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.
1. 1 Introduction - p. 1 2. 2 Governing equations - p. 2 3. 3 Some physical properties - p. 8 4. 4 Discretizing in time - p. 10 5. 5 Discretizing in space - p. 22 6. 6 (Semi-)Lagrangian approach - p. 33 7. 7 Multidimensional aspects - p. 37 8. 8 An outlook - p. 41 9. References - p. 44 10. Chapter 2. Numerical uncertainties in discretization of the shallow-water equations for weather predication models - p. 47 11. Chapter 3. Probabilistic view of numerical weather prediction and ensemble prediction - p. 81 12. Chapter 4. Predictability - p. 119 13. Chapter 5. Modeling moist dynamics on subgrid - p. 147 14. Chapter 6. Ensemble data assimilation for estimating analysis uncertainty - p. 179 15. Chapter 7. Subgrid turbulence mixing - p. 205 16. Chapter 8. Uncertainties in the surface layer physics parameterizations - p. 229 17. Chapter 9. Radiation - p. 237 18. Chapter 10. Uncertainties in the parameterization of cloud microphysics: An illustration of the problem - p. 265 19. Chapter 11. Mesoscale orographic flows - p. 297 20. Chapter 12. Numerical methods to identify model uncertainty - p. 309 21. Chapter 13. Dynamic identification and tracking of errors in numerical simulations of the atmosphere - p. 331 22. Index - p. 345
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.
1. 1 Introduction - p. 1 2. 2 Governing equations - p. 2 3. 3 Some physical properties - p. 8 4. 4 Discretizing in time - p. 10 5. 5 Discretizing in space - p. 22 6. 6 (Semi-)Lagrangian approach - p. 33 7. 7 Multidimensional aspects - p. 37 8. 8 An outlook - p. 41 9. References - p. 44 10. Chapter 2. Numerical uncertainties in discretization of the shallow-water equations for weather predication models - p. 47 11. Chapter 3. Probabilistic view of numerical weather prediction and ensemble prediction - p. 81 12. Chapter 4. Predictability - p. 119 13. Chapter 5. Modeling moist dynamics on subgrid - p. 147 14. Chapter 6. Ensemble data assimilation for estimating analysis uncertainty - p. 179 15. Chapter 7. Subgrid turbulence mixing - p. 205 16. Chapter 8. Uncertainties in the surface layer physics parameterizations - p. 229 17. Chapter 9. Radiation - p. 237 18. Chapter 10. Uncertainties in the parameterization of cloud microphysics: An illustration of the problem - p. 265 19. Chapter 11. Mesoscale orographic flows - p. 297 20. Chapter 12. Numerical methods to identify model uncertainty - p. 309 21. Chapter 13. Dynamic identification and tracking of errors in numerical simulations of the atmosphere - p. 331 22. Index - p. 345
