Cone Of Uncertainty Research Articles

Time-modulated planar arrays enabled directional modulation symbol synthesis

Different from linear arrays that can resolve only one angular component, two-dimensional (2-D) array patterns enable to remove the cone of uncertainty and right/left ambiguities generated in a linear array. Motivated by this, a novel structure of 2-D time-modulated planar arrays (TMPAs) is developed to synthesize directional modulation (DM) symbols in this paper. The proposed 2-D TMPA DM transmitter can synthesize multicarrier DM symbols with suppressing the mirror harmonic frequencies and provide a higher power efficiency. This is demonstrated by way of simulations of a multi-beam 8*8 element planar antenna array and its security performance is shown via the bit error rate (BER) metric.

Beyond the Cone of Uncertainty: Exploring the Flexibility of Animated Risk Trajectories for Improving Hurricane Risk Communication

Beyond the Cone of Uncertainty: Exploring the Flexibility of Animated Risk Trajectories for Improving Hurricane Risk Communication

Implementation of deep learning models in predicting ESG index volatility

The consideration of environmental, social, and governance (ESG) aspects has become an integral part of investment decisions for individual and institutional investors. Most recently, corporate leaders recognized the core value of the ESG framework in fulfilling their environmental and social responsibility efforts. While stock market prediction is a complex and challenging task, several factors associated with developing an ESG framework further increase the complexity and volatility of ESG portfolios compared with broad market indices. To address this challenge, we propose an integrated computational framework to implement deep learning model architectures, specifically long short-term memory (LSTM), gated recurrent unit, and convolutional neural network, to predict the volatility of the ESG index in an identical environment. A comprehensive analysis was performed to identify a balanced combination of input features from fundamental data, technical indicators, and macroeconomic factors to delineate the cone of uncertainty in market volatility prediction. The performance of the constructed models was evaluated using standard assessment metrics. Rigorous hyperparameter tuning and model-selection strategies were implemented to identify the best model. Furthermore, a series of statistical analyses was conducted to validate the robustness and reliability of the model. Experimental results showed that a single-layer LSTM model with a relatively small number of neurons provides a superior fit with high prediction accuracy relative to more complex models.

A new approach to generate dynamical cone of uncertainty for cyclone track forecast over North Indian Ocean

A new approach to generate dynamical cone of uncertainty for cyclone track forecast over North Indian Ocean

Visualizing Uncertainty in Hurricane Forecasts with Animated Risk Trajectories

Abstract Hurricane forecasts are often communicated through visualizations depicting the possible future track of the storm. The cone of uncertainty (COU) is a commonly used visualization, but the graphic is prone to misinterpretation such as thinking only locations contained within the cone’s boundary are at risk. In this study, we investigated the utility of conveying hurricane forecast tracks using a set of animated icons, each representing an instance of a possible storm path. We refer to this new visualization as animated risk trajectories (ARTs). We measured nonexperts’ perception of risk when viewing simplified, hypothetical hurricane forecasts presented as ARTs or COUs. To measure perception of risk for each visualization type, we designed experiments to have participants make decisions to evacuate individual towns at varying distances from the most likely forecast path of a storm. The ARTs led to greater risk perception in areas that fell beyond the cone’s boundaries. Nonexperts’ interpretation of risk was impacted by the visual properties of the ARTs, such as the distribution of the icons, including their density, and whether the distribution was unimodal or bimodal. This supports the suggestion that ARTs can have value in communicating spatial–temporal uncertainty. Significance Statement Because of the inherent uncertainties in weather forecasts and emergency management planning, communicating hurricane risk to the public is a unique challenge for decision-makers. Our study investigated the effect of conveying uncertainty in hurricane forecast tracks using a distribution of animated icons [animated risk trajectories (ARTs)], which correspond to potential hurricane tracks that evolve over time. This visualization was compared with a simplified cone-of-uncertainty graphic, such as those used by the National Hurricane Center. We found that ARTs offered flexibility in conveying information about hurricane risk, such as the magnitude of the risk via the number of icons and the location of the risk via the distribution of icons.

