## 3 Results and Discussion
### Post-Correction Performance  
The base models, as automatically determined by the AutoML framework, are mostly GBM with different parameters, with a small proportion of other models such as general linear models. Within these models, GBM models usually have larger weights, which is similar to findings in previous studies [17]. Forecast performance after post-correction with AutoML generally improved at all 13 stations compared to raw forecast. The mean RMSE of post-corrected temperature forecast at the 13 stations was 2.34 \({}^{\circ}\)C, which was about 28% smaller than that of the raw forecast (3.24 \({}^{\circ}\)C) (Figure 6a). After correction, RMSE values among stations were all at similar levels, with a standard deviation of 0.34 \({}^{\circ}\)C, compared to 1.09 \({}^{\circ}\)C for raw forecasts. The largest improvement occurred at station 51470, where the percentage improvement was around 65% in terms of RMSE, from 6.02 \({}^{\circ}\)C to 2.11 \({}^{\circ}\)C. The smallest improvement happened at station 51469, where the RMSE increased by only 1%. As for the bias of the temperature forecasts, they were generally biased high, with an overestimation of 2.06 \({}^{\circ}\)C for all stations. The largest bias occurred at station 51470, where the temperature was overestimated by 5.68 \({}^{\circ}\)C. The smallest bias occurred at station 51469, with an MAE value of 0.70 \({}^{\circ}\)C. After post-correction by the AutoML model, mean MAE values became much smaller, with a mean of 0.33 \({}^{\circ}\)C, and the overestimation at most stations was less than 1 \({}^{\circ}\)C.  
As for the relative humidity forecast, the mean RMSE of the raw forecast was 19.54% for all 13 stations, with mean ME values being around \(-16.21\%\). After post-correction, mean RMSE became smaller (11.54%), which was a 41% decrease. The greatest improvement was at station 51359, where the RMSE improved from 29.31% to 12.53%, i.e., a decrease of 57%. Similar performance was also achieved at stations 51368 and 51356, with RMSE decreasing by 56% and 53%, respectively. As for MAE values, raw WRF forecasts greatly  
Figure 5: Forecast performance along the forecast lead hours at station 51359; panels (**a**,**c**,**e**) are, respectively, referring to the R-square, RMSE, and NME values of temperature. The panels (**b**,**d**,**f**) are the metric values of relative humidity. The _x_-axis refers to forecast lead time. overestimated relative humidity at all 13 stations, with a mean ME of \(-15.41\%\). The most significant underestimation of the raw forecast occurred at station 51359, which had an ME of \(-26.3\%\). After correction, forecasts were generally unbiased, with the largest bias (\(-3.52\%\)) occurring at station 51365. It should be noted that, for both temperature and relative humidity, forecast biases were much smaller in almost all stations. This revealed that the AutoML post-correction model could substantially remove systematic errors in raw forecasts. At the stations where bias values of raw forecasts were small, such as 51463 for temperature and 51468 for relative humidity, the post-correction performance evaluated by RMSE improved less than other stations. This indicated the post-correction models are relatively weak when it comes to reducing random errors in raw forecasts. Compared with the post-correction performance of the models fitted by data from the previous month, the model fitted by data from August 2018 generally behaved better, as shown in Figure 7. As for temperature, the post-correction performance of the latter model also had a larger RMSE than the model of the previous month at most stations. Moreover, compared to three other MOS methods, our ensembled AutoML model also had a better performance, as shown in Figures 8-10. For example, RF, MLR, and moving error post-correction model, respectively, have larger RMSE values than the AutoML model, with a mean difference of 0.11, 0.07, and 0.09 \({}^{\circ}\)C for temperature and 0.55%, 0.61%, and 1.12% for relative humidity.