Post-processing techniques can provide significant improvement in the forecast skill of air quality models. In this study, the implementation of an analog-based technique to Comprehensive Air Quality Model with Extensions (CAMx) coupled with Weather Research and Forecasting (WRF) model results is examined. WRF-CAMx runs with a 2-km horizontal grid increment over Greece for one month of every season of the year 2012 (i.e., January, April, July and October). The analog ensemble (AnEn) technique attempts to improve the accuracy of ozone and particulate matter forecasts by using a method that searches for analogs in past forecasts. An optimization process that minimizes Root Mean Square Error (RMSE) metric has been used to find the best AnEn configuration. The corrected forecasts are computed with two approaches, i.e., AnEn ‘mean’ and AnEn ‘bias correction’ (AnEn-bias) approach. The methods are tested with observations from 23 surface stations for ozone, 16 stations for PM10 and 3 stations for PM2.5 for an 11-day period for each month. The results which are very similar for both techniques show an improvement of the forecast skill of all pollutants. The corrected forecasts have smaller RMSE and higher Correlation Coefficient (R). A reduction of 40 and 70% for AnEn RMSE values is found for ozone and particulate matter, respectively. For AnEn R, an improvement of 11% for ozone, 46% for PM10 and 26% for PM2.5 is estimated. These techniques are also successful in drastically reducing the mean bias of raw forecasts to close to zero.

后处理技术可以大大改善空气质量模型的预测能力。在这项研究中，研究了基于模拟的技术对具有扩展功能的综合空气质量模型（CAMx）以及天气研究和预报（WRF）模型结果的实施。WRF-CAMx在希腊整个2012年每个季节的一个月（即一月，四月，七月和十月）以2公里的水平网格增量在希腊上空运行。模拟集合体（AnEn）技术试图通过使用一种在过去的预测中搜索类似物的方法来提高臭氧和颗粒物预测的准确性。已使用使最小均方根误差（RMSE）度量最小化的优化过程来找到最佳的AnEn配置。校正后的预测是通过两种方法计算的，即 AnEn“均值”和AnEn“偏差校正”（AnEn-bias）方法。每个月有11天的时间，对23个地面站的臭氧，16个PM10站和3个PM2.5站的观测值进行了测试。两种技术的结果非常相似，表明所有污染物的预测技术都有所提高。校正后的预测具有较小的RMSE和较高的相关系数（R）。臭氧和颗粒物的AnEn RMSE值分别降低了40％和70％。对于AnEn R，估计臭氧可提高11％，PM10可提高46％，PM2.5可提高26％。这些技术还可以成功地将原始预测的平均偏差降低到接近零。每个月为期11天的16个PM10站和3个PM2.5站。两种技术的结果非常相似，表明所有污染物的预测技术都有所提高。校正后的预测具有较小的RMSE和较高的相关系数（R）。臭氧和颗粒物的AnEn RMSE值分别降低了40％和70％。对于AnEn R，估计臭氧可提高11％，PM10可提高46％，PM2.5可提高26％。这些技术还可以成功地将原始预测的平均偏差降低到接近零。每个月为期11天的16个PM10站和3个PM2.5站。两种技术的结果非常相似，表明所有污染物的预测技术都有所提高。校正后的预测具有较小的RMSE和较高的相关系数（R）。臭氧和颗粒物的AnEn RMSE值分别降低了40％和70％。对于AnEn R，估计臭氧可提高11％，PM10可提高46％，PM2.5可提高26％。这些技术还可以成功地将原始预测的平均偏差降低到接近零。臭氧和颗粒物的AnEn RMSE值分别降低了40％和70％。对于AnEn R，估计臭氧可提高11％，PM10可提高46％，PM2.5可提高26％。这些技术还可以成功地将原始预测的平均偏差降低到接近零。臭氧和颗粒物的AnEn RMSE值分别降低了40％和70％。对于AnEn R，估计臭氧可提高11％，PM10可提高46％，PM2.5可提高26％。这些技术还可以成功地将原始预测的平均偏差降低到接近零。