Synopses & Reviews
Numerical weather prediction is receiving increased attention as weather forecasters aim to improve the numerical models used to forecast the weather. This is a textbook on global spectral modeling, which is an important component for global weather forecasts at numerous operational centers. This book covers all areas of model development including numerical analysis, treatment of clouds, mountains, radiation, precipitation processes, and the surface layers over land and the ocean. The objectives of this book are to provide a systematic and sequential background for students, researchers, and operational weather forecasters in order to develop comprehensive weather forecast models. This is designed for a one semester introductory graduate level course on weather prediction methodologies. As a prerequisite it requires a basic background in meteorology, applied mathematics, and numerical analysis.
Review
James Russell Carr in Mathematical Geology, Vol. 31, No. 8, 1999 on the book's first edition: In summary, the mathematical treatment is quite intense and demands patience of readers, at least in the case of this one. But if at all intrigued by how sophisticated weather forecasting has become (certainly strom forecasting) then a reader will find this book not only interesting, but thorough enough to enable model development if that is a goal. Problems are presneted at the end of each chapter, so this book can be used as a texct in the class room. Reserachers involved in the modeling of turbulence, ocean systems and tectonic systems may also value the presentation of this book.
Synopsis
This introductory book on numerical weather prediction focuses on the spectral transform method, which is an important component for global weather forecasts at numerous operational centers. Therefore, it is an indispensable guide to the methods being used by nearly all major weather forecast centers in the United States, England, Japan, India, France, and Australia. The objectives of this book are to provide a systematic and sequential background for students, researchers, and operational weather forecasters in order to develop comprehensive weather forecast models. The chapter exercises allow it to be used as a graduate textbook for courses in meteorology as well.
About the Author
T.N. Krishnamurti is professor of meteorology at Florida State University. He obtained his PhD in 1959 at the University of Chicago. His research interests are in the following areas: high resolution hurricane forecast (tracks, landfall, and intensity), monsoon forecasts on short, medium range, and monthly time scale and studies of interseasonal and interannual variability of the tropical atmosphere. As a participant in the meteorology team in tropical field projects, he has been responsible for the acquisition and analysis of meteorological data, which extends over most of the tropical atmosphere over several years and is now being assembled and analyzed. These data are unique; it is unlikely that a meteorological data record will be available for decades. Phenomenological interests include hurricanes, monsoons, jet streams, and the meteorology of arid zones. H.S. Bedi is affiliated with Florida State University. V.M. Hardiker is a research associate at Florida State University. L. Ramaswamy is a graduate research assistant in the Department of Meteorology at Florida State University.
Table of Contents
Introduction An Introduction to Finite Differencing 2.1 Introduction 2.2 Application of Taylor's Series to Finite Differencing 2.3 Forward and Backward Differencing 2.4 Centered Finite Differencing 2.5 Fourth-Order Accurate Formulas 2.6 Second-Order Accurate Laplacian 2.7 Fourth-Order Accurate Laplacian 2.8 Elliptical Partial Differential Equations in Meteorology 2.9 Direct Method 2.10 Relaxation Method 2.11 Sequential Relaxation Versus Simultaneous Relaxation 2.12 Barotropic Vorticity Equation 2.13 The 5-Point Jacobian 2.14 Arakawa Jacobian 2.15 Exercises 3 Time-Differencing Schemes 3.1 Introduction 3.2 Amplification Factor 3.3 Stability 3.4 Shallow-Water Model 4 What Is a Spectral Model? 4.1 Introduction 4.2 The Galerkin Method 4.3 A Meteorological Application 4.4 Exercises 5 Low-Order Spectral Model 5.1 Introduction 5.2 Maximum Simplification 5.3 Conservation of Mean-Square Vorticity and Mean Kinetic Energy 5.4 Energy Transformations 5.5 Mapping the Solution 5.6 An Example of Chaos 5.7 Exercises 6 Mathematical Aspects of Spectral Models 6.1 Introduction 6.2 Legendre Equation and Associated Legendre Equation 6.3 Laplace's Equation 6.4 Orthogonality Properties 6.5 Recurrence Relations 6.6 Gaussian Quadrature 6.7 Spectral Representation of Physical Fields 6.8 Barotropic Spectral Model on a Sphere 6.9 Shallow-Water Spectral Model 6.10 Semi-implicit Shallow-Water Spectral Model 6.11 Inclusion of Bottom Topography 6.12 Exercises 7 Multilevel Global Spectral Model 7.1 Introduction 7.2 Truncation in a Spectral Model 7.3 Aliasing 7.4 Transform Method 7.5 The x-y-s Coordinate System 7.6 A Closed System of Equations in s Coordinates on a Sphere 7.7 Spectral Form of the Primitive Equations 7.8 Examples 8 Physical Processes 8.1 Introduction 8.2 The Planetary Boundary Layer 8.3 Cumulus Parameterization 8.4 Large-Scale Condensation 8.5 Parameterization of Radiative Processes 9 Initialization Procedures 9.1 Introduction 9.2 Normal Mode Initialization 9.3 Physical Initialization 9.4 Initialization of the Earth's Radiation Budget 10 Spectral Energetics 10.1 Introduction 10.2 Energy Equations on a Sphere 10.3 Energy Equations in Wavenumber Domain 10.4 Energy Equations in Two-Dimensional Wavenumber Domain 11 Limited Area Spectral Model 11.1 Introduction 11.2 Map Projection 11.3 Model Equations 11.4 Orography and Lateral Boundary Relaxation 11.5 Spectral Representation and Lateral Boundary Conditions 11.6 Spectral Truncation 11.7 Model Physics and Vertical Structure 11.8 Regional Model Forecast Procedure 12 Ensemble Forecasting 12.1 Introduction 12.2 Monte Carlo Method 12.3 National Center for Environmental Prediction Method 12.4 Florida State University Method 12.5 European Center for Medium Range Forecasts Method 12.6 Superensemble Methodology and Results 13 Adaptive Observational Strategies 13.1 Introduction 13.2 Techniques for Targeted Observations Appendix A Appendix B References Index