Abstract:Based on the Tianjin extended period Air Quality Numerical forecasting system (CFS/WRF-Chem), this study introduced Nudging technology to restrict the model integration process, and conducted sensitivity experiments by Grid Nudging and Spectral Nudging. Through the optimization scheme experiments of parameters such as Nudging wave number, Nudging time and Nudging variables, the improvement applicability of Nudging technology in the extended period PM2.5 numerical prediction to effectively enhance the accuracy of air quality trend prediction for 10~45 days in autumn and winter in Tianjin. The results showed that: Compared to the extend period numerical model prediction without Nudging technology, no matter the Grid Nudging or Spectral Nudging, prediction improved after using Nudging technology. The correlation coefficient between daily prediction and observed PM2.5 concentration increased from 0.1without Nudging technology to 0.35 by Grid Nudging and 0.43 by Spectral Nudging, and the prediction accuracy of PM2.5 grade increased from 33% to 47% and 44%, respectively. Compared to the selection of Grid Nudging and Spectral Nudging scheme, the optimal configuration of parameters such as Nudging wave number, Nudging time and Nudging variables, was more critical to the application of Nudging technology. In Grid Nudging, the best prediction was the experiment by Nudging coefficient was 5×10-5, Nudging time was 6h, and Nudging variable was θ-q-uv. In Spectral Nudging, the best prediction is the experiment by Nudging wave number was 1, truncated wavelength was 1800km, and Nudging variable was θ-q-uv. Even using Nudging technology, there are still great uncertainties in the extended period daily PM2.5 concentration prediction, which is more suitable for trend prediction (increase or decrease). According to the analysis of the PM2.5 concentration trend between pentads, the prediction accuracy was 50% without Nudging technology, and increased to 75% by Grid Nudging, and 88% by Spectral Nudging. Spectral Nudging provided a slightly better prediction, which could effectively support the extended period PM2.5 trend prediction by pentad resolution.
唐颖潇, 秦明月, 蔡子颖, 杨旭. 基于松弛逼近方法的天津PM2.5延伸期数值预报研究[J]. 中国环境科学, 2023, 43(9): 4503-4511.
TANG Ying-xiao, QIN Ming-yue, CAI Zi-ying, YANG Xu. Numerical prediction technology of extended period PM2.5 in autumn and winter in Tianjin based on Nudging method. CHINA ENVIRONMENTAL SCIENCECE, 2023, 43(9): 4503-4511.
林廷坤,洪礼楠,黄争超,等.北京市秋冬季大气环流型下的气象和污染特征[J]. 中国环境科学, 2019,39(5):1813-1822. Lin T K, Hong L N, Huang Z C, et al. Meteorological and pollution characteristics under atmospheric circulation types in autumn and winter in Beijing[J]. China Environmental Science, 2019,39(5):1813-1822.
[2]
吕梦瑶,张恒德,王继康,等.2015年冬季京津冀两次重污染天气过程气象成因[J]. 中国环境科学, 2019,39(7):2748-2757. Lü M Y, Zhang H D, Wang J K, et al. Analysis of meteorological causes of two heavily polluted weather processes in Beijing-Tianjin-Hebei Region in winter of 2015[J]. China Environmental Science, 2019,39(7):2748-2757.
[3]
程念亮,李云婷,张大伟,等.2013年1月北京市一次空气重污染成因分析[J]. 环境科学, 2015,36(4):10. Cheng N L, Li Y T, Zhang D W, et al. Formation mechanism of a serious pollution event in January 2013 in Beijing[J]. Environmental Science, 2015,36(4):1154-1163.
[4]
蔡子颖,韩素芹,邱晓滨,等.基于WRF/Chem模式天津地区重污染天气成因分析[J]. 高原气象, 2019,38(5):12. Cai Z Y, Han S Q, Qiu X B, et al. 2019. Research on causes of severely polluted weather in Tianjin based on WRF/Chem[J]. Plateau Meteorology, 38(5):1108-1119.
[5]
Tai A, Mickley L J, Jacob D J. Correlations between fine particulate matter (PM2.5) and meteorological variables in the United States:implications for the sensitivity of PM2.5 to climate change[J]. Atmospheric environment, 2010,(32):3976-3984.
[6]
He J, Gong S, Yu Y, et al. Air pollution characteristics and their relation to meteorological conditions during 2014~2015 in major Chinese cities[J]. Environmental Pollution, 2017,223:484-496.
[7]
Zhang H, Wang Y, Hu J, et al. Relationships between meteorological parameters and criteria air pollutants in three megacities in China[J]. Environmental Research, 2015,140:242-254.
