# A Model Output Deep Learning Method for Grid Temperature Forecasts in Tianjin Area
## 4 Results and Discussion
### Comparison of MODL-PLAIN and MODL-U  
This subsection will compare MODL-U and MODL-PLAIN. The various statistical performance of MODL-U is better than MODL-PLAIN due to the integration of the features of different time and space scales. MODL-U has a smaller \(T_{RMSE}\) and a larger \(T_{Acc}\) in Table 3. The error of MODL-U at different forecast lead times are also smaller than MODL-PLAIN, which can be seen in Figure 10. The smaller rectangular area of MODL-U in Figure 11 shows its higher robustness. The shallower comparison matrix in Figure 12 shows that MODL-U is better than MODL-PLAIN for any forecast lead time and any forecast date. It can be seen that the multi-scale MODL model has better forecasting capabilities.  
Figure 12: Comparison matrix of RMSE for different post-processing methods. The total number of samples in the test set is \(1032\times 0.2\approx 206\), including 51 dates in 12 months throughout 2017. A forecast will be made for a test date. Each time it will forecast the temperature at 53 steps in the future. Therefore, the horizontal axis of the heat map is 51 test times arranged in date order in 2017. The vertical axis is the 53 steps forecast for each forecast date. The pixel value of the heat map indicates the error between the forecast temperature and the observed temperature. The deviation evaluation index is the root mean square error (RMSE).  
However, for deep learning models, the demand for computing resources is also an important measurement. This directly affects whether the MODL algorithm can be applied to the weather consultation of the meteorological department. The indexes that affect the application ability of model engineering include model size, memory requirements, floating point operations (FLOPs), and parameter-number. Models with large storage space and high memory requirements cannot be run on computers with low configuration. Models with massive FLOPs require more computing power. Table 4 compares the above indicators of MODL-U and MODL-PLAIN.  
Figure 13: 2 m air temperature forecast visualization comparison figure. The figure visualizes the observation results of 2 m temperature and the forecast results of different forecast models at six different time steps. Each row of pictures corresponds to a temperature field at a time step. From left to right is the observation field, TIGGE forecast field, MOS forecast field, MOML-RF forecast field and MODL-U forecast field. Each dot on the subgraph represents the temperature of a valid forecast grid point. The shade of the color indicates the level of temperature.  
The storage space is the space occupied by the trained model on the hard disk. MODL-PLAIN and MODL-U take up little storage space and have low demand for the machine. The memory requirements in the table are the memory required for testing when batch size = 128. When the computer uses the MODL model for post-processing, it will read the model parameters from the hard disk to the internal memory. In addition, the matrix calculation also requires memory when the model is running. Too many requests for memory will slow down the speed of the computer. MODL-U requires a computer with at least 8 GB memory and MOML-PLAIN only needs 4 GB. FLOPs is a measure of computer performance, useful in fields of scientific computations that require floating-point calculations. FLOPs can reflect the speed of the calculation. MODL-U has twice as much floating-point calculation as MODL-PLAIN. The parameter-number is the total amount of \(W\) and \(b\) in the formula (4). MODL-PLAIN has less than half of MODL-U's parameter-numbers. During post-processing, MODL-PLAIN will run much faster than MODL-U, because of the parameter-number and FLOPs. Therefore, MODL-PLAIN is a lightweight model. In actual operations, if better forecast results are needed, the MODL-U model can be used for the post-processing of NWP. MODL-PLAIN can be used as an alternative model when the computer configuration is not sufficient to run MODL-U, or the processing results need to be obtained quickly.