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Beijing Regional Environmental Meteorology Prediction System and Its Performance Test of PM2.5 Concentration
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摘要: 基于北京地区快速更新循环同化预报系统 (BJ-RUC)、WRF-Chem模式和优选的能见度参数化方案，建立了北京区域环境气象数值预报系统。对2014年全年PM2.5浓度、能见度和APEC (Asia-Pacific Economic Cooperation) 期间预报效果检验结果表明:该系统对京津冀及周边地区PM2.5浓度的预报效果较好，大部分站点的相关系数在0.6以上，特别是北京的部分站点可达0.8以上，预报结果相比观测总体偏低，随着预报时效的延长，24 h之后预报效果略有下降。相比人工观测，能见度预报结果与自动观测能见度更加接近，对持续性低能见度过程预报与实况吻合较好，对于小时能见度低于10 km的分级检验显示，预报准确率从77%左右逐级下降，2 km以下在40%左右。2014年APEC期间，系统很好地预报出北京地区空气质量指数、PM2.5浓度和能见度的时空演变特征，为APEC期间环境气象预报服务提供了有力的技术支撑。Abstract: Beijing Regional Environmental Meteorology Prediction System (BREMPS) is established by coupling BJ-RUC, WRF-Chem and preferred visibility parameterization scheme. The performance test with observations in 2014 and Asia-Pacific Economic Cooperation (APEC) period shows that BREMPS has a good forecasting ability for two important elements in air quality and haze forecasting in Beijing and surrounding area, PM2.5 concentration and visibility. Correlation coefficients of PM2.5 between forecasted and observed values reaches above 0.6 at most sites, and even reaches above 0.8 at some sites in Beijing. Forecasted values generally underestimate the PM2.5 concentrations with a regional average normalized mean bias of-15%. The forecast performance shows slight decrease after 24 forecasted hours. Comparing with the regional average, the forecast performance is best in Beijing urban area and northern of Hebei. The forecasted PM2.5 concentration agrees well with the observation in Beijing area. Correlation coefficients of PM2.5 concentrations between forecasted and observed values in 48 forecasted hours are about 0.77 in urban area. The normalized mean bias is generally in a range of-26%. The correlation coefficient in rural area is higher than that in urban area. However, the mean bias is also higher in rural area. That is probably attributed to the inaccuracy of the emission information in these areas. The forecast performance is better in spring, autumn and winter, during which the correlation coefficient between forecasted and observed values mostly ranges from 0.7 to 0.9, and the normalized mean bias is within 20%. The forecasted visibility is closer to automatic measurements than artificial observations. Forecasted values are in good agreement with observations during sustained low visibility synoptic processes. For the hourly visibility lower than 10 km, the accuracy of forecast is 77%, which decreases with the reduction of visibility and reaches 40% when the visibility is lower than 2 km. BREMPS shows good forecast performance during the APEC period, when the temporal evolutions of AQI and visibility, and spatial distribution of PM2.5 concentrations are well forecasted, providing strong support for the environmental meteorology forecast service.
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图 2 北京区域环境气象数值预报系统 (BREMPS) 的主要架构
Fig. 2 Main frame of Beijing regional environmental meteorology prediction system
图 3 24 h预报的PM2.5日平均浓度与观测的检验统计值空间分布
(a) 相关系数，(b) 归一化平均偏差
Fig. 3 Spatial distribution of correlation coefficient (a) and normalized mean bias (b) between 24 h forecasted and observed daily mean PM2.5 concentration
图 4 北京城区 (a) 和郊区 (b) PM2.5日平均浓度观测与24 h预报对比
Fig. 4 Comparison between observation and 24 h forecast of daily mean PM2.5 concentration in Beijing urban (a) and rural (b) areas
图 5 北京城区PM2.5观测与预报检验统计量逐月变化
Fig. 5 Monthly test statistic for observation and forecast of PM2.5 in Beijing urban area
图 6 2014年观测与24 h预报的日平均能见度
(a) 北京市观象台，(b) 海淀站
Fig. 6 Observation and 24 h forecast of daily mean visibility
(a) Beijing Weather Observatory, (b) Haidian Station
图 7 2014年观测与24 h预报的日平均能见度散点拟合
(a) 北京市观象台, (b) 海淀站
Fig. 7 Linear fitting between observation and 24 h forecast of daily mean visibility
(a) Beijing Weather Observatory, (b) Haidian Station
图 8 2014年10月26日—11月13日北京地区观测AQI与海淀站和官园站预报AQI对比
(a) 逐日AQI对比，(b) 观测与海淀站预报AQI拟合，(c) 观测与官园站预报AQI拟合
Fig. 8 Comparison between observed AQI in Beijing area and forecasted AQI at Haidian and Guanyuan stations from 26 Oct to 13 Nov in 2014
(a) daily AQI, (b) linear regression between the observed and the forecasted AQI at Haidian Station, (c) linear regression between the observed and the forecasted AQI at Guanyuan Station
图 9 2014年11月7日08：00—11日02：00观测与预报PM2.5小时浓度空间分布
(填色为24 h预报结果，实心圆点代表观测结果)
Fig. 9 Spatial distributions of observed and forecasted hourly concentrations of PM2.5from 7 Nov to 11 Nov in 2014
图 10 2014年11月1—13日北京市观象台观测与预报小时能见度对比
Fig. 10 Comparison of hourly visibility between observation and 24 h forecast at Beijing Weather Observatory from 1 Nov to 13 Nov in 2014
表 1 年平均PM2.5日平均浓度的预报与观测检验统计
Table 1 Test statistics for forecasted and observed annual mean daily average PM2.5 concentration
区域 浓度/(μg·m-3) 相关系数 归一化平均偏差/% 平均误差/(μg·m-3) 观测 24 h 48 h 72 h 24 h 48 h 72 h 24 h 48 h 72 h 24 h 48 h 72 h 北京城区 88.0 78.9 69.7 65.2 0.77 0.77 0.69 -10.3 -20.8 -25.9 -9.0 -18.2 -22.7 北京郊区 76.7 49.2 45.8 43.1 0.83 0.79 0.75 -35.8 -40.1 -43.7 -27.4 -30.6 -33.4 天津 85.4 59.0 49.8 45.7 0.69 0.64 0.56 -30.9 -41.7 -46.5 -26.4 -35.6 -39.7 石家庄 132.5 91.6 81.5 77.0 0.68 0.62 0.53 -30.9 -38.5 -41.8 -40.9 -51.0 -55.4 河北中南部 117.5 82.5 71.3 67.8 0.73 0.66 0.57 -29.8 -39.3 -42.3 -35.0 -46.2 -49.7 河北北部 55.6 44.5 39.5 37.5 0.77 0.76 0.73 -20.0 -28.9 -32.4 -11.1 -16.1 -18.0 整个区域 74.3 51.8 45.2 43.0 0.73 0.69 0.65 -30.2 -39.2 -42.1 -22.5 -29.1 -31.3 
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表 2 北京市观象台能见度分级检验统计表
Table 2 Classification statistics of forecasted visibility at Beijing Weather Observatory
分级/km 小时样本量 准确率/% 日样本量 准确率/% 观测 预报 观测 预报 [10，30) 1333 1073 80.5 200 179 89.5 [0，10) 1424 1098 77.1 165 111 67.3 [0，5) 903 562 62.2 89 50 56.2 [0，2) 307 125 40.7 24 2 8.3 [2，5) 597 229 38.4 66 29 43.9 [5，10) 522 174 33.3 77 29 37.7
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