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Combining 2-m temperature nowcasting and short range ensemble forecasting

机译：结合2-m温度临近预报和短距离系综预报

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During recent years, numerical ensemble prediction systems have become an important tool for estimating the uncertainties of dynamical and physical processes as represented in numerical weather models. The latest generation of limited area ensemble prediction systems (LAM-EPSs) allows for probabilistic forecasts at high resolution in both space and time. However, these systems still suffer from systematic deficiencies. Especially for nowcasting (0–6 h) applications the ensemble spread is smaller than the actual forecast error. This paper tries to generate probabilistic short range 2-m temperature forecasts by combining a state-of-the-art nowcasting method and a limited area ensemble system, and compares the results with statistical methods. The Integrated Nowcasting Through Comprehensive Analysis (INCA) system, which has been in operation at the Central Institute for Meteorology and Geodynamics (ZAMG) since 2006 (Haiden et al., 2011), provides short range deterministic forecasts at high temporal (15 min–60 min) and spatial (1 km) resolution. An INCA Ensemble (INCA-EPS) of 2-m temperature forecasts is constructed by applying a dynamical approach, a statistical approach, and a combined dynamic-statistical method. The dynamical method takes uncertainty information (i.e. ensemble variance) from the operational limited area ensemble system ALADIN-LAEF (Aire Limitée Adaptation Dynamique Développement InterNational Limited Area Ensemble Forecasting) which is running operationally at ZAMG (Wang et al., 2011). The purely statistical method assumes a well-calibrated spread-skill relation and applies ensemble spread according to the skill of the INCA forecast of the most recent past. The combined dynamic-statistical approach adapts the ensemble variance gained from ALADIN-LAEF with non-homogeneous Gaussian regression (NGR) which yields a statistical mbox{correction} of the first and second moment (mean bias and dispersion) for Gaussian distributed continuous variables. Validation results indicate that all three methods produce sharp and reliable probabilistic 2-m temperature forecasts. However, the statistical and combined dynamic-statistical methods slightly outperform the pure dynamical approach, mainly due to the under-dispersive behavior of ALADIN-LAEF outside the nowcasting range. The training length does not have a pronounced impact on forecast skill, but a spread re-scaling improves the forecast skill substantially. Refinements of the statistical methods yield a slight further improvement.
机译：近年来，数值集成预报系统已成为估算数值天气模型所代表的动力学和物理过程不确定性的重要工具。最新一代的有限区域集合预测系统（LAM-EPS）可以在空间和时间上以高分辨率进行概率预测。但是，这些系统仍然存在系统缺陷。特别是对于临近预报（0-6小时）的应用，集合扩展小于实际的预测误差。本文尝试通过结合最新的临近预报方法和有限区域集成系统来生成概率为2 m的短程温度预报，并将结果与​​统计方法进行比较。自2006年以来，中央气象与地球动力学研究所（ZAMG）一直在使用“综合分析综合临近预报（INCA）”系统（Haiden等，2011），该系统可在高时空（15分钟– 60分钟）和空间（1公里）分辨率。通过应用动态方法，统计方法和组合的动态统计方法，构造了2米温度预报的INCA集合（INCA-EPS）。动力学方法从运行在ZAMG上的有限区域集成系统ALADIN-LAEF（AireLimitée适应性动力学发展国际有限区域集合预报）中获取不确定性信息（Wang等，2011）。纯粹的统计方法假定校准良好的传播技巧之间的关系，并根据INCA最近的预测技巧应用整体传播。组合的动态统计方法采用非均质的高斯回归（NGR）调整了从ALADIN-LAEF获得的整体方差，从而得出了高斯分布连续变量的第一和第二阶矩（均值偏差和离散度）的统计 mbox {校正} 。验证结果表明，这三种方法都能产生准确可靠的2 m温度概率预报。但是，统计方法和组合动态统计方法的性能略好于纯动力学方法，这主要是由于ALADIN-LAEF的色散行为在临近预报范围之外所致。训练时间对预测技能没有明显的影响，但是扩展重新定标可以显着提高预测技能。对统计方法的细化带来了进一步的改进。

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