# 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
By comparing Table 3 and Figure 10, it is found that the daily \(T_{RMSE}\) in MOML algorithm and MODL algorithm in Figure 10 looks smaller than the total \(T_{RMSE}\) in Table 3. It indicates that there are a few forecast results with large errors, and those points with large forecast errors are often regarded as abnormal points that affect the results of weather consultation. Therefore, we use the box chart in order to visualize the error distribution of the above algorithm presented in Figure 11. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Methods** & **EC** & **MOS** & **MOML-LR** & **MOML-RF** & **MODL-U** & **MODL-PLAIN** \\ \hline RMSE & 4.0667 & 3.0154 & 2.5388 & 2.2206 & **1.9542** & 2.0147 \\ Acc & 69.5 & 70.21 & 74.53 & 77.71 & **80.65** & 79.51 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Model performance on the test set. Figure 10: Forecast error’s daily root-mean-square error (RMSE). The smaller the rectangular box area, the more concentrated the distribution of error data. It can reflect the robustness of the algorithm to a certain extent. The robustness the of EC forecast is poor. The error distribution of MODL is more concentrated and concentrated at a lower level. Therefore, MODL forecast results are more reliable and stable in operational applications. The points above the upper edge in the box plot are considered to be abnormal points in the data distribution. EC forecast and MOS forecast have many abnormal points with large errors. EC forecast, MOS forecast, and MOML-LR forecast have some abnormal points with an error greater than 10 \({}^{\circ}\)C. These anomalies will interfere with weather consultation. The extreme value of the abnormal point of MOML-RF does not exceed 10 \({}^{\circ}\)C, but its effect in correcting extreme errors is not as good as the MODL algorithm. The forecast deviation of models in the test dataset is expanded into a deviation comparison matrix in both dimensions of the date and the forecast time which is plotted in the form of a heat map in Figure 12 to compare the post-processing effects of different models for different dates and different time steps. For an element of the contrast matrix with the coordinates \((a,b)\), the shade of its grid color means the \(T_{RMSE}\) of the forecast step \(b\) on the test date \(a\). The darker the element is, the greater the error is, and the worse the prediction effect is. On the contrary, the lighter the part is, the prediction result is closer to the observation result. According to Figure 12, the better the EC data, the better the forecast results. It means that the improved models can only post-process based on the deviation of the learned observation data from the EC data. The seasonal impact of EC temperature forecast results is not enormous. Accordingly, the seasonality of the post-processing algorithm is not obvious. Of the six error heat maps, the two heat maps for MODL forecasts have the lightest color, which shows that the MODL forecast has the best effect. Finally, the experiment randomly selected the forecast data at six forecast moments from test dataset for visualization in Figure 13 in order to show the improvement effect of MODL more clearly. Because of space limitations, in addition to the observed temperature field, TIGGE forecast temperature field, and MOS forecast temperature field, we have selected the MOML-RF and MODL-U forecasted temperature fields that perform better in MOML and MODL to visualize them. Through horizontal comparison, it can be seen that most of the MODL-U forecast temperature field is closer to the observed temperature field. This shows that the post-processing effect of MODL-U is better. However, there is  
Figure 11: Error box plot. Each box plot corresponds to the distribution of a model forecast error. The ordinate of the green line in the box is the median of the error distribution, and the ordinate of the green triangle is the mean of the error distribution.