A Simple Bias and Uncertainty Scheme for Tropical Cyclone Intensity Change Forecasts

Abstract To better forecast tropical cyclone (TC) intensity change and understand forecast uncertainty, it is critical to recognize the inherent limitations of forecast models. The distributions of intensity change for statistical–dynamical models are too narrow, and some intensity change forecasts are shown to have larger errors and biases than others. The Intensity Bias and Uncertainty Scheme (IBUS) is developed in an intensity change framework, which estimates the bias and the standard deviation of intensity forecast errors. The IBUS is developed and applied to the Decay Statistical Hurricane Intensity Prediction Scheme (DSHP), the Logistic Growth Equation Model (LGEM), and official National Hurricane Center (NHC) forecasts (OFCL) separately. The analysis uses DSHP, LGEM, and OFCL forecasts from 2010 to 2019 in both the Atlantic and east Pacific basins. Each IBUS contains both a bias correction and forecast uncertainty estimate that is tested on the training dataset and evaluated on the 2020 season. The IBUS is able to reduce intensity biases and improve forecast errors beyond 120 h in each model and basin relative to the original forecasts. The IBUS is also able to communicate forecast uncertainty that explains ∼7%–11% of forecast variance at 48 h for DSHP and LGEM in the Atlantic. Better performance is found in the east Pacific at 96 h where the IBUS explains up to 30% of the errors in DSHP and 14% of the errors for LGEM. The IBUS for OFCL explains 9%–13% of the 48-h forecast uncertainty in the Atlantic and east Pacific with up to 30% variance explained for east Pacific forecasts at 96 h. IBUS for OFCL has the capability to provide intensity forecast uncertainty similar to the “cone of uncertainty” for track forecasts.

An Interdisciplinary Approach to Evaluate Public Comprehension of the “Cone of Uncertainty” Graphic

Abstract The accurate interpretation of hurricane risk graphics is expected to benefit public decision-making. To investigate public interpretation and suggest improvements to graphical designs, an interdisciplinary, mixed-methods approach is being undertaken. Drawing on a series of focus groups with Miami residents that focused on understanding interpretations of the National Hurricane Center’s (NHC) track forecast cone or “Cone of Uncertainty,” we developed an online survey targeting a much larger sample of Florida residents (n = 2,847). The findings from this survey are the primary focus of this short article. We attempt to answer three questions: 1) What are the most frequent and trusted sources of information that Florida residents use when they learn that a hurricane is coming their way? 2) How accurately are Florida residents able to interpret risk based on the NHC Cone of Uncertainty graphic? 3) What is the relationship, if any, between the number of correct interpretations and income, age, education, housing location, housing type, or “most trusted” sources of information? Unlike previous public surveys that focused more on evacuation decisions, forecast usage, and perception of hurricane risk, our approach specifically pays attention to the details of design elements of the forecast graphics with the long-term goal of minimizing misinterpretation of future graphics. Our analysis suggests that many residents have difficulty interpreting several aspects, suggesting a rethink on how to graphically communicate aspects such as uncertainty, the size of the storm, areas of likely damage, watches and warnings, and wind intensity categories. Graphical communication strategies need to be revised to better support the different ways in which people understand forecast products, and these strategies should be tested for validity in real world settings.

Exploring the Impact of Visualization Design on Non-Expert Interpretation of Hurricane Forecast Path

Hurricane forecast graphics have the challenging task of communicating information about spatial and temporal uncertainty. Although forecasting accuracy has improved, the popular track forecast cone or “Cone of Uncertainty” graphic, produced by the National Hurricane Center, is poorly understood by the general public. A better understanding of the forecast can potentially assist in timely decisions and life-saving actions. This study evaluates the impact of visualization design, tropical cyclone characteristics, subjective numeracy, and subjective graphicacy on visual attention to and user interpretation of hurricane forecast graphics. Forty-three non-expert participants completed forecast path estimates for nine tropical cyclones, comparing their responses when using the National Hurricane Center’s cone of uncertainty graphic and two alternative forecast visualizations. Results show that design modifications did not alter visual attention patterns or improve interpretations. Results also indicate that subjective numeracy, subjective graphicacy, and tropical cyclone characteristics, in combination, influence estimates of hurricane forecast tracks. The findings from this study inform redesign efforts of hurricane risk communication products.

