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The UKCP09 projections provide changes in mean monthly climate for different periods in the future. Some users want information on future time series of weather at the daily or even hourly scale. One way of creating this is by applying the monthly changes to an observed time series of daily or hourly weather to create a perturbed daily weather series. However, daily weather data may not always be available, or records may be short. Another way of creating future weather series is to use the Weather Generator, which simulates plausible daily or hourly weather time series. It uses a random number generator to produce time series that have the same statistical properties as 'real' data. This random element allows different (but statistically equivalent) series to be generated.

The UKCP09 Weather Generator (WG) in particular is designed to work in harmony with the UKCO9 probabilistic projections. It is based on a well established and tested methodology, and outputs can be used to generate derived climate indices.

Well established and tested methodology

The WG has benefited from previous work including  BETWIXT (Built Environment: Weather scenarios for investigation of impacts and extremes) and EARWIG (Environment Agency Rainfall and Weather Impacts Generator).

The WG methodology is based on an improved understanding of how climate influences weather and has been developed to work in partnership with the UKCP09 probabilistic projections.

See the UKCP09 Reports & guidance section for results of extensive testing and verification.

Outputs can be used to generate derived climate indices

The daily time series of weather produced using the WG can also be used to develop a number of derived variables. Table 1 in the UKCP09 Weather Generator report, found in the Reports & guidance section, provides examples of derived indices based on temperature and rainfall time series (e.g. number of consecutive dry days). Users are also able to derive others from the generated time series using the associated Threshold Detector.

Although there are many important assumptions behind the WG, the time series produced using a WG are more suited to the calculation of derived climate indices than the direct output from climate models because they are tuned to reproduce well the current baseline climate.