Download or read book The State of the Art of Probabilistic Weather Forecasting written by Allan H. Murphy and published by . This book was released on 1984 with total page 64 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Decision Making and Change in Human Affairs written by H. Jungermann and published by Springer Science & Business Media. This book was released on 2012-12-06 with total page 525 pages. Available in PDF, EPUB and Kindle. Book excerpt: It is only just recently that people have the tools to judge how well they are doing when making decisions. These tools were conceptualized in the seventeenth century. Since then many people have worked to sharpen the concepts, and to explore how these can be applied further. The problems of decision-making and the theory developed correspondingly have drawn the interest of mathematicians, psychologists, statisticians, economists, philosophers, organizational experts, sociologists, not only for their general relevance, but also for a more intrinsic fascination. There are quite a few institutionalized activities to disseminate results and stimulate research in decision-making. For about a decade now a European organizational structure, centered mainly around the psy chological interest in decision-making. There have been conferences in Hamburg, Amsterdam, Uxbridge, Rome and Darmstadt. Conference papers have been partly published+. The organization has thus stabilized, and its re latively long history makes it interesting to see what kind of developments occurred, within the area of interest.
Download or read book Statistical Postprocessing of Ensemble Forecasts written by Stéphane Vannitsem and published by Elsevier. This book was released on 2018-05-17 with total page 362 pages. Available in PDF, EPUB and Kindle. Book excerpt: Statistical Postprocessing of Ensemble Forecasts brings together chapters contributed by international subject-matter experts describing the current state of the art in the statistical postprocessing of ensemble forecasts. The book illustrates the use of these methods in several important applications including weather, hydrological and climate forecasts, and renewable energy forecasting. After an introductory section on ensemble forecasts and prediction systems, the second section of the book is devoted to exposition of the methods available for statistical postprocessing of ensemble forecasts: univariate and multivariate ensemble postprocessing are first reviewed by Wilks (Chapters 3), then Schefzik and Möller (Chapter 4), and the more specialized perspective necessary for postprocessing forecasts for extremes is presented by Friederichs, Wahl, and Buschow (Chapter 5). The second section concludes with a discussion of forecast verification methods devised specifically for evaluation of ensemble forecasts (Chapter 6 by Thorarinsdottir and Schuhen). The third section of this book is devoted to applications of ensemble postprocessing. Practical aspects of ensemble postprocessing are first detailed in Chapter 7 (Hamill), including an extended and illustrative case study. Chapters 8 (Hemri), 9 (Pinson and Messner), and 10 (Van Schaeybroeck and Vannitsem) discuss ensemble postprocessing specifically for hydrological applications, postprocessing in support of renewable energy applications, and postprocessing of long-range forecasts from months to decades. Finally, Chapter 11 (Messner) provides a guide to the ensemble-postprocessing software available in the R programming language, which should greatly help readers implement many of the ideas presented in this book. Edited by three experts with strong and complementary expertise in statistical postprocessing of ensemble forecasts, this book assesses the new and rapidly developing field of ensemble forecast postprocessing as an extension of the use of statistical corrections to traditional deterministic forecasts. Statistical Postprocessing of Ensemble Forecasts is an essential resource for researchers, operational practitioners, and students in weather, seasonal, and climate forecasting, as well as users of such forecasts in fields involving renewable energy, conventional energy, hydrology, environmental engineering, and agriculture. Consolidates, for the first time, the methodologies and applications of ensemble forecasts in one succinct place Provides real-world examples of methods used to formulate forecasts Presents the tools needed to make the best use of multiple model forecasts in a timely and efficient manner
Download or read book Applied Probabilistic Forecasting written by Roman Binter and published by . This book was released on 2012 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: In any actual forecast, the future evolution of the system is uncertain and the forecasting model is mathematically imperfect. Both, ontic uncertainties in the future (due to true stochasticity) and epistemic uncertainty of the model (reflecting structural imperfections) complicate the construction and evaluation of probabilistic forecast. In almost all nonlinear forecast models, the evolution of uncertainty in time is not tractable analytically and Monte Carlo approaches ("ensemble forecasting") are widely used. This thesis advances our understanding of the construction of forecast densities from ensembles, the evolution of the resulting probability forecasts and methods of establishing skill (benchmarks). A novel method of partially correcting the model error is introduced and shown to outperform a competitive approach. The properties of Kernel dressing, a method of transforming ensembles into probability density functions, are investigated and the convergence of the approach is illustrated. A connection between forecasting and Information theory is examined by demonstrating that Kernel dressing via minimization of Ignorance implicitly leads to minimization of Kulback-Leibler divergence. The Ignorance score is critically examined in the context of other Information theory measures. The method of Dynamic Climatology is introduced as a new approach to establishing skill (benchmarking). Dynamic Climatology is a new, relatively simple, nearest neighbor based model shown to be of value in benchmarking of global circulation models of the ENSEMBLES project. ENSEMBLES is a project funded by the European Union bringing together all major European weather forecasting institutions in order to develop and test state-of-the-art seasonal weather forecasting models. Via benchmarking the seasonal forecasts of the ENSEMBLES models we demonstrate that Dynamic Climatology can help us better understand the value and forecasting performance of large scale circulation models. Lastly, a new approach to correcting (improving) imperfect model is presented, an idea inspired by [63]. The main idea is based on a two-stage procedure where a second stage 'corrective' model iteratively corrects systematic parts of forecasting errors produced by a first stage 'core' model. The corrector is of an iterative nature so that at a given time t the core model forecast is corrected and then used as an input into the next iteration of the core model to generate a time t + 1 forecast. Using two nonlinear systems we demonstrate that the iterative corrector is superior to alternative approaches based on direct (non-iterative) forecasts. While the choice of the corrector model class is flexible, we use radial basis functions. Radial basis functions are frequently used in statistical learning and/or surface approximations and involve a number of computational aspects which we discuss in some detail.
