# A Model Output Deep Learning Method for Grid Temperature Forecasts in Tianjin Area
## 4 Results and Discussion
### Comparison of MODL, MOML, MOS and EC Forecast  
In this subsection, a comparison in the test dataset of the ECMWF model (EC), univariate linear running training period MOS (MOS), MOML, and MODL is presented. The results of MOML with multiple linear regression (MOML-LR), the results of MOML with random forest (MOML-RF), the results of MODL based 3D FCNN (MODL-PLAIN), and the results of MODL based U-structure (MODL-U) are reported. The more obvious the statistical characteristics of the model data, the better the forecast results. The forecast \(T_{RMSE}\) is negatively correlated with the accuracy. The lower \(T_{RMSE}\) means higher forecast accuracy. The total number of samples in the test set is \(1032\times 0.2\approx 206\). Each test sample will predict a total of 53 steps in the next 10 days. Each time step has \(18\times 14\) grid points, and each grid point is regarded as a forecast result. First of all, calculate the \(T_{RMSE}\) and \(T_{acc}\) of all the grid data in all time steps of all test samples. The \(T_{RMSE},T_{acc}\) that are shown in Table 3 are the mean value of all forecast grid evaluations of the test set data. It can be seen that using EC data to predict temperature directly has a massive error. All of the post-processing methods have the ability to revise the 2 m air temperature  
Figure 9: Eight-fold cross-validation of experimental data. Training is the process of training the model using the selected hyperparameter in the training set. Evaluating is the process of saving the optimal model based on the validation set’s score. Testing is the process of evaluating the hyperparameters by calculating each folding model’s scores on the test set. data of the ECMWF model quite fit in the sense of the annual average. MOS can slightly reduce the temperature prediction error. Both MOML models can better improve numerical prediction indicators. The post-processing effect of MODL-PLAIN and MODL-U proposed in this paper is better than other models. MODL-U can reduce the EC forecast error by nearly half. The lead time between the forecast step and the forecast time will affect the final effect of the weather consultation. Therefore, we separately calculate the \(T_{RMSE}\) of all test samples at different time steps and visualize them in Figure 10. The horizontal axis is the forecast lead time, and the vertical axis is \(T_{RMSE}\). A test sample has 53 grid predictions. Figure 10 shows daily \(T_{RMSE}\). The time interval for grid forecasting from 0-2 days (left side of the dotted line in the figure) to the reporting time is 3 h, and the time interval of 3-10 days (right side of the dotted line in the figure) is 6 h. The EC forecast error increases nonlinearly with the increase of forecast lead time. The forecasting effect of the first three days is obviously better than 3-10 days. The MOS algorithm can slightly reduce the forecast error of the first three days and greatly reduce the forecast error of 3-10 days. However, generally, in weather consultation, the forecast results of the first three days are more important. Both MOML and MODL algorithms can greatly reduce the daily forecast errors. The MOML algorithm can limit the daily forecast error to about 2 \({}^{\circ}\)C. The MOML algorithm based on random forest is slightly better than the MOML algorithm that is based on multiple linear regression. Besides, the error curve of MOML does not increase significantly with the increase in forecast lead time. The MODL-PLAIN algorithm's improvement effect in the first three days is superior to the MOML algorithm, but the improvement effect of 3-10 days is close to that of MOML-RF. The daily forecast effect of the MODL-U algorithm is the best among all algorithms. Nevertheless, the forecasting time of 72-96 h and 216-240 h is not as good as MOML-RF. These two steps are the left and right boundaries in the time dimension of the 6-h interval model. Therefore, a better algorithm to solve the boundary problem may be needed to improve this result.