研究报告
张敏,蔡子颖,韩素芹.天津静稳指数建立及在环境气象预报和评估中的应用[J].环境科学学报,2020,40(12):4453-4460
天津静稳指数建立及在环境气象预报和评估中的应用
- Establishment of stable weather index of Tianjin and its application in environmental weather forecast and assessment
- 基金项目：国家自然科学基金（No.41771242）；天津市自然科学基金（No.18JCYBJC23100）；天津市重大专项（No.18ZXAQSF00130，18ZXSZSF00160）
- 作者
- 单位
- 张敏
- 1. 天津市环境气象中心, 天津 300074;2. 中国气象局-南开大学大气环境与健康研究联合实验室, 天津 300074
- 蔡子颖
- 天津市环境气象中心, 天津 300074
- 韩素芹
- 天津市环境气象中心, 天津 300074
- 摘要：基于2014—2015年NCEP再分析资料及天津地区PM2.5质量浓度数据，构建天津地区静稳指数以期反映大气综合扩散能力，为天津霾、空气质量预报和大气环境气象条件评估提供支撑.研究结果表明：本文构建的静稳指数可作为评估天津地区细颗粒物大气扩散能力的重要指标参数，其建模产品与天津地区PM2.5质量浓度相关系数为0.62，在评测期间（2016年6月1日—2018年5月31日），基于EC模式的静稳指数24 h预报产品与天津地区PM2.5质量浓度相关系数为0.67，1~7 d预报产品与PM2.5质量浓度相关系数超过0.53，8~10 d预报产品随着时效增加指示能力有所下降，相关系数为0.4左右，但通过显著性检验.对比同期基于GFS资料和WRF/Chem构建的天津中长期环境气象数值模式，其相关系数接近，趋势预报效果相当.同时，由于静稳指数综合了水平风速、混合层高度、相对湿度等多项指标，可以综合反映一个地区大气扩散能力，在大气环境气象条件评估中可发挥积极作用，以同排放源不同气象条件的数值模拟为基准定义细颗粒物大气扩散条件，2013—2018年逐月大气扩散条件同比上年改善的占总样本41%，同比转差的占总样本59%，利用静稳指数对大气扩散条件月同比变化趋势进行判断，识别准确率可达80%.
- Abstract：For the purpose of reflecting the atmospheric diffusion capacity, Stable Weather Index (SWI) was established based on the NCEP reanalysis data and PM2.5 mass concentration data in Tianjin from 2014 to 2015. It can be used for air quality forecast and environmental meteorological assessment. The results showed that SWI constructed in this paper can be used as an important index parameter to evaluate the atmospheric diffusion capacity of fine particles in Tianjin. The correlation coefficient between SWI established with NCEP data and PM2.5 from 2014 to 2015 was 0.62. The 1~10 days SWI forecast products were calculated with ECMWF forecasting data from 20160601 to 20180531, the correlation coefficient between 1-day SWI forecast product and the mass concentration of PM2.5 in Tianjin was 0.67, and the correlation coefficient between 1~7 days SWI forecast product and PM2.5 was greater than 0.53, the indicator ability of the SWI prediction products from 8 to 10 days decreased with a correlation coefficient of 0.4, but it also passed the significance test of 0.01. The forecast trend with SWI was similar to that with the medium and long-term numerical model of Tianjin WRF-Chem. At the same time, because SWI integrated multiple indicators such as horizontal wind speed, mixed layer height and relative humidity, it could comprehensively reflect the region atmospheric diffusion capacity and play an active role in the monthly and annual assessment of environmental meteorology. The standard atmospheric diffusion conditions of fine particles were defined with numerical simulations results with different meteorological conditions and the same pollution emission. During 2013 to 2018, the month when atmospheric diffusion conditions turn better and worse accounted for 41% and 59%, respectively. The accuracy of using SWI to identify the trend of monthly atmospheric diffusion conditions was more than 80%.
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