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Gridded probabilistic weather forecasts with an Analog Ensemble

pubblicazioni - Poster

Gridded probabilistic weather forecasts with an Analog Ensemble

Questo lavoro presenta un’estensione della tecnica analog ensemble (AnEn) utilizzata per generare delle stime probabilistiche della velocità del vento di 10m in una rete. Per ognuna di stima un tempo di risposta e una posizione, AnEn è generata usando segnalazioni che corrispondono a predizioni deterministiche passate che sono più simili alla stima attuale.

This work presents an extension of the analog ensemble (AnEn) technique to generate probabilistic forecasts of 10-m wind speed over a grid. For each forecast lead time and location, the ensemble of analogs is generated using the observations corresponding to the past deterministic predictions that are more similar to the current forecast. An effective integration of different renewable energy sources into the electric grid usually requires predictions over large geographic areas. Here, the AnEn is extended over a two-dimensional grid, where each grid point is treated independently, using meteorological analysis instead of observations.

The AnEn forecasts are generated using data from the European Centre for Medium-Range Weather Forecasts (ECMWF) deterministic and analysis model. Data from the calibrated ECMWF Ensemble Prediction System (EPS CAL) are used for comparison. Given that the AnEn predictions are generated independently at any locations and lead time, the resulting spatial and temporal correlation may be degraded by noise. A reordering technique (Schaake Shuffle, SS) is then applied to the ensemble members to recover the spatio-temporal correlation.

The AnEn outperforms EPS CAL for the first two days ahead prediction using 1/6 of its computational resources. During the third forecast day the AnEn is competitive with EPS CAL, while the latter is more skillful from 72 to 144 hours ahead.

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