Comparison Of The Refractive Index Structure Constant Derived From Numerical Weather Prediction Nwp Models And Thermosonde Data

An accurate depiction of atmospheric turbulence is required for successful employment of a viable airborne laser for the Department of Defense (DOD). The ABL Special Program Office (SEC) which is tasked by the Missile Defense Agency (MDA) bas not designated any particular numerical weather model that is tasked exclusively to model optical turbulence This research compares CLEAR1, 2 X CLEAR I and thermosonde derived values of the refractive index structure constant to optical turbulence values derived from several numerical weather prediction models currently in use by the DOD. The models used were the fifth Generation Mesoscale Model (MM5), the Coupled Ocean Atmosphere Prediction System (COAMPS) and the Advanced Climate Modeling and Environmental Simulation (ACMES) program Comparisons are presented using thermosonde data collected at Vandenberg AEB California during the period l9-26 Oct 200l Universal Time Coordinated (UTC ) Results indicate that the model-derived optical turbulence and the thermosonde derived optical turbulence values are statistically different in many cases

This book has as main aim to be an introductory textbook of applied knowledge in Numerical Weather Prediction (NWP), which is a method of weather forecasting that employs: A set of equations that describe the flow of fluids translated into computer code, combined with parameterizations of other processes, applied on a specific domain and integrated in the basis of initial and domain boundary conditions. Current weather observations serve as input to the numerical computer models through a process called data assimilation to produce atmospheric properties in the future (e.g. temperature, precipitation, and a lot of other meteorological parameters). Various case studies will be also presented and analyzed through this book.

This set of notes is based on a series of lectures presented at the Department of Meteorology of the Hebrew University of Jerusalem during 1961 - 1962. The topics are selected so as to trace the development of numerical models, from the geostrophic to the nongeostrophic. Application for prediction of specific phenomena, such as the hurricane and the sea breeze, is discussed. Much emphasis is placed on discussion of integral constraints. (Author).

An Introduction to Numerical Weather Prediction Techniques is unique in the meteorological field as it presents for the first time theories and software of complex dynamical and physical processes required for numerical modeling. It was first prepared as a manual for the training of the World Meteorological Organization's programs at a similar level. This new book updates these exercises and also includes the latest data sets. This book covers important aspects of numerical weather prediction techniques required at an introductory level. These techniques, ranging from simple one-dimensional space derivative to complex numerical models, are first described in theory and for most cases supported by fully tested computational software. The text discusses the fundamental physical parameterizations needed in numerical weather models, such as cumulus convection, radiative transfers, and surface energy fluxes calculations. The book gives the user all the necessary elements to build a numerical model. An Introduction to Numerical Weather Prediction Techniques is rich in illustrations, especially tables showing outputs from each individual algorithm presented. Selected figures using actual meteorological data are also used. This book is primarily intended for senior-level undergraduates and first-year graduate students in meteorology. It is also excellent for individual scientists who wish to use the book for self-study. Scientists dealing with geophysical data analysis or predictive models will find this book filled with useful techniques and data-processing algorithms.

Up to now, past data have not been utilized in numerical weather forecasting due to the particular formulations of the problem. This paper indicates that the condition for solution, formed by a whole set of available meteorological data, is not compatible with the general system of weather equations. Thus, it is necessary to study the best way of formulating the problem using the greatest possible amount of available data in the condition for solution in order to obtain the optimum way of forecasting. Two possibilities are given. First, it is shown that under certain conditions, weather forecasting may be formulated as an evolutionary problem, in which past data are utilized. Secondly, past data could be partly utilized in an initial-value problem in which the system of weather equations, with certain meteorological variables eliminated, contains higher order time derivatives. The significance of these formulations is discussed.