniques such as Convolutional Recurrent Neural Network (ConvRNN) models have been designed to solve the task. These models, albeit performing much better than conventional optical flow based approaches, suffer from a common problem of underestimating the high echo value parts. The drawback is fatal to precipitation nowcasting, as the parts often lead to heavy rains that may cause natural disasters. In this paper, we propose a novel interaction dual attention long short-term memory (IDA-LSTM) model to address the drawback. In the method, an interaction framework is developed for the ConvRNN unit to fully exploit the short-term context information by constructing a serial of coupled convolutions on the input and hidden states. Moreover, a dual attention mechanism on channels and positions is developed to recall the forgotten information in the long term. Comprehensive experiments have been conducted on CIKM AnalytiCup 2017 data sets, and the results show the effectiveness of the IDA-LSTM in addressing the underestimation drawback. The extrapolation performance of IDA-LSTM is superior to that of the state-of-the-art methods.

scholarly journals The Relative Success of WTO Dispute Settlement and What Planet Would the EU Investment Court System Be On? A Rejoinder to AJIL Unbound Comments

AJIL Unbound ◽  
2015 ◽  
Vol 109 ◽  
pp. 316-318
Author(s):  
Joost Pauwelyn
Keyword(s):  
World Trade ◽  
Public Perception ◽  
Dispute Settlement ◽  
Decision Makers ◽  
The Other ◽  
Court System ◽  
Wto Dispute Settlement ◽  
The Many ◽  
Better Than ◽  
The Eu

I am extremely grateful, and humbled, by the wealth of comments received on my AJIL article through this AJIL Unbound Symposium. One of the many points I take away from these reactions is, indeed, that my analysis offers a snapshot and that many of the critiques now leveled against Investor-State Dispute Settlement (ISDS) are, in Catherine Rogers’s words, “effectively recycled versions of criticisms that were originally leveled against the WTO and its decision-makers.” (Freya Baetens makes a similar point.)In this rejoinder, I would only like to make two points. Firstly, many commentators seem to think that in this article I took the normative position that World Trade Organization (WTO) dispute settlement is “better” than ISDS. Although I did point to the current discrepancy in public perception of the respective regimes, I purposefully avoided expressing any personal, normative position on one being “better” than the other (but apparently not explicitly enough).

scholarly journals Evaluation of WRF Model Simulations of Tornadic and Nontornadic Outbreaks Occurring in the Spring and Fall

2010 ◽  
Vol 138 (11) ◽  
pp. 4098-4119 ◽  
Author(s):  
Chad M. Shafer ◽  
Andrew E. Mercer ◽  
Lance M. Leslie ◽  
Michael B. Richman ◽  
Charles A. Doswell
Keyword(s):  
Wrf Model ◽  
Severe Weather ◽  
Weather Research And Forecasting ◽  
Synoptic Scale ◽  
Sea Level Pressure ◽  
Significant Events ◽  
Skill Scores ◽  
Time Of Year ◽  
Tornado Outbreaks ◽  
Thermodynamic Instability

Abstract Recent studies, investigating the ability to use the Weather Research and Forecasting (WRF) model to distinguish tornado outbreaks from primarily nontornadic outbreaks when initialized with synoptic-scale data, have suggested that accurate discrimination of outbreak type is possible up to three days in advance of the outbreaks. However, these studies have focused on the most meteorologically significant events without regard to the season in which the outbreaks occurred. Because tornado outbreaks usually occur during the spring and fall seasons, whereas the primarily nontornadic outbreaks develop predominantly during the summer, the results of these studies may have been influenced by climatological conditions (e.g., reduced shear, in the mean, in the summer months), in addition to synoptic-scale processes. This study focuses on the impacts of choosing outbreaks of severe weather during the same time of year. Specifically, primarily nontornadic outbreaks that occurred during the summer have been replaced with outbreaks that do not occur in the summer. Subjective and objective analyses of the outbreak simulations indicate that the WRF’s capability of distinguishing outbreak type correctly is reduced when the seasonal constraints are included. However, accuracy scores exceeding 0.7 and skill scores exceeding 0.5 using 1-day simulation fields of individual meteorological parameters, show that precursor synoptic-scale processes play an important role in the occurrence or absence of tornadoes in severe weather outbreaks. Low-level storm-relative helicity parameters and synoptic parameters, such as geopotential heights and mean sea level pressure, appear to be most helpful in distinguishing outbreak type, whereas thermodynamic instability parameters are noticeably both less accurate and less skillful.

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