June 20, 2021

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Uncertainties in Numerical Weather Prediction

Uncertainties in Numerical Weather Prediction
Author : Haraldur Olafsson,Jian-Wen Bao
Publisher : Elsevier
Release Date : 2020-12-08
Category : Computers
Total pages :364
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Uncertainties in Numerical Weather Prediction is a comprehensive work on the most current understandings of uncertainties and predictability in numerical simulations of the atmosphere. It provides general knowledge on all aspects of uncertainties in the weather prediction models in a single, easy to use reference. The book illustrates particular uncertainties in observations and data assimilation, as well as the errors associated with numerical integration methods. Stochastic methods in parameterization of subgrid processes are also assessed, as are uncertainties associated with surface-atmosphere exchange, orographic flows and processes in the atmospheric boundary layer. Through a better understanding of the uncertainties to watch for, readers will be able to produce more precise and accurate forecasts. This is an essential work for anyone who wants to improve the accuracy of weather and climate forecasting and interested parties developing tools to enhance the quality of such forecasts. Provides a comprehensive overview of the state of numerical weather prediction at spatial scales, from hundreds of meters, to thousands of kilometers Focuses on short-term 1-15 day atmospheric predictions, with some coverage appropriate for longer-term forecasts Includes references to climate prediction models to allow applications of these techniques for climate simulations

Modeling Uncertainty of Numerical Weather Predictions Using Learning Methods

Modeling Uncertainty of Numerical Weather Predictions Using Learning Methods
Author : Ashkan Zarnani,University of Alberta. Department of Electrical and Computer Engineering
Publisher : Unknown
Release Date : 2014
Category : Numerical weather forecasting
Total pages :127
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Weather forecasting is one of the most vital tasks in many applications ranging from severe weather hazard systems to energy production. Numerical weather prediction (NWP) systems are commonly used state-of-the-art atmospheric models that provide point forecasts as deterministic predictions arranged on a three-dimensional grid. However, there is always some level of error and uncertainty in the forecasts due to inaccuracies of initial conditions, the chaotic nature of weather, etc. Such uncertainty information is crucial in decision making and optimization processes involved in many applications. A common representation of forecast uncertainty is a Prediction Interval (PI) that determines a minima, maxima and confidence level for each forecast, e.g. [2°C, 15°C]-95%. In this study, we investigate various methods that can model the uncertainty of NWP forecasts and provide PIs for the forecasts accordingly. In particular, we are interested in analyzing the historical performance of the NWP system as a valuable source for uncertainty modeling. Three different classes of methods are developed and applied for this problem. First, various clustering algorithms (including fuzzy c-means) are employed in concert with fitting appropriate probability distributions to obtain statistical models that can dynamically provide PIs depending on the forecast context. Second, a range of quantile regression methods (including kernel quantile regression) are studied that can directly model the PI boundaries as a function of influential features. In the third class, we focus on various time series modeling approaches including heteroscedasticity modeling methods that can provide forecasts of conditional mean and conditional variance of the target for any forecast horizon. iv All presented PI computation methods are empirically evaluated using a developed comprehensive verification framework in a set of experiments involving real-world data sets of NWP forecasts and observations. A key component is proposed in the evaluation process that would lead to a considerably more reliable judgment. Results show that PIs obtained by the ARIMA-GARCH model (for up to 6-hour-ahead forecasts) and Spline Quantile Regression (for longer leads) provide interval forecasts with satisfactory reliability and significantly better skill. This can lead to improvements in forecast value for many systems that rely on the NWP forecasts.

Parametric Uncertainty in Numerical Weather Prediction Models

Parametric Uncertainty in Numerical Weather Prediction Models
Author : Pirkka Ollinaho
Publisher : Unknown
Release Date : 2014
Category :
Total pages :129
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High Resolution Numerical Weather Prediction, Distributed Hydrological Models and Uncertainty - Towards a Unified Approach

High Resolution Numerical Weather Prediction, Distributed Hydrological Models and Uncertainty - Towards a Unified Approach
Author : Philip M. Younger
Publisher : Unknown
Release Date : 2007
Category :
Total pages :129
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Computational Science – ICCS 2019