Design guidelines for hurricane risk forecast to non-expert users

Hurricane forecast graphics, such as the cone of uncertainty and the hurricane threat impacts graphics, are provided to assist the general public in assessing their risk and in making protective action decisions for an approaching storm. Many researchers have demonstrated that hurricane forecast graphics are often misinterpreted by the general public. Few studies, however, have investigated the impact of design elements of these graphics on interpretations of hurricane risk communication for non-expert users. This study provides design guidelines for improving hurricane risks forecast products based on a literature review.

Informational Determinants of Large-Area Hurricane Evacuations

Abstract This study reports on two experiments to investigate the informational determinants of hurricane evacuation decisions (temporal and spatial). Whereas most observational and experimental studies in this domain address the public’s response to forecast information, this study addresses emergency management decisions. Using a subject sample of emergency managers and other public safety leaders, contrasted with a more typical university subject pool, this study presents an experimental design that overcomes the counterfactual problem present in all prior published experiments, by relying on an actual storm (Hurricane Rita) with a known outcome. Several methodological advancements are presented, including the use of an established numeracy instrument, integration of advanced hydrodynamic forecasts, and use of a loss aversion frame to improve generalizability. Results indicate that the availability of additional forecast information (e.g., wind speed, forecast tracks) significantly increases the probability and improves the timing of early voluntary evacuation. However, we observe that more numerate subjects are less likely to avoid relying upon forecast information that is characterized by probability (e.g., the uncertainty in the forecast track, sometimes referred to as the “cone of uncertainty”). Consequently, more numerate emergency managers are almost twice as likely as less numerate ones to provide additional evacuation time to their coastal communities, and they do so by longer than a typical workday (8.8 h). Results also indicate that subjects knowingly overevacuate large populations when making spatial mandatory evacuation orders. However, results indicate that numeracy mitigates this effect by more than half in terms of the population subject to mandatory evacuation.

News Sentiment Informed Time-series Analyzing AI (SITALA) to curb the spread of COVID-19 in Houston

Coronavirus disease (COVID-19) has evolved into a pandemic with many unknowns. Houston, located in the Harris County of Texas, is becoming the next hotspot of this pandemic. With a severe decline in international and inter-state travel, a model at the county level is needed as opposed to the state or country level. Existing approaches have a few drawbacks. Firstly, the data used is the number of COVID-19 positive cases instead of positivity. The former is a function of the number of tests carried out while the number of tests normalizes the latter. Positivity gives a better picture of the spread of this pandemic as, with time, more tests are being administered. Positivity under 5% has been desired for the reopening of businesses to almost 100% capacity. Secondly, the data used by models like SEIRD (Susceptible, Exposed, Infectious, Recovered, and Deceased) lacks information about the sentiment of people concerning coronavirus. Thirdly, models that make use of social media posts might have too much noise and misinformation. On the other hand, news sentiment can capture long-term effects of hidden variables like public policy, opinions of local doctors, and disobedience of state-wide mandates. The present study introduces a new artificial intelligence (i.e., AI) model, viz., Sentiment Informed Time-series Analyzing AI (SITALA), trained on COVID-19 test positivity data and news sentiment from over 2750 news articles for Harris county. The news sentiment was obtained using IBM Watson Discovery News. SITALA is inspired by Google-Wavenet architecture and makes use of TensorFlow. The mean absolute error for the training dataset of 66 consecutive days is 2.76, and that for the test dataset of 22 consecutive days is 9.6. A cone of uncertainty is provided within which future COVID-19 test positivity has been shown to fall with high accuracy. The model predictions fare better than a published Bayesian-based SEIRD model. The model forecasts that in order to curb the spread of coronavirus in Houston, a sustained negative news sentiment (e.g., death count for COVID-19 will grow at an alarming rate in Houston if mask orders are not followed) will be desirable. Public policymakers may use SITALA to set the tone of the local policies and mandates.