Download or read book Operational Weather Forecasting written by Peter Michael Inness and published by John Wiley & Sons. This book was released on 2012-12-06 with total page 276 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book offers a complete primer, covering the end-to-end process of forecast production, and bringing together a description of all the relevant aspects together in a single volume; with plenty of explanation of some of the more complex issues and examples of current, state-of-the-art practices. Operational Weather Forecasting covers the whole process of forecast production, from understanding the nature of the forecasting problem, gathering the observational data with which to initialise and verify forecasts, designing and building a model (or models) to advance those initial conditions forwards in time and then interpreting the model output and putting it into a form which is relevant to customers of weather forecasts. Included is the generation of forecasts on the monthly-to-seasonal timescales, often excluded in text-books despite this type of forecasting having been undertaken for several years. This is a rapidly developing field, with a lot of variations in practices between different forecasting centres. Thus the authors have tried to be as generic as possible when describing aspects of numerical model design and formulation. Despite the reliance on NWP, the human forecaster still has a big part to play in producing weather forecasts and this is described, along with the issue of forecast verification – how forecast centres measure their own performance and improve upon it. Advanced undergraduates and postgraduate students will use this book to understand how the theory comes together in the day-to-day applications of weather forecast production. In addition, professional weather forecasting practitioners, professional users of weather forecasts and trainers will all find this new member of the RMetS Advancing Weather and Climate series a valuable tool. Provides an end-to-end description of the weather forecasting process Clearly structured and pitched at an accessible level, the book discusses the practical choices that operational forecasting centres have to make in terms of what numerical models they use and when they are run. Takes a very practical approach, using real life case-studies to contextualize information Discusses the latest advances in the area, including ensemble methods, monthly to seasonal range prediction and use of ‘nowcasting’ tools such as radar and satellite imagery Full colour throughout Written by a highly respected team of authors with experience in both academia and practice. Part of the RMetS book series ‘Advancing Weather and Climate’
Download or read book Wind Power Ensemble Forecasting written by André Gensler and published by kassel university press GmbH. This book was released on 2019-01-16 with total page 216 pages. Available in PDF, EPUB and Kindle. Book excerpt: This thesis describes performance measures and ensemble architectures for deterministic and probabilistic forecasts using the application example of wind power forecasting and proposes a novel scheme for the situation-dependent aggregation of forecasting models. For performance measures, error scores for deterministic as well as probabilistic forecasts are compared, and their characteristics are shown in detail. For the evaluation of deterministic forecasts, a categorization by basic error measure and normalization technique is introduced that simplifies the process of choosing an appropriate error measure for certain forecasting tasks. Furthermore, a scheme for the common evaluation of different forms of probabilistic forecasts is proposed. Based on the analysis of the error scores, a novel hierarchical aggregation technique for both deterministic and probabilistic forecasting models is proposed that dynamically weights individual forecasts using multiple weighting factors such as weather situation and lead time dependent weighting. In the experimental evaluation it is shown that the forecasting quality of the proposed technique is able to outperform other state of the art forecasting models and ensembles.