[8]
Hossein S, Sajjad K, Vahid H, et al. A novel regression imputation framework for Tehran air pollution monitoring network using outputs from WRF and CAMx models[J]. Atmospheric Environment, 2018,187:24-33.
[9]
关攀博,周颖,程水源,等.典型重工业城市空气重污染过程特征与来源解析[J]. 中国环境科学, 2020,40(1):31-40. Guan P B, Zhou Y, Cheng S Y, et al. Characteristics of heavy pollution process and source appointment in typical heavy industry cities[J]. China Environmental Science, 2020,40(1):31-40.
[10]
周广强,谢英,吴剑斌,等.基于WRF-Chem模式的华东区域PM2.5预报及偏差原因[J]. 中国环境科学, 2016,36(8):2251-2259. Zhou G Q, Xie Y, Wu J B, et al. WRF-Chem based PM2.5 forecast and bias analysis over the East China Region[J]. China Environmental Science, 2016,36(8):2251-2259.
[11]
朱蓉,张存杰,梅梅.大气自净能力指数的气候特征与应用研究[J]. 中国环境科学, 2018,38(10):3601-3610. Zhu R, Zhang C J, Mei M. The climate characteristics of atmospheric self-cleaning ability index and its application in China[J]. China Environmental Science, 2018,38(10):3601-3610.
[12]
Wei Y, Chen X, Chen H, et al. IAP-AACM v1.0:a global to regional evaluation of the atmospheric chemistry model in CAS-ESM[J]. Atmospheric Chemistry and Physics, 2019,19(12):8269-8296.
[13]
杨欣悦,谭钦文,陆成伟,等.基于CFSv2的延伸期空气质量数值预报技术及效果评估[J]. 中国环境监测, 2021,37(5):175-184. Yang X Y, Tan Q W, Lu C W, et al. Numerical prediction technology and effect evaluation of extended period air quality based on CFSv2[J]. Environmental Monitoring in China, 2021,37(5):175-184.
[14]
常炉予,谷怡萱,周广强,等.长三角环境气象预报预警中心,.华东区域环境气象延伸期预报技术研发[Z]. 2020-09-24. Chang L Y, Gu Y X, Zhou G Q, et al. Yangtze River Delta Center for Environmental Meteorology Prediction and Warning, Forecast technology of environmental meteorological extended period in east China[Z]. 2020-09-24.
[15]
Storch H V, Langenberg H, Feser F. A spectral nudging technique for dynamical downscaling purposes[J]. Mon.wea.rev, 2000,128(10):3664-3673.
[16]
Miguez M G, Stenchikow G L, Robock A. Spectral nudging to eliminate the effects of domain position and geometry in regional climate model simulations[J]. Journal of Geophysical Research:Atmospheres, 2004,109(D13):1025-1045.
[17]
Salameh T, Drobinski P, Dubos T. The effect of indiscriminate nudging time on large and small scales in regional climate modelling:Application to the Mediterranean basin[J]. Quarterly Journal of the Royal Meteorological Society, 2010,136(646):170-182.
[18]
Omrani H, Drobinski P, Dubos T. Investigation of indiscriminate nudging and predictability in a nested quasi-geostrophic model[J]. Quarterly Journal of the Royal Meteorological Society, 2012,138(662):158-169.
[19]
Hoke J E, Anthes R. The initialization of numerical models by a dynamic-initialization technique[J]. 1976,104(12):1551-1556.
[20]
易雪,李得勤,赵春雨,等.分析Nudging对辽宁地区降尺度的影响[J]. 地球科学进展, 2018,33(5):517-531. Yi X, Li D Q, Zhao C Y, et al. Assessment of dynamical climate downscaling methods using analysis nudging for Liaoning Area[J]. Advances in Earth Science, 2018,33(5):517-531.
[21]
李明妍,崔志强,王澄海.Nudging方法对中国西北强降水过程的模拟试验研究[J]. 气候与环境研究, 2017,22(5):563-573. Li M Y, Cui Z Q, Wang C H. A numerical simulation study on Heavy rain processes in Northwest China using the Nudging method[J]. Climatic and Environmental Research, 2017,22(5):563-573.
[22]
Xu X, Xie L, Cheng X, et al. Application of an adaptive Nudging scheme in air quality forecasting in China[J]. Journal of Applied Meteorology and Climatology, 2008,47(8):2105-2114.
[23]
Cheng X H, Xu X D, Ding G A. An emission source inversion model based on satellite data and its application in air quality forecasts[J]. Science China, 2010,53(5):752-762.