The Cone of Uncertainty:Planning in a Pandemic

In Short No good precedents exist for forecasts to inform state policy makers about the enrollment and financial impacts of the COVID-19 pandemic on public institutions. To address the resulting un...

A combined probabilistic framework to support investment appraisal under uncertainty in desalination projects: an application to Kuwait's water/energy nexus

Abstract Quantifying uncertainty over technologies, costs, and prices that stem from site-specific conditions, technological particularities and future projections is an important element in the investment appraisal of desalination facilities. Yet, the majority of economic assessments in the field of desalination plants, so far, use deterministic estimation methods based on ‘best guess’ estimates and ceteris paribus sensitivity analyses. Aiming to fill this gap, this paper introduces a new approach towards comparing alternative technological options for desalination facilities under uncertainty based on the Levelized Cost of Water (LCOW). The proposed framework combines Monte Carlo simulations with scenario analysis and random-walk-based models to account for the cone of uncertainty of the LCOW. For purely illustrative purposes, five alternative combinations of desalination technologies and energy sources are examined in the State of Kuwait. The findings show that the proposed framework, although it cannot eliminate uncertainty, can assist decision-makers in managing it by framing the range of possible outcomes of the LCOW. In this way, it offers an insight into the accuracy of the estimates and allows the validation of the impact of risks and uncertainties against the acceptable tolerance level. Yet, several issues need to be addressed in future research.

Development Cycle Modeling: Resource Estimation

This paper presents results produced by a domain-independent system development model that enables objective and quantitative calculation of certain development cycle characteristics. The presentation recounts the model’s motivation and includes an outline of the model’s structure. The outline shows that the model is constructive. As such, it provides an explanatory mechanism for the results that it produces, not just a representation of qualitative observations or measured data. The model is a Statistical Agent-based Model of Development and Evaluation (SAbMDE); and it appears to be novel with respect to previous design theory and methodology work. This paper focuses on one development cycle characteristic: resource utilization. The model’s resource estimation capability is compared to Boehm’s long-used software development estimation techniques. His Cone of Uncertainty (COU) captures project estimation accuracy empirically at project start but intuitively over a project’s duration. SAbMDE calculates estimation accuracy at start up and over project duration; and SAbMDE duplicates the COU’s empirical values. Additionally, SAbMDE produces results very similar to the Constructive Cost Model (COCOMO) effort estimation for a wide range of input values.

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

AbstractThere 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.

Application of Organizational Agility to Enhance the Predictability of Costs and Increase the Probability of Creating Value

In order to create any value, an organization needs to incur costs. This article builds on the original concept of the ‘Cone of Uncertainty’ for costs. However, the focus of an organization should mainly be on value. To visualize value, the concept of a ‘Double Cone of Uncertainty’ is introduced. Organizations often focus on reducing costs instead of enhancing the creation of value because costs are easier to measure and in control of the organization. Value is harder to measure, and here the customer is in charge. In addition, value creation lies further in the future. The article shows, with IT development as an example, how organizational agility and emergent product development can enhance the predictability of costs by time-boxing with stable, multidisciplinary teams. Although organizational agility cannot enhance the predictability of value, it can enhance the probability of value with emergent product development and quick and frequent feedback from customers. This evolutionary process of trial and error is essential in complicated and complex fast-changing environments.

