Gridded probabilistic weather forecasts with an analog ensemble
Gridded probabilistic weather forecasts with an analog ensemble
复制标题
使用模拟集合进行网格概率天气预报
DOI：
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发表时间：
2017
期刊：
影响因子：
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通讯作者：
Luca Delle Monache
中科院分区：
文献类型：
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作者：
S. Sperati;S. Alessandrini;Luca Delle Monache
这项研究扩展了模拟集合(ANEn)技术，以生成网格上10米风速的概率预报。ANEN已被广泛用于生成气象变量、空气质量、风能和太阳能的短期概率预报，并有效地缩小再分析领域的规模。它基于一个历史数据集，其中包括与相应的确定性预测配对的观察结果。对于每个预测提前期和位置，使用与更类似于当前预测的过去确定性预测相对应的观测值来生成ANEn。到目前为止，ANEn已经被用来利用可用的观测来生成特定地点的预测。在这里，它被扩展到一个二维网格上，其中每个网格点被单独处理，使用气象分析而不是观测。预测范围提前6天，以评估ANEn在短期和中期的表现。所提出的方法允许将ANEN扩展到需要网格化概率积的应用，这些网格化概率积通常是由动态系综模型生成的。ANEn预报是使用欧洲中期天气预报中心(ECMWF)确定性和分析模式生成的。此外，还使用ECMWF集合预报系统(EPS)进行了比较。由于ANEn预测是在任何位置和提前时间独立生成的，因此产生的空间和时间相关性可能会因噪声而恶化。然后，将一种称为Schaake Shuffle(SS)的重新排序技术应用于集合成员以恢复时空相关性。在预测的头两天，ANEn的表现优于EPS的校准版本。在第三个预测日内，ANEn仍与校准后的EPS保持竞争力，而后者在72至144小时内更熟练。ANEn使用大约六分之一的计算资源来生成实时每股收益预测。
This study extends the analog ensemble (AnEn) technique to generate probabilistic forecasts of 10 m wind speed over a grid. The AnEn has been widely used to generate short‐term probabilistic predictions of meteorological variables, air quality, wind and solar power, and to effectively downscale reanalysis fields. It is based on a historical dataset including observations paired with corresponding deterministic predictions. For each forecast lead time and location, the AnEn is generated using the observations corresponding to the past deterministic predictions that are more similar to the current forecast. So far, the AnEn has been used to generate predictions at specific locations with available observations. Here, it is extended over a two‐dimensional grid, where each grid point is treated independently, using meteorological analysis instead of observations. The forecast range is extended up to 6 days ahead to evaluate the performance of the AnEn in both the short and medium range. The proposed approach allows to extend the AnEn to applications requiring gridded probabilistic products that are often generated with dynamical ensemble models. The AnEn forecasts are generated using the European Centre for Medium‐Range Weather Forecasts (ECMWF) deterministic and analysis model. Also, the ECMWF Ensemble Prediction System (EPS) is used for comparison. Given that the AnEn predictions are generated independently at any location and lead time, the resulting spatial and temporal correlation may be degraded by noise. A reordering technique called Schaake Shuffle (SS) is then applied to the ensemble members to recover the spatio‐temporal correlation. The AnEn outperforms a calibrated version of EPS for the first two days of the prediction. During the third forecast day, the AnEn remains competitive with the calibrated EPS, while the latter is more skilful from 72 to 144 h ahead. The AnEn uses about one sixth of the computational resources necessary to generate the real‐time EPS prediction.
影响因子：
5.7
作者：
Roman Schefzik;T. Thorarinsdottir;T. Gneiting
通讯作者：
Roman Schefzik;T. Thorarinsdottir;T. Gneiting