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| Author | : Eric P. Grimit |
| Publisher | : |
| Total Pages | : 146 |
| Release | : 2004 |
| Genre | : Weather forecasting |
| ISBN | : |
| Author | : Frederick Anthony Eckel |
| Publisher | : |
| Total Pages | : 242 |
| Release | : 2003-01-01 |
| Genre | : Atmospheric circulation |
| ISBN | : 9781423513162 |
This study developed and evaluated a short-range ensemble forecasting (SREF) system with the goal of producing useful forecast probability (FP). Real- time, 0 to 48-h forecasts from four different SREF systems were compared for 129 forecast cases over the Pacific Northwest. Eight analyses from different operational forecast centers were used as initial conditions (ICs) for running the Fifth-Generation Pennsylvania State University-National Center of Atmospheric Research Mesoscale Model (MM5). Additional ICs were generated through linear combinations of the original 8 analyses, but this did not result in an increase in FP skill commensurate with the increase in ensemble size. It was also found that an ensemble made up of unequally likely members can be skillful as long as all members at least occasionally perform well. Model error is a large source of forecast uncertainty and must be accounted for to maximize SREF utility, particularly for mesoscale, sensible weather phenomena. Inclusion of model perturbations in a SREF increased dispersion toward statistical consistency, but low dispersion remained problematic. Additionally, model perturbations notably improved FP skill (both reliability and resolution), revealing the significant influence of model uncertainty. Systematic model errors (i.e., biases) should always be removed from a SREF since they are a large part of forecast error but do not contribute to forecast uncertainty. A grid-based, 2-week, running-mean bias correction was shown to improve FP skill through: 1) better reliability by adjusting the ensemble mean toward the verification's mean, and 2) better resolution by reducing unrealistic ensemble variance.
| Author | : Stéphane Vannitsem |
| Publisher | : Elsevier |
| Total Pages | : 362 |
| Release | : 2018-05-17 |
| Genre | : Science |
| ISBN | : 012812248X |
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
| Author | : |
| Publisher | : |
| Total Pages | : 20 |
| Release | : 2003 |
| Genre | : |
| ISBN | : |
Probabilistic weather forecasting consists of finding a joint probability distribution for future weather quantities or events. It is typically done by using a numerical weather prediction model, perturbing the inputs to the model in various ways, often depending on data assimilation, and running the model for each perturbed set of inputs. The result is then viewed as an ensemble of forecasts, taken to be a sample from the joint probability distribution of the future weather quantities of interest. This is typically not feasible for mesoscale weather prediction carried out locally by organizations without the vast data and computing resources of national weather centers. Instead, we propose a simpler method which breaks with much previous practice by perturbing the outputs, or deterministic forecasts, from the model. Forecast errors are modeled using a geostatistical model, and ensemble members are generated by simulating realizations of the geostatistical model. The method is applied to 48-hour mesoscale forecasts of temperature in the US Pacific Northwest in 2000 and 2002. The resulting forecast intervals turn out to be well calibrated for individual meteorological quantities, to be sharper than those obtained from approximate climatology, and to be consistent with aspects of the spatial correlation structure of the observations.
| Author | : |
| Publisher | : |
| Total Pages | : 1652 |
| Release | : 2007 |
| Genre | : Meteorology |
| ISBN | : |
| Author | : |
| Publisher | : |
| Total Pages | : 22 |
| Release | : 2006 |
| Genre | : |
| ISBN | : |
An analogue of the linear continuous ranked probability score is introduced that applies to probabilistic forecasts of circular quantities. This scoring rule is proper and thereby discourages hedging. The circular continuous ranked probability score reduces to angular distance when the forecast is deterministic, just as the linear continuous ranked probability score generalizes the absolute error. Furthermore, the continuous ranked probability score provides a direct way of comparing deterministic forecasts, discrete forecast ensembles, and post-processed forecast ensembles that can take the form of probability density functions. The circular continuous ranked probability score is used in this study to assess predictions of 10 m wind direction for 361 cases of mesoscale, short-range ensemble forecasts over the North American Paci c Northwest. Reference probability forecasts based on the ensemble mean and its forecast error history over the period outperform probability forecasts constructed directly from the ensemble sample statistics. These results suggest that short-term forecast uncertainty is not yet well predicted at mesoscale resolutions near the surface, despite the inclusion of multi-scheme physics diversity and surface boundary parameter perturbations in the mesoscale ensemble design.
| Author | : Amanda Katherine Young |
| Publisher | : |
| Total Pages | : 200 |
| Release | : 2014 |
| Genre | : Electronic books |
| ISBN | : |
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.
| Author | : |
| Publisher | : |
| Total Pages | : 10 |
| Release | : 2009 |
| Genre | : |
| ISBN | : |
The long-term goal of this project is to develop robust global and mesoscale ensemble analysis and forecast systems that are able to provide probabilistic forecast guidance in an operational environment. Although current guidance relies primarily on single deterministic forecasts from numerical weather prediction models, recent advances in probabilistic prediction, or ensemble forecasting, have made this technique a very powerful tool for providing more complete input on environmental conditions, including a measure of the forecast confidence. Probabilistic prediction of Navy relevant high-impact weather will significantly benefit sea strike and sea shield functions if there are clearly identified user-relevant norms. Ensembles also allow for the covariance between relevant weather variables to be taken into account, for example, indicating that if the wind speed is high, then visibility will also be high.
| Author | : Douglas J. Parker |
| Publisher | : John Wiley & Sons |
| Total Pages | : 490 |
| Release | : 2017-04-24 |
| Genre | : Science |
| ISBN | : 1118391306 |
Meteorology of tropical West Africa: the Forecasters’ Handbook presents the science and practice of weather forecasting for an important region of the tropics. Connecting basic theory with forecasting practice, the book provides a unique training volume for operational weather forecasters, and is also suitable for students of tropical meteorology. The West African region contains a number of archetypal climatic zones, meaning that the science of its weather and climate applies to many other tropical regions. West Africa also exhibits some of the world’s most remarkable weather systems, making it an inspiring region for students to investigate. The weather of West Africa affects human livelihoods on a daily basis, and can contribute to hardship, poverty and mortality. Therefore, the ability to understand and predict the weather has the potential to deliver significant benefits to both society and economies. The book includes comprehensive background material alongside documentation of weather forecasting methods. Many examples taken from observations of West African weather systems are included and online case-studies are referenced widely.
| Author | : Robert Sharman |
| Publisher | : Springer |
| Total Pages | : 529 |
| Release | : 2016-06-27 |
| Genre | : Technology & Engineering |
| ISBN | : 331923630X |
Anyone who has experienced turbulence in flight knows that it is usually not pleasant, and may wonder why this is so difficult to avoid. The book includes papers by various aviation turbulence researchers and provides background into the nature and causes of atmospheric turbulence that affect aircraft motion, and contains surveys of the latest techniques for remote and in situ sensing and forecasting of the turbulence phenomenon. It provides updates on the state-of-the-art research since earlier studies in the 1960s on clear-air turbulence, explains recent new understanding into turbulence generation by thunderstorms, and summarizes future challenges in turbulence prediction and avoidance.
