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Standardized Anomaly Forest-Based Methods for Wind Speed Ensemble Postprocessing
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AI
DOI：10.1175/JAMC-D-24-0135.1.png)
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Abstract
En 中文
Data preprocessing based on standardized anomaly (SA) has been shown to enhance spatial coherence and reduce computational costs by enabling statistical methods to postprocess forecasts from multiple stations simultaneously, rather than fitting the forecast system separately at each station, as is done with ensemble model output statistics. However, no studies have focused on SA combined with forest-based methods despite its distribution-free and highly flexible use for ensemble postprocessing. We propose Standardized Anomaly Quantile Regression Forest (SAQRF) for 10-m wind speed forecasts. The SAQRF employs a two-step approach involving SA preprocessing and spatial QRF modeling to capture underlying nonlinear features. QRF incorporates raw scale and static geographic predictors in a comparable setting, termed QRFGE. Similar settings are applied to the generalized random forest (GRF) named SAGRF and GRFGE. We compare four forest-based methods with competing statistical techniques, Standardized Anomaly Model Output Statistics (SAMOS) and Standardized Anomaly Boosting (SABST) approaches, using ECMWF's high-resolution and ensemble forecasts from 2019 to 2020 in Hebei, China. The results show that forest-based methods achieve an average 5% improvement compared to SABST in the stationwise continuous ranked probability skill score.
Keywords:
Climatology
Statistical techniques
Probability forecasts/models/distribution
Postprocessing
Machine
Machine learning
Journal
IF:
2.2
Papers:
90
Citations:
1.3W