Computational Science – ICCS 2019
Author : João M. F. Rodrigues,Pedro J. S. Cardoso,Jânio Monteiro,Roberto Lam,Valeria V. Krzhizhanovskaya,Michael H. Lees,Jack J. Dongarra,Peter M.A. Sloot
Publisher : Springer
Release Date : 2019-06-07
Category : Computers
Total pages :663
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The five-volume set LNCS 11536, 11537, 11538, 11539 and 11540 constitutes the proceedings of the 19th International Conference on Computational Science, ICCS 2019, held in Faro, Portugal, in June 2019. The total of 65 full papers and 168 workshop papers presented in this book set were carefully reviewed and selected from 573 submissions (228 submissions to the main track and 345 submissions to the workshops). The papers were organized in topical sections named: Part I: ICCS Main Track Part II: ICCS Main Track; Track of Advances in High-Performance Computational Earth Sciences: Applications and Frameworks; Track of Agent-Based Simulations, Adaptive Algorithms and Solvers; Track of Applications of Matrix Methods in Artificial Intelligence and Machine Learning; Track of Architecture, Languages, Compilation and Hardware Support for Emerging and Heterogeneous Systems Part III: Track of Biomedical and Bioinformatics Challenges for Computer Science; Track of Classifier Learning from Difficult Data; Track of Computational Finance and Business Intelligence; Track of Computational Optimization, Modelling and Simulation; Track of Computational Science in IoT and Smart Systems Part IV: Track of Data-Driven Computational Sciences; Track of Machine Learning and Data Assimilation for Dynamical Systems; Track of Marine Computing in the Interconnected World for the Benefit of the Society; Track of Multiscale Modelling and Simulation; Track of Simulations of Flow and Transport: Modeling, Algorithms and Computation Part V: Track of Smart Systems: Computer Vision, Sensor Networks and Machine Learning; Track of Solving Problems with Uncertainties; Track of Teaching Computational Science; Poster Track ICCS 2019 Chapter “Comparing Domain-decomposition Methods for the Parallelization of Distributed Land Surface Models” is available open access under a Creative Commons Attribution 4.0 International License via link.springer.com.

Uncertainty Propagation in Complex Coupled Flood Risk Models Using Numerical Weather Prediction and Weather Radars

Uncertainty Propagation in Complex Coupled Flood Risk Models Using Numerical Weather Prediction and Weather Radars
Author : Yunqing Xuan
Publisher : Unknown
Release Date : 2007
Category :
Total pages :250
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Uncertainty in Mesoscale Numerical Weather Prediction

Uncertainty in Mesoscale Numerical Weather Prediction
Author : Anonim
Publisher : Unknown
Release Date : 2015
Category :
Total pages :108
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A Case Study of the Persistence of Weather Forecast Model Errors

A Case Study of the Persistence of Weather Forecast Model Errors
Author : Barbara Sauter
Publisher : Unknown
Release Date : 2005
Category : Error analysis (Mathematics)
Total pages :40
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Decision makers could frequently benefit from information about the amount of uncertainty associated with a specific weather forecast. Automated numerical weather prediction models provide deterministic weather forecast values with no estimate of the likely error. This case study examines the day-to-day persistence of forecast errors of basic surface weather parameters for four sites in northern Utah. Although exceptionally low or high forecast errors on one day are more likely to be associated with a similar quality forecast the following day, the relationship is not considered strong enough to provide beneficial guidance to users without meteorological expertise. Days resulting in average forecast errors showed no persistence in the quality of the subsequent day's forecast. More sophisticated methods are needed to generate and portray weather forecast uncertainty information.

Conference on Numerical Weather Prediction

Conference on Numerical Weather Prediction
Author : Anonim
Publisher : Unknown
Release Date : 1999
Category : Numerical weather forecasting
Total pages :129
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Short- and Medium-Range Numerical Weather Prediction

Short- and Medium-Range Numerical Weather Prediction
Author : Matsuno, T.
Publisher : Unknown
Release Date : 1987
Category : Meteorology
Total pages :831
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Journal of Hydrometeorology

Journal of Hydrometeorology
Author : Anonim
Publisher : Unknown
Release Date : 2007
Category : Hydrometeorology
Total pages :129
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Monthly Weather Review

Monthly Weather Review
Author : Anonim
Publisher : Unknown
Release Date : 2006-05
Category : Meteorology
Total pages :129
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Advances in Computational Oceanography

Advances in Computational Oceanography
Author : Anonim
Publisher : Unknown
Release Date : 2006
Category : Oceanography
Total pages :200
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Masters of Uncertainty

Masters of Uncertainty
Author : Phaedra Daipha
Publisher : University of Chicago Press
Release Date : 2015-11-17
Category : Social Science
Total pages :272
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Though we commonly make them the butt of our jokes, weather forecasters are in fact exceptionally good at managing uncertainty. They consistently do a better job calibrating their performance than stockbrokers, physicians, or other decision-making experts precisely because they receive feedback on their decisions in near real time. Following forecasters in their quest for truth and accuracy, therefore, holds the key to the analytically elusive process of decision making as it actually happens. In Masters of Uncertainty, Phaedra Daipha develops a new conceptual framework for the process of decision making, after spending years immersed in the life of a northeastern office of the National Weather Service. Arguing that predicting the weather will always be more craft than science, Daipha shows how forecasters have made a virtue of the unpredictability of the weather. Impressive data infrastructures and powerful computer models are still only a substitute for the real thing outside, and so forecasters also enlist improvisational collage techniques and an omnivorous appetite for information to create a locally meaningful forecast on their computer screens. Intent on capturing decision making in action, Daipha takes the reader through engrossing firsthand accounts of several forecasting episodes (hits and misses) and offers a rare fly-on-the-wall insight into the process and challenges of producing meteorological predictions come rain or come shine. Combining rich detail with lucid argument, Masters of Uncertainty advances a theory of decision making that foregrounds the pragmatic and situated nature of expert cognition and casts into new light how we make decisions in the digital age.