本文介绍了一个以Models-3为基础的自动化空气质量数值预报系统，该系统通过Gambas、Yabasic和R语言等工具进行开发，集成WRF-SMOKE-CMAQ三个模式，可通过监测数据进行自动修正，完成空气质量业务数值预报，并将结果发布到Web服务器上进行呈现。该系统对硬件的要求较低，将部署于一台DELLOptiplex9010工作站上，设置6km—2km双层嵌套，进行成都市空气质量数值预报。本文分析了成都市2014年1月1日至2014年12月31日的空气质量数值预报结果，评价系统对成都市NO2、SO2、PM10、PM2.5、O3、CO以及空气质量指数（AQI）的预报效果。结果显示，系统对于成都市2014年空气质量变化情况趋势的预报效果较好，302天有效预报中，24小时直接预报的空气质量等级准确率为58.27%，AQI预报相关系数0.71，观测值自动修正预报对24小时空气质量预报具有明显改善效果，使其等级预报准确率达到64.9%，相关系数提高到0.89。%A Models-3 based self-correcting air quality forecast system was discussed in this paper, the automated system was developed with Gambas, Yabasic and R language, which integrates 3 models including WRF, SMOKE and CMAQ. The forecast system captures monitor data from network, corrects the concentrations of different pollutants, and then public the results via web server. The hardware requirements of forecast system is relatively low and it was deployed on a DELL Optiplex 9010 workstation with a 6km-2km nested domain, giving operational air quality forecast for Chengdu. A estimation of the system was performed with 2014 forecasted concentrations and AQI, the results showed that the system well reflected the air quality variations in 2014, the hit rate of 24h direct forecast on air quality grads was 58.27% with a correlation coefficient of 0.71, and the corrected 24h forecast had a hit rate of 64.9% with a correlation coefficient of 0.89, the self-correcting method can improve the 24h forecast of Chengdu.
展开▼
