Gridded probabilistic weather forecasts with an analog ensemble

This study extends the analog ensemble (AnEn) technique to generate probabilistic forecasts of 10 m wind speed over a grid. The AnEn has been widely used to generate short-term probabilistic predictions of meteorological variables, air quality, wind and solar power, and to effectively downscale reanalysis fields. It is based on a historical dataset including observations paired with corresponding deterministic predictions. For each forecast lead time and location, the AnEn is generated using the observations corresponding to the past deterministic predictions that are more similar to the current forecast. So far, the AnEn has been used to generate predictions at specific locations with available observations. Here, it is extended over a two-dimensional grid, where each grid point is treated independently, using meteorological analysis instead of observations. The forecast range is extended up to 6 days ahead to evaluate the performance of the AnEn in both the short and medium range. The proposed approach allows to extend the AnEn to applications requiring gridded probabilistic products that are often generated with dynamical ensemble models. The AnEn forecasts are generated using the European Centre for Medium-Range Weather Forecasts (ECMWF) deterministic and analysis model. Also, the ECMWF Ensemble Prediction System (EPS) is used for comparison. Given that the AnEn predictions are generated independently at any location and lead time, the resulting spatial and temporal correlation may be degraded by noise. A reordering technique called Schaake Shuffle (SS) is then applied to the ensemble members to recover the spatio-temporal correlation. The AnEn outperforms a calibrated version of EPS for the first two days of the prediction. During the third forecast day, the AnEn remains competitive with the calibrated EPS, while the latter is more skilful from 72 to 144 h ahead. The AnEn uses about one sixth of the computational resources necessary to generate the real-time EPS prediction.