[24]
李嘉鼎,孟凯,赵天良,等.基于CMAQ模式的自适应"nudging"源反演方法的中国主要污染区排放特征分析[J]. 环境科学学报, 2020, 40(3):754-762. Li J D, Meng K, Zhao T L, et al. Air pollutant emission characteristics over major pollution areas in China based on adaptive "nudging" method with CMAQ model[J]. Acta Scientiae Circumstantiae, 2020, 40(3):754-762.
[25]
赵俊日,肖昕,吴涛,等.空气质量数值预报优化方法研究[J]. 中国环境科学, 2018,38(6):2047-2054. Zhao J R, Xiao X, Wu T, et al. A revised approach to air quality forecast based on Models-3/CMAQ[J]. China Environmental Science, 2018,38(6):2047-2054.
[26]
熊一帆,丁秋冀,舒卓智,等.基于数值模拟与资料同化探究长三角地区冬季PM2.5污染过程的气象影响[J]. 环境科学学报, 2022,42(4):293-303. Xiong Y F, Ding Q J, Shu Z Z, et al. The influence of meteorological parameters on particulate matter in the Yangtze River Delta Region based on numerical simulation and data assimilation[J]. Acta Scientiae Circumstantiae, 2022,42(4):293-303.
[27]
邱雨露,陈磊,朱佳,等.COVID-19管控期间气象条件变化对京津冀PM2.5浓度影响[J]. 环境科学, 2022,43(6):2831-2839. Qiu Y L, Chen L, Zhu J, et al. Impacts of changes in meteorological conditions during COVID-19 lockdown on PM2.5 concentrations over the Jing-Jin-Ji Region[J]. Environmental Science, 2022,43(6):2831-2839.
[28]
Liang F, Meng G, Xiao Q, et al. Evaluation of a data fusion approach to estimate daily PM2.5 levels in North China[J]. Environmental Research, 2017,158:54-60.
[29]
Du Q, Zhao C, Zhang M, et al. Modeling diurnal variation of surface PM2.5 concentrations over East China with WRF-Chem:impacts from boundary-layer mixing and anthropogenic emission[J]. Atmospheric Chemistry and Physics, 2020,(5):2839-2863.
[30]
Saha S, Moorthi S, Wu X, et al. The NCEP climate forecast system version 2[J]. Journal of climate, 2014,27(6):2185-2208.
[31]
郎杨.CFSv2在中国区域的季节干旱可预报性研究[D]. 北京:北京师范大学, 2015:1-40. Lang Y. CFSv2-based seasonal drought predictability in China[D]. Beijing:Beijing Normal University, 2015:1-40.
[32]
Stauffer D R, Seaman N L. Use of four-dimensional data assimilation in a limited-area mesoscale model. Part I:Experiments with synoptic-scale data[J]. Monthly Weather Review, 1990,118(6):1250-1277.
[33]
Glisan J M, Gutowski Jr W J, Cassano J J, et al. Effects of spectral nudging in WRF on Arctic temperature and precipitation simulations[J]. Journal of Climate, 2013,26(12):3985-3999.
[34]
Waldron K M, Paegle J, Horel J D. Sensitivity of a spectrally filtered and nudged limited-area model to outer model options[J]. Monthly weather review, 1996,124(3):529-547.
[35]
von Storch H, Langenberg H, Feser F. A spectral nudging technique for dynamical downscaling purposes[J]. Monthly weather review, 2000,128(10):3664-3673.
[36]
Choi S J, Lee D K. Impact of spectral nudging on the downscaling of tropical cyclones in regional climate simulations[J]. Advances in Atmospheric Sciences, 2016,33(6):730-742.
[37]
陈伯民,梁萍,信飞,等.延伸期过程预报预测技术及应用[J]. 气象科技进展, 2017,7(6):82-91. Chen B M, Liang P, Xin F, et al. The extended-range process prediction technique and application[J]. Advances in Meteorological Science and Technology, 2017,7(6):82-91.
[38]
郑广芬,王素艳,杨建玲,等.基于前期海温异常的宁夏5~9月候降水量客观预测方法及检验评估[J]. 干旱气象, 2016,34(1):43-50. Zheng G F, Wang S Y, Yang J L, et al. Study on the objective prediction method and its vefificion and assessment for pentad precipitation from May to September in Ningxia based on the preceding SST anomaly[J]. Journal of Arid Meteorology, 2016,34(1):43-50.
Bullock Jr O R, Alapaty K, Herwehe J A, et al. An observation-based investigation of nudging in WRF for downscaling surface climate information to 12-km grid spacing[J]. Journal of Applied Meteorology and Climatology, 2014,53(1):20-33.
[41]
Mai X, Ma Y, Yang Y, et al. Impact of Grid Nudging parameters on dynamical downscaling during summer over mainland China[J]. Atmosphere, 2017,8(10):184.