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Calibrating 2 m Temperature Forecast for the Regional Ensemble Prediction System at NMC
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摘要: 采用非齐次高斯回归 (NGR) 技术对国家气象中心区域集合预报系统的2 m温度预报结果开展了一阶偏差和二阶离散度的校准研究。对预报结果比较详尽的检验分析表明：校准后的2 m温度预报可靠性和预报技巧均显著提高，表现为校准后集合预报成员的均方根误差与离散度更为接近；原Talagrand直方图中的“L”形分布现象得到有效改善；Brier评分、最小连续分级概率评分 (CRPS) 明显减小，相对作用特征 (ROC) 面积增大，说明校准后的2 m温度预报表现出更好的预报技能。此外，NGR技术与自适应误差订正技术的对比试验表明，NGR在消除集合平均偏差和提高集合离散度两个方面均有优势。
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关键词:
- 集合预报;
- 偏差校准;
- 非齐次高斯回归 (NGR);
- 检验评分
Abstract: It's known that ensemble forecasts provide a flow-dependent sample of the probability distribution of possible future atmospheric states instead of the single and deterministic prediction. Ideally, the probability of any event could be skillfully estimated directly from the relative event frequency in the ensemble. Unfortunately, although the quality of ensemble prediction systems (EPS) has been improved greatly, the direct output of EPS generally is subject to the systematic deficiencies, especially for surface variables. They are under-dispersive and lack of reliability. Therefore, statistical post-processing methods have been developed to improve direct model output. The nonhomogeneous Gaussian regression (NGR) is used to calibrate 2 m temperature forecast of the regional EPS at NMC/CMA. The NGR is the statistical correction method with the first and the second moment (mean bias and dispersion) for Gaussian-distributed continuous variable. This method is based on the multiple linear regression technique and provides a predictive probability density function (PDF) in terms of the normal distribution. The method of minimum continuous ranked probability score (CRPS) estimation is used to fit the regression coefficients of PDF. It can be found that NGR method can greatly improve 2 m temperature forecast compared with the raw ensemble output, and the improvement is as follows: The mean bias is reduced and the spread of ensemble members is increased reasonably; the L-shaped Talagrand diagram of the direct ensemble output has been improved and the calibration reduces the number of outliers, especially in the 9th bin; the probabilistic scores (the brier score, continuous ranked probability score, area under relative operating characteristic curves) all show the significant forecast skill improvement in the calibrated forecasts.In addition, the sensitive study is performed to investigate the effect of the training length, and the results show that the training length plays a minor role, at least for the chosen verification period. Finally, the comparison by using the time-decaying average bias correction method and NGR is performed, showing that NGR not only has advantages in reducing ensemble mean bias and increasing ensemble spread, but improves the forecast skill in terms of probabilistic scores. -
图 1 2008年7月20日12:00起报的2 m温度集合平均 (预报时效为30 h) 与观测场之差的空间分布
(a) 校准前，(b) 校准后
Fig. 1 Distribution of the difference of 30-hour 2 m temperation forecast at 1200 UTC 20 July 2008
(a) the difference between ensemble averge of model direct output and observation, (b) the difference between calibrated ensemble average and observation
图 3 2 m温度预报Talagrand分布
Fig. 3 Talagrand diagrams of raw and calibrated 2 m temperature (forecast lead time is 6 hours)
图 8 不同离散度因子的2 m温度预报CRPS评分
Fig. 8 CRPS of 2 m temperature from different spread rescaling
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