Operational statistical postprocessing of temperature ensemble forecasts with station‐specific predictors,Meteorological Applications

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Operational statistical postprocessing of temperature ensemble forecasts with station‐specific predictors
Meteorological Applications ( IF 2.3 ) Pub Date : 2020-12-16 , DOI: 10.1002/met.1971
Kaisa Ylinen ₁ , Olle Räty ₁ , Marko Laine ₁

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A proper account for forecast uncertainty is crucial in operational weather services and weather‐related decision‐making. Ensemble forecasts provide such information. However, they may be biased and tend to be under‐dispersive. Therefore, ensemble forecasts need to be post‐processed before using them in operational weather products. The present study post‐processes the European Centre for Medium‐Range Weather Forecasts (ECMWF) ensemble prediction system temperature forecasts over Europe with lead times up to 240 hr using the statistical calibration method that is currently implemented in operational workflow at the Finnish Meteorological Institute (FMI). The calibration coefficients are estimated simultaneously for all stations using a 30 day rolling training period. Station‐specific characteristic are accounted for by using elevation, latitude and land–sea mask as additional predictors in the calibration. On average the calibration improved the ensemble spread over Europe, although the improvements varied between different verification months. In March, the calibration improved ensemble forecasts the most, while in January the performance depended strongly on location. A comparison between three versions with different sets of station‐specific predictors in calibration showed that elevation was the most important predictor, while latitude and land–sea mask improved the forecasts mostly with shorter lead times. The calibration for the Finnish stations was also tested using three different size training domains in order to find the optimal training area. The results showed that smaller training domains had a significant effect on calibration performance only at lead times up to a few days. With longer lead times, the calibrated forecasts were better when all available stations were included.

中文翻译：

使用站特定的预测器对温度集合预报进行操作统计后处理

适当地预测预报不确定性对运营天气服务和与天气相关的决策至关重要。合奏预报可提供此类信息。但是，它们可能有偏差，并且往往分散性不足。因此，在将预报用于业务天气产品之前，需要对其进行后处理。本研究使用目前在芬兰气象研究所的工作流程中实施的统计校准方法对欧洲中距离天气预报中心（ECMWF）整体预报系统进行后处理，交货时间长达240小时。 FMI）。使用30天的滚动训练时间段同时估算所有站点的校准系数。特定于站的特征通过使用高程来说明，纬度和陆地-海洋掩膜作为校准中的其他预测因子。平均而言，尽管在不同的验证月份之间，改进有所不同，但平均而言，校准改善了整个欧洲的合奏效果。3月，校准对集合预报的改进最大，而1月，性能很大程度上取决于位置。在校准中使用了不同的站点特定预测器集的三个版本之间的比较显示，海拔是最重要的预测器，而纬度和陆地-海洋掩蔽改善了预测，而交货时间更短。为了找到最佳的训练区域，还使用三个不同大小的训练域对芬兰站的校准进行了测试。结果表明，较小的训练域仅在交货期长达几天时才对校准性能产生重大影响。由于交货时间较长，因此将所有可用的电台都包括在内时，校准后的预测会更好。

更新日期：2020-12-17

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