Download or read book Sub seasonal Forecasting Using Large Ensembles of Data driven Global Weather Prediction Models written by Jonathan A. Weyn-Vanhentenryck and published by . This book was released on 2020 with total page 125 pages. Available in PDF, EPUB and Kindle. Book excerpt: The current state-of-the-art in numerical weather prediction (NWP) is to generate probabilistic forecasts using large ensembles consisting of equally-likely realizations of future weather. Such large ensembles, however, require significant computational resources. I have developed a purely data-driven weather prediction model using convolutional neural networks (CNNs) trained on globally-gridded analysis of the atmosphere. While this model only evolves a small set of key atmospheric variables and does not quite approach the performance of state-of-the-art NWP models, it has a number of desirable properties: 1) by using data remapped to a cubed sphere, our CNN model is a closed system which can be integrated forward indefinitely, 2) our model remains stable indefinitely, producing realistic atmospheric states and even a correct seasonal cycle when allowed to run freely for up to a year, and 3) our model executes extremely quickly, requiring only one tenth of a second for a 1-week forecast on a global 1.5-degree grid. Taking advantage of the efficient computation, I designed a large 320-member ensemble of CNNs using both initial-condition perturbations and stochastic model perturbations yielded by the internal randomness of training multiple CNNs. While the ensemble is under-dispersive, ensemble mean forecasts notably outperform single deterministic data-driven forecasts, but still lag the skill of the European Centre for Medium-Range Weather Forecasts (ECMWF) ensemble forecasts. Armed with an efficient large ensemble, I then target predictions on the sub-seasonal-to-seasonal (S2S) time frame, or about 2 weeks to 2 months out, where traditional NWP models struggle due to a lack of information from initial conditions and difficulty outperforming persistence forecasts of slowly-evolving earth system components such as ocean sea surface temperatures. Ensemble mean forecasts of 2-meter temperature and 850-hPa temperature from my CNN ensemble clearly outperform persistence forecasts across the S2S time frame. Evaluating full ensemble probabilistic forecasts using the continuous ranked probability score and the ranked proba- bility skill score, I demonstrate that my CNN ensemble provides nearly universal useful S2S skill relative to persistence and climatology, notably over most land masses instead of just over oceans. My ensemble even compares well with the ECMWF S2S ensemble, matching or exceeding the latter at forecast lead times of weeks 5-6, and particularly excels during the boreal spring and summer months, where the ECMWF ensemble is weakest. While my CNN ensemble shows great promise as an S2S forecasting tool, many opportunities remain to further improve it, especially for its predictions of long-term climate variability including the Madden-Julian Oscillation and the El Niño-Southern Oscillation.
Download or read book Uncertainties in Numerical Weather Prediction written by Haraldur Olafsson and published by Elsevier. This book was released on 2020-11-25 with total page 366 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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
Download or read book Probability Statistics And Decision Making In The Atmospheric Sciences written by Allan Murphy and published by CRC Press. This book was released on 2019-07-11 with total page 547 pages. Available in PDF, EPUB and Kindle. Book excerpt: Methodology drawn from the fields of probability. statistics and decision making plays an increasingly important role in the atmosphericsciences. both in basic and applied research and in experimental and operational studies. Applications of such methodology can be found in almost every facet of the discipline. from the most theoretical and global (e.g., atmospheric predictability. global climate modeling) to the most practical and local (e.g., crop-weather modeling forecast evaluation). Almost every issue of the multitude of journals published by the atmospheric sciences community now contain some or more papers involving applications of concepts and/or methodology from the fields of probability and statistics. Despite the increasingly pervasive nature of such applications. very few book length treatments of probabilistic and statistical topics of particular interest to atmospheric scientists have appeared (especially inEnglish) since the publication of the pioneering works of Brooks andCarruthers (Handbook of Statistical Methods in Meteorology) in 1953 and Panofsky and Brier-(some Applications of)statistics to Meteor) in 1958. As a result. many relatively recent developments in probability and statistics are not well known to atmospheric scientists and recent work in active areas of meteorological research involving significant applications of probabilistic and statistical methods are not familiar to the meteorological community as a whole.
Download or read book Invisible in the Storm written by Ian Roulstone and published by Princeton University Press. This book was released on 2013-02-21 with total page 344 pages. Available in PDF, EPUB and Kindle. Book excerpt: An accessible book that examines the mathematics of weather prediction Invisible in the Storm is the first book to recount the history, personalities, and ideas behind one of the greatest scientific successes of modern times—the use of mathematics in weather prediction. Although humans have tried to forecast weather for millennia, mathematical principles were used in meteorology only after the turn of the twentieth century. From the first proposal for using mathematics to predict weather, to the supercomputers that now process meteorological information gathered from satellites and weather stations, Ian Roulstone and John Norbury narrate the groundbreaking evolution of modern forecasting. The authors begin with Vilhelm Bjerknes, a Norwegian physicist and meteorologist who in 1904 came up with a method now known as numerical weather prediction. Although his proposed calculations could not be implemented without computers, his early attempts, along with those of Lewis Fry Richardson, marked a turning point in atmospheric science. Roulstone and Norbury describe the discovery of chaos theory's butterfly effect, in which tiny variations in initial conditions produce large variations in the long-term behavior of a system—dashing the hopes of perfect predictability for weather patterns. They explore how weather forecasters today formulate their ideas through state-of-the-art mathematics, taking into account limitations to predictability. Millions of variables—known, unknown, and approximate—as well as billions of calculations, are involved in every forecast, producing informative and fascinating modern computer simulations of the Earth system. Accessible and timely, Invisible in the Storm explains the crucial role of mathematics in understanding the ever-changing weather. Some images inside the book are unavailable due to digital copyright restrictions.
