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This weather guide includes detailed specifications for locating and instrumenting fire weather stations, taking weather observations, and overwintering the Drought Code component of the FWI System. The sensitivity of the FWI System components to weather elements is represented quantitatively. The importance of weather that is not directly observable is discussed in the context of fuel moisture and fire behavior. Current developments in the observation and measurement of fire weather and the forecasting of fire danger are discussed, along with the implications for the reporting of fire weather of increasingly automated fire management information systems.
The Canadian Forest Fire Behaviour Prediction (FBP) System provides a systematic method of assessing fire behaviour. The FBP System has 14 primary inputs that can be divided into 5 general categories: fuels, weather, topography, foliar moisture content, and type and duration of prediction. In the FBP System these inputs are used to mathematically develop 4 primary and 11 secondary outputs. Primary outputs are generally based on a fire intensity equation, and secondary outputs are calculated using a simple elliptical fire growth model. This publication provides diagrams, examples, and exercises that explain the FBP System in a user-oriented manner. This guideline delineates the interpretation of the FBP System's inputs and outputs and details how the predictions are derived.
Fourth edition of tables for calculating the six standardcomponents of the Canadian Forest Fire Weather Index System. Thefirst three components are fuel moisture codes that follow dailychanges in the moisture contents of three classes of forestsfuel; the final three are fire behaviour indexes that representrate of spread, amount of available fuel, and fire intensity. The system provides a uniform method of rating fire danger acrossCanada.
The Canadian Forest Fire Behavior Prediction (FBP) system is a systematic method for assessing wildland fire behaviour potential. Presented in tabular format, this guide provides a simplified version of the system and is designed to assist field staff in making approximations of FBP System outputs.
The Canadian Forest Fire Behavior Prediction (FBP) System is a subsystem of the larger Canadian Forest Fire Danger Rating System, which also includes the Canadian Forest Fire Weather Index (FWI) System. The FBP system provides quantitative estimates of head fire spread rate, fuel consumption, fire intensity and fire description and gives estimates of fire area, perimeter, perimeter growth rate and flank and back fire behaviour. This report describes the structure and content of the system and its use with forest fire characteristics.
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
Wilks provides a historical background, list of publications, and description of activities for most of the major science initiatives undertaken at the federal level. He surveys a wide range of government documents and monographic and serial science collections used by both faculty and students.
Seminar paper from the year 2014 in the subject Forestry / Forestry Economics, grade: 1,3, University of Toronto, language: English, abstract: This paper describes and compares conceptually the Fire Weather Index (FWI) system of Canada and the National Fire Danger Rating System (NFDRS) of the USA. The relatively simple FWI system rates fire danger for all Canada and is based on empirical field-data derived from a single fuel type. The laboratory-based NFDRS, in contrast, allows more specification for distinct fire danger areas and models fuel moisture more abstract and in different classifications. Relative strengths and weaknesses with regard to the vegetative conditions in the particular country are discussed. The use of empirical data and the good and simple representation of soil moisture are the major strengths of the FWI system. The NFDRS wins through its possibility to model specifically a distinct fire danger area and through the consideration of live fuel moisture content. The conclusion of this paper is that both systems can benefit from each other. A combination of the simplicity of the FWI and specialization on a distinct area through the site descriptors similar to the ones of the NFDRS is proposed.