The numerical test of precipitation prediction is made of the projection pursuit regression (PPR) based on SMART are introduced. The numerical test of precipitation prediction is made by using projection pursuit multi dimension regression and stepwise regression(SR). The results show that the precision of PPR model are much better than those of SR model in both fitting and prediction.
In this paper, we analyze the circulation characteristics, discuss the precipitation forecast factors in 2003 flood season and develop a precipitation forecast model based on the subtropical high characteristics quantity.
Based on these conclusions and applied methods of singular spectrum analysis and time series analysis,statistical models of rainfall forecast were designed,and it were applied to do forecast experiments for monthly rainfall.
Total precipitation values estimated from maximum liquid water content, maximum vertical velocity, cloud top height, and temperature excess are also used to provide an equation for the total precipitation prediction.
In this paper a more quantitative method of precipitation prediction, based upon the surface energy budget, is discussed as well.
A diagnostic method for high-resolution precipitation prediction using dynamically adapted vertical velocities
Evaluation of precipitation prediction skill of IMD operational NWP system over Indian monsoon region
A simple ranking formulation among simulations does suggest that use of the new droplet parameterization improves the precipitation prediction.
A short-range quantitative precipitation forecast algorithm using back-propagation neural network approach
In order to demonstrate and improve the precipitation forecast skill, several numerical experiments were designed using the 14-level Florida State University Global Spectral Model (FSUGSM) at a resolution of T106.
The quantitative precipitation "forecast" fields are compared with available rain data.
Some characteristics of Limited-Area Model-Precipitation forecast of Indian monsoon and evaluation of associated flow features
The precipitation forecast also is improved when initialized with the analyses containing radiosonde data.
Application of ATOVS microwave radiance assimilation to rainfall prediction in Summer 2004
Experiments are performed in this paper to understand the influence of satellite radiance data on the initial field of a numerical prediction system and rainfall prediction.
The heavy rainfall experiments reveal that the application of AMSU-A (B) microwave radiance data can improve the rainfall prediction significantly.
The success of an ENSO-based statistical rainfall prediction scheme and the influence of ENSO on Australia are shown to vary in association with a coherent, inter-decadal oscillation in surface temperature over the Pacific Ocean.
All India summer monsoon rainfall prediction using an artificial neural network