Skip to main content Accessibility help
×
Hostname: page-component-78c5997874-dh8gc Total loading time: 0 Render date: 2024-11-09T02:09:49.727Z Has data issue: false hasContentIssue false

5 - Probabilistic hydrometeorological forecasting

Published online by Cambridge University Press:  18 January 2010

Janos J. Bogardi
Affiliation:
Division of Water Sciences, UNESCO, Paris
Zbigniew W. Kundzewicz
Affiliation:
Research Centre of Agricultural and Forest Environment, Polish Academy of Sciences
Get access

Summary

ABSTRACT

The U.S. National Weather Service has supported the development of an integrated probabilistic hydrometeorological forecasting system. The system produces probabilistic quantitative precipitation forecasts that are used to produce probabilistic river stage forecasts; these in turn are input to optimal decision procedures for issuing flood warnings, operating waterways and barges, or controlling storage reservoirs. The system is designed based on Bayesian principles of probabilistic forecasting and rational decision making. This chapter outlines the system concept.

INTRODUCTION

Systems approach to hydrometeorological forecasting

That forecasts should be stated in probabilistic rather than categorical terms has been argued from operational (Cooke 1906) and decision-theoretic (Murphy 1991) perspectives for almost a century. Yet most operational systems produce deterministic forecasts and most research in physical and statistical sciences has been devoted to finding the “best” estimates rather than probability distributions of predictands. Undoubtedly, the leap from a deterministic frame of thought to one that not only admits our limited knowledge and information, but also quantifies uncertainty about future states of the environment, requires a vast and coordinated effort at two levels: engineering – to design probabilistic forecasting systems, and organizational – to alter the institutional mindset and modus operandi.

The U.S. National Weather Service (NWS) has embarked on making such a quantum change (Zevin 1994; Krzysztofowicz 1998). The goal is to increase the value of service to users by developing and implementing an integrated probabilistic hydrometeorological forecasting system.

Type
Chapter
Information
Publisher: Cambridge University Press
Print publication year: 2002

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

Save book to Kindle

To save this book to your Kindle, first ensure coreplatform@cambridge.org is added to your Approved Personal Document E-mail List under your Personal Document Settings on the Manage Your Content and Devices page of your Amazon account. Then enter the ‘name’ part of your Kindle email address below. Find out more about saving to your Kindle.

Note you can select to save to either the @free.kindle.com or @kindle.com variations. ‘@free.kindle.com’ emails are free but can only be saved to your device when it is connected to wi-fi. ‘@kindle.com’ emails can be delivered even when you are not connected to wi-fi, but note that service fees apply.

Find out more about the Kindle Personal Document Service.

Available formats
×

Save book to Dropbox

To save content items to your account, please confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your account. Find out more about saving content to Dropbox.

Available formats
×

Save book to Google Drive

To save content items to your account, please confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your account. Find out more about saving content to Google Drive.

Available formats
×