GLOBAL REAL-DATA FORECASTS WITH THE NCAR TWO-LAYER GENERAL CIRCULATION MODEL

Abstract

A number of global real-data numerical forecasts have been calculated using the two-layer NCAR (National Center for Atmospheric Research) general circulation model. The purpose of these experiments was threefold: 1) to evaluate the model's ability to predict the real atmosphere, 2) to develop a global forecasting model which will make use of the data obtained by the proposed GARP (Global Atmospheric Research Program), and 3) to help determine some of the internal, empirical constants of the model. In order to evaluate the accuracy of the predictions, several “skill scores” were calculated from the forecasted and observed variables. A by-product of this research was the testing of five different types of data-initialization schemes. Over 50, 4-day forecasts have been run, in which the initialization schemes and internal constants were varied. The results from these experiments indicate that the present two-layer model is capable of forecasting the real atmosphere with reasonable skill out to 2 days at the surface and 4 days in the middle troposphere. The best initialization scheme for this particular model, thus far, appears to be the complete balance equation. However, several of the simplified initialization techniques are very close in terms of forecasting skill.