Evaluating the Degree of Uncertainty of Research Activities in Industry 4.0

Research and development (R&D) are always oriented towards new discoveries, based on original terms or hypotheses, and their concluding outcomes are often uncertain. The present work focused on the degree of uncertainty for R&D activities. In fact, uncertainty makes it difficult to quantify the time and resources needed to achieve a final outcome, create a work plan and budget, and finalize the resulting “innovative” products or services that could be transferred or exchanged in a specific market. The present work attempts to indicate the degree of uncertainty of the research activities developed by a set of firms. The method used aimed to quantify the five criteria defined by the Manual of Frascati. Through the creation of an uncertainty cloud, a cone of uncertainty was defined following an approach based on project management. The evaluation grid was characterized by the decomposition of the different variables divided into quartiles, which allowed for the detection of the evolution of the project and each of its component. The ancillary objective aim was to also observe the development degree of these industries towards a framework of Industry 4.0.

Evaluation of Cone of Uncertainty in Tropical Cyclone Track Forecast Over North Indian Ocean Issued by India Meteorological Department

ABSTRACT India Meteorological Department (IMD) introduced cone of uncertainty (COU) in cyclonic disturbances (CDs) alongwith the 72 hr track forecast over the north India Ocean (NIO) in 2009. The track forecast for CDs is issued for + 6, + 12, + 18, + 24, + 36, + 48, + 60 and + 72 hrs time period from the stage of deep depression onwards. An attempt is made to evaluate COU forecast issued by IMD during 2009-2011(3 years). The size of the cone is deduced from climatological track forecast errors. The accuracy of COU forecast has been analysed with respect to basin of formation, season of formation, intensity and type of track (climatological/straight moving and recurving/looping type) of CDs by calculating percentage of total number of forecasts in each category lying within COU. The observed track lies within the forecast COU in about 60% of the cases over the NIO. The accuracy of COU forecast is about 66% in post-monsoon season and about 50% in pre-monsoon season. The observed track lies within the forecast COU in 90% cases of climatological/straight moving CDs and 39% cases of recurving/ looping CDs. The observed track lies within COU forecast in about 71% cases of severe cyclonic storm and 37% cases of cyclonic storm/deep depression.

Tropical Cyclone Forecast Verification by India Meteorological Department for North Indian Ocean: a Review

ABSTRACT The tropical cyclone (TC) forecast verification procedure followed by India Meteorological Department (IMD) is reviewed herewith as compared to the standard prescribed by World Meteorological Organisation. The limitations in the present procedure and the future scope are presented and analysed. The IMD has considerably increased its efforts in recent years in the areas of TC forecasting and verification. Many facilities have been developed to enable a detailed assessment of the global model’s performance in the forecasting of TCs as well as operational TC forecast performance. Further development of these facilities is being undertaken by IMD. There are grey areas in TC forecast verification of IMD in the field of calculation of forecast error in surface wind radii in geographical quadrants, which is yet to start. Also there is need for improvement in verification of storm surge, wave height, coastal inundation forecasts and heavy rainfall forecasts by calculating the skill parameters and also by improving the analysis data required for verification of forecasts. Similarly there is need for verification of strike probability as well as dynamical cone of uncertainty as given by ensemble prediction system.

Back-projecting secondary craters using a cone of uncertainty

In this paper we present an extension to the Large Crater Clustering (LCC) tool set which places a cone of uncertainty around the trajectories of secondary impact craters to determine potential locations of source craters. The LCC tool set was a first step in the spatial quantification of primary and secondary cratering processes, which allows planetary geologists to accurately estimate the geologic age of a celestial surface. This work builds on the LCC tool set by accounting for the ambiguity of flight path trajectories through a Python script that leverages ArcGIS's ArcPy library. We chronicle the mechanics of the script, which creates geodetically correct cones then counts them within equally sized cells of a vector grid. We describe the process that was used to derive the shape of the cone and provide parameters for the sizes of the cones and the grid. We demonstrate that the cone of uncertainty has the ability to compensate for error in secondary crater trajectories by introducing deviation in the trajectory bearing and comparing the predicted primary crater location. We use two study areas on Mars as well as the entire lunar surface to illustrate the usefulness of the extension as an aid to human interpretation of back-projections.