Weather generators provide synthetic daily values of meteorological variables, statistically equivalent to observed local series of those variables.
IPCC suggests the use of weather generators. They are not forecasts, but tools to assess the impacts of climate variability.
Weather generators are extensively used in Climate Change impact assessments. Generation results comprise many meteorological series, equivalent to local historical data, but considering Climate Change model outputs.
LARS-WG does not assume a priori statistical distribution for the meteorological variables. This is in principle a better approach compared with other generators. For instance, WGEN and others consider Markov series and assumptions of specific probabilistic distributions for temperature and precipitation.
The LARS-WG skills to simulate climate variability and extreme events has been tested. Differences between observed and generated yearly means were within the 95% confidence interval. However, the weather generator did not to accurately reproduce daily means.
Utset and Del Rio (2011) “perturbed” a weather generator, according to climate model outputs. The wanted to assess if a current large irrigation investment could meet the crop water demand in future months of July. They consider evapotranspiration increases due to global warming. They took into account CO2 “fertilization effect” as well.
However, a simple approach might be to randomly select realizations which just meet some conditions.
Constraining the weather generator outputs is a better solution than adjusting the generator input parameters, in order to produce reliable weather data for climate impact assessments.
Utset et al. (2006) generated a series of thirty “dry” and “wet” years, randomly selected from a realization of 500 years using LARS-WG. All the 500 realizations were in principle statistically equivalent to the local historical series considered.
Dry years were selected as those when precipitation was lower than the 10th percentile of the historical series, whereas severe rainfall years were those when year total record was higher than the 90th percentile.
The final goal was to assess rainfall variability effects on deficit irrigation efficiency.
Combining this meteorological information with a simulation model will provide statistical estimates of the climate impact of the conditions considered.