Local temperature forecasts based on statistical post-processing of numerical weather prediction data,Meteorological Applications

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Local temperature forecasts based on statistical post-processing of numerical weather prediction data
Meteorological Applications ( IF 2.3 ) Pub Date : 2021-07-06 , DOI: 10.1002/met.2006
Emy Alerskans _{1,

2} , Eigil Kaas _{1,

3}

Affiliation

Six adaptive, short-term post-processing methods for correcting systematic errors in numerical weather prediction (NWP) forecasts of near-surface air temperatures using local meteorological observations are assessed and compared. The methods tested are based on the simple moving average and the more advanced Kalman filter. Forecasts from the rather coarse-resolution global NWP model Global Forecast System (GFS) and the regional high-resolution NWP model HARMONIE are post-processed, and the results are evaluated for 100 private weather stations in Denmark. The performance of the post-processing methods differs depending on the NWP model. Overall, the combined moving average and a so-called lead time Kalman filter performs the best. The moving average was shown to be superior to a diurnal bias correction Kalman filter at removing the longer-term systematic errors for HARMONIE forecast data and comparable for GFS forecast data. Subsequent application of the lead time Kalman filter corrects for the short-term errors using the real-time forecast error. The post-processing method is adaptive and there is no need for a long record of observations or a historical archive of forecasts.

中文翻译：

基于数值天气预报数据统计后处理的局地温度预报

评估和比较了六种自适应短期后处理方法，用于校正使用当地气象观测的近地表气温数值天气预报 (NWP) 预报中的系统误差。测试的方法基于简单的移动平均和更高级的卡尔曼滤波器。对来自相当粗分辨率的全球 NWP 模型 Global Forecast System (GFS) 和区域高分辨率 NWP 模型 HARMONIE 的预测进行了后处理，并对丹麦 100 个私人气象站的结果进行了评估。后处理方法的性能因 NWP 模型而异。总的来说，组合移动平均线和所谓的提前期卡尔曼滤波器表现最好。移动平均在消除 HARMONIE 预测数据的长期系统误差方面优于日偏差校正卡尔曼滤波器，并且与 GFS 预测数据具有可比性。前置时间卡尔曼滤波器的后续应用使用实时预测误差校正短期误差。后处理方法是自适应的，不需要长时间的观测记录或历史预测档案。

更新日期：2021-07-07

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