Uncertainties in Numerical Weather Prediction

Haraldur Olafsson, Jian-Wen Bao

• Publisher: Elsevier
• Year: 2020
• ISBN:
  9780128157107
• Topic: Physical Sciences
• Subtopic: Meteorology & Climatology

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

Table of contents

• Cover image
• Title page
• Table of Contents
• Copyright
• Contributors
• Preface
• Chapter 1: Dynamical cores for NWP: An uncertain landscape*
• Chapter 2: Numerical uncertainties in discretization of the shallow-water equations for weather predication models
• Chapter 3: Probabilistic view of numerical weather prediction and ensemble prediction
• Chapter 4: Predictability
• Chapter 5: Modeling moist dynamics on subgrid
• Chapter 6: Ensemble data assimilation for estimating analysis uncertainty
• Chapter 7: Subgrid turbulence mixing
• Chapter 8: Uncertainties in the surface layer physics parameterizations
• Chapter 9: Radiation
• Chapter 10: Uncertainties in the parameterization of cloud microphysics: An illustration of the problem
• Chapter 11: Mesoscale orographic flows
• Chapter 12: Numerical methods to identify model uncertainty
• Chapter 13: Dynamic identification and tracking of errors in numerical simulations of the atmosphere
• Index