能见度参数化方案优化及在北京地区的应用评估

王继康; 谢超; 张天航; 张碧辉; 张恒德; 饶晓琴

doi:10.12153/j.issn.1674-991X.20190186

能见度参数化方案优化及在北京地区的应用评估

doi: 10.12153/j.issn.1674-991X.20190186

国家气象中心

详细信息

作者简介:
王继康(1990—),男,硕士,主要研究方向为空气值模拟与预报,wjk_1990@126.com

通讯作者:
张天航 E-mail: sharp@mail.iap.ac.cn

中图分类号: X16
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出版历程
- 收稿日期: 2019-11-07
- 刊出日期: 2020-05-20

Modification of visibility parameterization scheme and its application evaluation in Beijing

National Meteorological Center

More Information

Corresponding author: ZHANG Tianhang E-mail: sharp@mail.iap.ac.cn

摘要

摘要: 为了利用订正后PM_2.5浓度预报结果提高能见度预报准确性,根据不同相对湿度下能见度与PM_2.5浓度的相关关系,提出了一种改进的能见度参数化方案(S1)。为了评估该方案的预报性能,利用基于最优多模式集成方法预报的PM_2.5浓度对北京市2019-02-18—03-05污染过程的能见度进行预报,并对比评估了利用美国大气能见度观测项目建立的经验参数化方案(IMPROVE,S2)和基于神经网络(S3)的能见度预报效果。结果表明:基于多模式最优集成方法对PM_2.5浓度和WRF4.0模式对相对湿度的预报效果均较好,相关系数达0.90。3种方案都对选取时段能见度的变化趋势有较好的预报,其中S1方案相关系数最高(0.85),均方根误差最小,预报效果最好;S1方案较S2方案平均偏差降低3.6 km;S1方案较S3方案预报效果的提升主要是在能见度大于10 km的范围内。PM_2.5浓度预报效果对能见度预报效果的影响高于相对湿度,但在相对湿度大于70%的情况下,其预报效果对能见度预报效果的影响更大。
- 能见度 /
- 参数化方案 /
- PM_2.5订正结果 /
- 预报评估
Abstract: An modified visibility parameterization scheme (S1) was proposed based on the correlation between visibility and PM_2.5 concentrations under different relative humidity, so as to improve the accuracy of visibility forecast by using the revised PM_2.5 concentrations forecast results. In order to evaluate the forecast performance of the scheme, the forecasted PM_2.5 concentrations based on a multi-model optimal integration method were used to forecast the visibility during the pollution process in Beijing for the period from February 18^th to March 5^th 2019. The scheme from Interagency Monitoring of Projected Visual Environments (IMPROVE, S2) and the a.pngicial neural network (S3) were also used to forecast the visibility in this period. The forecast results were compared and evaluated. The results showed that the forecast results of PM_2.5 concentration based on the multi-model optimal integration method and relative humidity based on WRF4.0 mode were good, and their correlation coefficient reached 0.90. All of the three schemes had good forecast for the trend of visibility in the selected period. S1 hold the best performances with the highest correlation coefficient (0.85) and lowest root mean square error (RMSE). The average deviation of homogenization in S1 was 3.6 km lower than that in S2. S1 performed better than S3 in range of the visibility greater than 10 km. The effect of PM_2.5 concentrations forecast on visibility forecast was higher than that of relative humidity forecast. The effect of relative humidity forecast could be more significant when the relative humidity exceeded 70%.
- visibility /
- parameterization scheme /
- revised PM_2.5 forecast /
- forecast evaluation

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