| 基于GRAPES-CMAQ的中山市空气质量预报系统预报效果评估 |
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| 引用本文： | 麦健华,于玲玲,邓涛,蒋争明,汤沛,刘江顺.基于GRAPES-CMAQ的中山市空气质量预报系统预报效果评估[J].热带气象学报,2018,34(1):78-86. |
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| 作者姓名： | 麦健华 于玲玲 邓涛 蒋争明 汤沛 刘江顺 |
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| 作者单位： | 1.中山市气象局, 广东 中山 528400 |
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| 基金项目： | 国家自然科学基金41775037中山市科技计划项目2015B2349广东省气象局科技创新团队计划项目201704 |
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| 摘 要： | 对基于GRAPES-CMAQ模式的广东省中山市空气质量预报系统2016年的AQI等级、首要污染物、PM2.5和O3浓度预报数据进行检验评估。预报系统对中山市全年的AQI等级24小时预报TS评分为0.66, TS评分在6—7月最高。当实况AQI等级为一级, TS为0.84, 当空气质量变差时, TS明显下降, 说明模式目前对空气重污染事件的预报能力不足。模式对中山市的主要污染物O3、PM和NO2的预报TS评分分别为0.21、0.24和0.24, 其中对O3和NO2的漏报率达到0.78和0.76, 而PM的空报率高达0.71。预报系统对PM2.5的浓度预报总体略偏高, 相关系数为0.59, 但在霾污染较严重的月份中预报效果并不稳定。对O3的预报全年相关系数达到0.7, 且在O3污染较严重的7—11月, 相关系数均能达到0.5以上, 但这几个月的O3预报浓度均比实况明显偏低。从气象预报入手分析预报系统对10月2—7日中山市一次连续O3污染过程漏报的原因, 发现GRAPES模式对这段时间内整个珠三角地区日间降水的预报范围偏大, 强度偏强, 且预报的地面风速也比实况要偏大, 导致模拟O3浓度比实况明显偏低。
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| 关 键 词： | 环境气象 空气质量 GRAPES-CMAQ 评估 O3 |
| 收稿时间： | 2016-11-20 |
ASSESSMENTS OF FORECAST PRODUCTS OF AIR QUALITY FORECASTING SYSTEM BASED ON GRAPES-CMAQ IN ZHONGSHAN CITY |
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| Institution: | 1.Zhongshan Meteorological Service, Zhongshan 528400, China2.Guangdong Meteorological Observation, Guangzhou 510080, China3.Guangzhou Institute of Tropical and Marine Meteorology, CMA, Guangzhou 510640, China4.Zhongshan Environmental Monitoring Station, Zhongshan 528400, China |
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| Abstract: | In this study, forecast products for 2016, including AQI level, primary pollutant, and PM2.5 and O3 concentrations, from an air quality forecasting system based on GRAPES-CMAQ model in Zhongshan city are assessed with the observations. The annual average TS score of forecasted AQI level was up to 0.66 and the best scores were found in June and July. When the observed AQI level was the first-class, the TS score was up to 0.84, but the TS score dropped evidently when the air pollution got worse, indicating that the model still could not give good performance in severe air pollution events. The annual average TS scores for the forecast of primary pollutants of O3, PM and NO2 were 0.21, 0.24 and 0.24, respectively. The PO for O3 and NO2 were up to 0.78 and 0.76, respectively, while the FAR for PM was 0.71. The annual average forecasted PM2.5 concentration was a little overestimated compared to the observations and the correlation coefficient was 0.59 for the whole year. However, the performances of the model were not stable among the months with heavy haze pollution. The correlation coefficient for O3 was 0.7 in 2016, and the coefficient of July to November with severe O3 pollution were all over 0.5. The forecasted O3 concentrations were all underestimated in these five months. An O3 pollution case during 2nd to 7th October 2016 was studied based on the forecasted data of GRAPES. It was found that the rainfall area and intensity in Pearl River Delta as well as the wind speed were all overestimated by GRPAES in the day time, leading to the underestimation of forecasted O3 concentrations in this event. |
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