An Evaluation of Analog-Based Postprocessing Methods across Several Variables and Forecast Models
| Autor: | Badrinath Nagarajan, Luca Delle Monache, Thomas N. Nipen, Jason C. Knievel, Keith R. Searight, Daran L. Rife, Joshua P. Hacker |
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| Rok vydání: | 2015 |
| Předmět: | |
| Zdroj: | Weather and Forecasting. 30:1623-1643 |
| ISSN: | 1520-0434 0882-8156 |
| DOI: | 10.1175/waf-d-14-00081.1 |
| Popis: | Recently, two analog-based postprocessing methods were demonstrated to reduce the systematic and random errors from Weather Research and Forecasting (WRF) Model predictions of 10-m wind speed over the central United States. To test robustness and generality, and to gain a deeper understanding of postprocessing forecasts with analogs, this paper expands upon that work by applying both analog methods to surface stations evenly distributed across the conterminous United States over a 1-yr period. The Global Forecast System (GFS), North American Mesoscale Forecast System (NAM), and Rapid Update Cycle (RUC) forecasts for screen-height wind, temperature, and humidity are postprocessed with the two analog-based methods and with two time series–based methods—a running mean bias correction and an algorithm inspired by the Kalman filter. Forecasts are evaluated according to a range of metrics, including random and systematic error components; correlation; and by conditioning the error distributions on lead time, location, error magnitude, and day-to-day error variability.Results show that the analog methods are generally more effective than time series–based methods at reducing the random error component, leading to an overall reduction in root-mean-square error. Details among the methods differ and are elucidated upon in this study. The relative levels of random and systematic error in the raw forecasts determine, to a large extent, the effectiveness of each postprocessing method in reducing forecast errors. When the errors are dominated by random errors (e.g., where thunderstorms are common), the analog-based methods far outperform the time series–based methods. When the errors are strictly systematic (i.e., a bias), the analog methods lose their advantage over the time series methods. It is shown that slowly evolving systematic errors rarely dominate, so reducing the random error component is most effective at reducing the error magnitude. The results are shown to be valid for all seasons. The analog methods show similar performance to the operational model output statistics (MOS) while showing greater reduction of random errors at certain lead times. |
| Databáze: | OpenAIRE |
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