Worldwide Cloud Forecasts with Neural Networks
Technical rept. 26 Aug 94-30 Apr 97
PACIFIC-SIERRA RESEARCH CORP SANTA MONICA CA
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Most approaches to weather and cloud forecasting entail the use of a large numerical weather prediction code. This project investigated an alternative approach to cloud forecasting based upon using a neural network NN to analyze and combine the basic meteorological elements of persistence, advection and evolution. The Worldwide Cloud Prediction Model WCPM is based upon a pixel by pixel implementation of a NN. The temporal evolution and advection are estimated from past satellite and numerical weather prediction data. Persistence of cloud properties at a pixel is estimated from past data. The forecast is based upon this pixel level analysis and is almost independent of changes in neighboring pixels. Over the limited data available, the NN performed somewhat better in tropical regions than the current HRCP model. The approach demonstrated the ability to predict both the advection and evolution of clouds. Performance was best in regions of significant cloud cover, regions of scattered clouds were smeared. RMS prediction errors of about 20 were typical for the WCPM as compared to rms errors of about 30 for tropical HRCP predictions. An alternative, fully object oriented approach to the NN is outlined to improve the performance and forecast sharpness in regions of scattered clouds.
- Numerical Mathematics