摘要
对高分辨率数值天气预报产品进行精度评估时考虑其时空不确定性是有必要的。从降水量级、空间分布和时间分布多角度出发建立降雨预报评估方法,选取2021年诺敏河流域的实测数据和预报数据,采用上述方法对NMCP模式进行了综合评价。结果表明:1) 在降水量级上,以相对误差作为评价指标。前36 h的NMCP模式相对误差在0.8以上,尤其对短时强降雨量预报精度较高;2) 在空间分布上,采用邻域法改善空间不确定性,结合FSS评分确定NMCP在该流域的最优邻域半径为60 km。3) 在时间分布上,引入时间域半径,结合TFSS评分确定最优时间域半径为2 h。前48 h的NMCP模式对大雨及以下的预报精度较高,FSS与TFSS评分均达到0.8以上,对于暴雨表现较差。该评价方法能够较好的反映降雨预报数据的时空分布,更加真实客观地评价降雨预报精度,为今后的多模式集成与气象水文耦合等应用提供理论基础。
It is necessary to consider the spatiotemporal uncertainty in the accuracy evaluation of high-resolution numerical weather forecast products. Based on the precipitation magnitude, spatial distribution and time distribution, a rainfall forecast evaluation method is established. The measured data and forecast data of NOMIN River Basin in 2021 are selected to comprehensively evaluate the NMCP model. The results show that: 1) In terms of precipitation magnitude, the relative error is used as the evaluation index. The relative error of NMCP model in the first 36 hours is more than 0.8, especially for short-term strong rainfall prediction;2) In terms of spatial distribution, neighborhood method is used to improve spatial uncertainty. Combined with FSS score, the optimal neighborhood radius of NMCP in the basin is determined to be 60 km. 3) In terms of time distribution, the time domain radius is introduced, and the optimal time domain radius is determined to be 2 h in com-bination with TFSS score. The NMCP model in the first 48 hours has high prediction accuracy for heavy rain and below, with FSS and TFSS scores above 0.8, and poor performance for heavy rain. The evaluation method can better reflect the spatial-temporal distribution of rainfall forecast data, and more truly and objectively evaluate the accuracy of rainfall forecast, providing a theoretical basis for the future application of multi-mode integration and meteorological hydrological coupling.
出处
《自然科学》
2022年第6期969-979,共11页
Open Journal of Nature Science