Neural network techniques are used to seek or model the relationships between a set of predictors and predictands, and used to improve precipitation and 2-meter temperature forecasts made by the NOAA NCEP Climate Forecast System Version 2 (CFSv2) for several weeks. Benefitting from advances in machine learning techniques in recent years, more flexible and capable machine learning algorithms and availability of big datasets enable us not only to explore nonlinear features or relationships within a given large dataset, but also to extract more sophisticated pattern relationships and co-variabilities hidden within the multi-dimensional predictors and predictands. Then these more sophisticated relationships and high-level statistical information are used to correct the model Week 1-6 precipitation and 2-meter temperature forecasts.

    References:

• Using Artificial Neural Networks to Improve CFS Week 3-4 Precipitation and 2 Meter Air Temperature Forecasts

• Improving CFS Precipitation and 2m Temperature Anomaly Outlooks from Week-1 to Week-6 with Machine Learning