Ozone Forecasting Performance Evaluation and Error Source Analysis of GRACEs Model Under Different Synoptic Patterns in Guangdong
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摘要: 利用2018—2020年广东省空气质量和气象要素监测数据、再分析资料、CMA模式预报资料和客观天气分型方法,开展不同天气型下华南区域大气成分数值模式系统(GRACEs)臭氧预报性能评估及其误差来源分析。结果表明:(1)GRACEs模式对O3_8h浓度趋势预报较好,但对O3_8h及其前体物NO2浓度预报值总体偏低,其中NO2浓度预报偏差更显著。(2)在台风外围+冷高压脊(TPR)和弱冷高压脊(HR)天气型下,臭氧平均浓度和臭氧污染城次概率最高,且模式对此类型天气下O3_8h浓度的预报能力亦最差。NO2预报偏差是导致O3_8h浓度预报偏差的重要原因,而CMA模式对边界层气象要素预报值的偏差可进一步导致O3_8h浓度预报偏低。(3)GRACEs模式对臭氧污染的漏报率较高,相对于整体预报水平,在TPR天气型下GRACEs模式对NO2浓度预报偏低程度更大,HR天气型下模式对2 m气温预报负偏差也更明显。(4)从空间分布来看,GRACEs模式对广东省东西两翼城市O3_8h浓度预报效果较好,而GRACEs模式对NO2浓度和CMA模式对全省21个城市2 m气温预报偏低的分布差异是导致O3_8h浓度预报效果分布差异的重要原因。Abstract: Based on the observed data of air quality and meteorological elements, reanalysis data and CMA model forecast products of Guangdong during 2018-2020, along with an objective classification method, this study conducts a comprehensive evaluation of the ozone forecasting performance and error source analysis of the Guangzhou Regional Atmospheric Composition and Environment Forecasting System (GRACEs). The key findings are as follows: (1) The GRACEs system forecast the trend of O3_8h concentration relatively well, but systematically underestimated both the concentrations of O3_8h and its precursor NO2, with the NO2 bias being particularly pronounced. (2) Under the control of typhoon periphery combined with cold high ridge (TPR) and weak cold high ridge (HR), the average ozone concentration and the rate of cities over standard were highest, while the model's skill in forecasting O3_8h was worst. The forecast deviation of NO2 concentration was an important reason for the O3_8h concentration bias, and the forecast deviation of boundary layer meteorological elements from CMA model might further lead to the underestimation of O3_8h concentration. (3) The GRACEs model had a high omission rate for ozone pollution. Compared with the overall dataset, a larger negative forecasting deviation of NO2 concentration appeared under the control of TPR, and the negative bias in 2 m temperature became more pronounced under the HR conditions. (4) As for the spatial distribution, GRACEs delivered superior O3_8h forecasts of O3_8h concentration over the eastern and western wings of Guangdong. The distinct patterns of the negative NO2 concentration forecasting deviation by GRACEs and 2 m temperature by the CMA model were the important reasons for the distribution difference of O3_8h concentration forecast skill.
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表 1 CMA模式和CMAQ模式的网格设置
CMA模式 CMAQ模式 嵌套区域 网格数 网格距/km 中心经纬度 嵌套区域 网格数 网格距/km 中心经纬度 1 283×184 27 121.86 °E, 23.05 °N 1 182×138 27 121.86 °E, 23.05 °N 2 233×163 9 117.55 °E, 23.02 °N 2 98×74 9 117.55 °E, 23.02 °N 3 172×130 3 113.38 °E, 22.93 °N 3 152×110 3 113.38 °E, 22.93 °N 表 2 2018—2020年广东省GRACEs模式预报结果检验
要素 平均浓度/(μg·m-3) R RMSE/(μg·m-3) MB/(μg·m-3) 24 h 48 h 72 h 24 h 48 h 72 h 24 h 48 h 72 h O3_8h 90 0.60 0.51 0.43 35.9 37.8 40.0 -11.8 -7.9 -6.2 NO2 25 0.48 0.43 0.41 14.6 15.2 15.5 -2.4 -2.7 -2.4 表 3 2018—2020年广东省GRACEs模式臭氧污染漏报率
天气型 污染总城次/个 24 h 48 h 72 h TPR 290 99.3% 97.3% 97.6% HR 126 99.0% 95.8% 96.9% OT 668 90.8% 85.1% 88.2% -
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