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About CFSv2 model and data

The second version of the NCEP (National Centers for Climate Predictions) Climate Forecast System (CFSv2) was made operational at NCEP in March 2011. This version has upgrades to nearly all aspects of the data assimilation and forecast model components of the system. A coupled reanalysis was made over a 32-yr period (1979-2010), which provided the initial conditions to carry out a comprehensive reforecast over 29 years (1982-2010). This was done to obtain consistent and stable calibrations, as well as skill estimates for the operational subseasonal and seasonal predictions at NCEP with CFSv2.

The operational implementation of the full system ensures a continuity of the climate record and provides a valuable up-to-date dataset to study many aspects of predictability on the seasonal and subseasonal scales. Evaluation of the reforecasts show that the CFSv2 increases the length of skilful MJO forecasts from 6 to 17 days (dramatically improving subseasonal forecasts), nearly doubles the skill of seasonal forecasts of 2-m temperatures over the United States, and significantly improves global SST forecasts over its predecessor. The CFSv2 not only provides greatly improved guidance at these time scales but also creates many more products for subseasonal and seasonal forecasting with an extensive set of retrospective forecasts for users to calibrate their forecast products. These retrospective and real-time operational forecasts are used by a wide community of users in their decision making processes in areas such as water management for rivers and agriculture, transportation, energy use by utilities, wind and other sustainable energy, and seasonal prediction of the tropical cyclone season.

Data applied

Real time monthly and daily data download from

Climatology 1981-2010:

Downscaling for New Zealand

To provide high-resolution seasonal forecasts for New Zealand, a generalised empirical statistical downscaling scheme (ESD) has been developed and applied. The scheme resolves monthly climate variables across a five kilometre grid over the whole country. Precipitation, PET and temperature are the key focus variables as they can be used in a water balance model to monitor regional droughts. The downscaling model was established based on large-scale NCAR/NCEP reanalysis data (predictors) and the virtual climate stations network (VCSN) data from NIWA (about 5k spatial resolution, predictand). A model is applied to downscale real-time CFSv2 seasonal forecasts. Cross-validation and optimizing predictor selection methods are applied.