An analog-based weather generator using re-forecast data

Accurately estimating the risks of weather-related impacts requires comprehensively simulating weather conditions that could occur but have not occurred in the historical record. This is the aim of weather generators. Analog-based weather generators exploit the fact that the large-scale atmospheric circulation constrains regional weather and generate multivariate spatiotemporal meteorological fields by resampling historical data. During the resampling, constraints are employed to ensure that successive samples have consistent circulation patterns. Compared to other types of weather generators, resampling-based methods have the advantage that dependencies between variables and between locations are automatically correctly captured. However, the generated time series are limited to observed ranges, and even “close” analogs in the historical record are relatively far away from each other.

We overcome these limitations by constructing a (daily) analog weather generator using ECMWF extended ensemble forecast hindcast (re-forecast) data, which provides a much larger sample size and the ability to sample values larger than the observed records. We choose this dataset because it has high spatial resolution and provides a large set of states from a relatively constant climate, while model biases remain limited because the forecasts are initialized from reanalysis data. With the ensemble hindcasts, we can also assess how “close” analogs are compared to typical ensemble spreads. We test our methodology by applying it to simulate weather over a European domain. Analogs are defined in terms of geopotential height at 500hPa and computed over an extended region including parts of the North Atlantic. With our approach, we can find better analogs compared to a baseline using only ERA5 data. We evaluate key properties of the simulated time series, such as their annual cycle, extremes, and lengths of wet and dry spells. The weather generator can be widely applied to estimate potential climate impacts, for instance with impact models. It is especially useful in cases where an accurate representation of dependencies between variables or across space is important for the impacts, which is the case for a number of different types of compound events.