Download or read book Probability Forecasting written by Lawrence Ambrose Hughes and published by . This book was released on 1980 with total page 96 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Sensitivity Analysis of Probabilistic Multi model Ensemble Forecasts of Wintertime Fronts Over Northwestern Nevada written by Amanda Katherine Young and published by . This book was released on 2014 with total page 100 pages. Available in PDF, EPUB and Kindle. Book excerpt: Probabilistic ensemble forecasting has become an essential tool to numerical weather prediction. With the chaotic nature of the atmosphere, decisions made by operational meteorologists are made with imperfect weather models. These deterministic numerical weather forecasts can be complemented with the use of regional ensemble predictions incorporating enhanced probabilistic, statistical analysis tools. The challenge is providing better statistical information using ensemble probabilistic information forecasts of mesoscale frontal features to better characterize frontal precipitation fields, intensity, and direction of movement. The purpose of this study was aimed at drawing attention to certain probabilistic distribution patterns for specific mesoscale circulations when physical parameterizations and/or initial conditions are varied for specific ensemble forecast members. A statistical sensitivity error-trend analysis of multi-model (MM5, COAMPS, and WRF) ensemble prediction system (EPS) was conducted to provide insight into how inherent changes to model parameterizations, i.e. PBL, convection, radiation, and microphysics can manifest intrinsic variability to ensemble predictability. Most studies in ensemble prediction used a single model in an ensemble mode, using variations in model initial conditions as the basis to produce simulation ensemble members and in most cases the total ensemble members were limited to 6-10. A total of 153 ensemble members with a horizontal resolution of 36 km were evaluated for this study using three state of the art regional-mesoscale models. Its focus was directed towards the use of a multi-model EPS to measure the statistical sensitivity of a sequence of three winter-time fronts observed over western Nevada during the period of 12-27 December 2008. The corresponding analysis and evaluation underscored a process through which 500 hPa thermal field dataset temperature differences, as it applied to rank data calculated for the three cold frontal systems observed over the period of the 15 day simulation, can also be applied to ensemble model spread and error trend analysis. This study enabled the extension of the forecast simulation period to two weeks, which is the assumed predictability limit for atmospheric simulations. Therefore, it became apparent that the use of statistical rank data error trends and ensemble model spread can improve predictability of certain aspects of frontal activity based on COAMPS smaller (high a priori forecast accuracy) ensemble simulation spread as compared to MM5 and WRF larger (low a priori forecast accuracy) ensemble spread.
Download or read book Completing the Forecast written by National Research Council and published by National Academies Press. This book was released on 2006-10-09 with total page 124 pages. Available in PDF, EPUB and Kindle. Book excerpt: Uncertainty is a fundamental characteristic of weather, seasonal climate, and hydrological prediction, and no forecast is complete without a description of its uncertainty. Effective communication of uncertainty helps people better understand the likelihood of a particular event and improves their ability to make decisions based on the forecast. Nonetheless, for decades, users of these forecasts have been conditioned to receive incomplete information about uncertainty. They have become used to single-valued (deterministic) forecasts (e.g., "the high temperature will be 70 degrees Farenheit 9 days from now") and applied their own experience in determining how much confidence to place in the forecast. Most forecast products from the public and private sectors, including those from the National Oceanographic and Atmospheric Administration's National Weather Service, continue this deterministic legacy. Fortunately, the National Weather Service and others in the prediction community have recognized the need to view uncertainty as a fundamental part of forecasts. By partnering with other segments of the community to understand user needs, generate relevant and rich informational products, and utilize effective communication vehicles, the National Weather Service can take a leading role in the transition to widespread, effective incorporation of uncertainty information into predictions. "Completing the Forecast" makes recommendations to the National Weather Service and the broader prediction community on how to make this transition.
Download or read book On the Use of Probability Statements in Weather Forecasts written by Charles F. Roberts and published by . This book was released on 1965 with total page 24 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Final Report on Probabilistic Cloud Optimized Day ahead Forecasting System Based on WRF Solar written by Manajit Sengupta and published by . This book was released on 2022 with total page 64 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Probability Statistics And Decision Making In The Atmospheric Sciences written by Allan Murphy and published by . This book was released on 2024-10-31 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Monthly Weather Review written by and published by . This book was released on 2003 with total page 1164 pages. Available in PDF, EPUB and Kindle. Book excerpt:
