# A Model Output Deep Learning Method for Grid Temperature Forecasts in Tianjin Area
## 3 Methods
### MODL Method
#### 3D Fully Convolutional Neural Network Model (MODL-PLAIN)
Similarly, during weather consultation, meteorologists also analyze the multi-scale weather system extracted from different element fields in order to determine the effect of different elements on temperature forecasting. Generally, the temperature of a certain area is affected by weather systems of different sizes at the same time. For example, the damp-heat summer in South China is affected by both the large-scale system such as subtropical high and the small-scale system such as wind shear. In addition, due to different life cycles of different weather systems, different life stages (generation, evolution, extinction) of the same weather system have different impacts on weather elements at the same location in the time dimension. Cold fronts and cyclones of different sizes may cause a decrease in temperature. Cold vortex with long life cycle will also cause temperature drop. Therefore, it is necessary to consider the scale of time and space when designing the model. However, the hidden layer features of MODL-PLAIN have the same scale. Weather-like systems of different sizes and different life cycles cannot be extracted. Therefore, this paper proposes a 3D Fully Convolutional Neural Network Model that is based on U-Net Structure (MODL-U). The U-net structure is a multi-scale feature fusion method commonly used in the field of computer vision. It uses  
Figure 6: MODL-PLAIN spatial information integration scope. Different administrative areas in each map are painted in different colors according to the observed temperature. convolution kernels of the same size to perform a convolution operation on the same feature tensor scaled to different sizes. It is a multi-scale feature fusion parallel branch structure without adding redundant network parameters. Figure 7 shows the MODL-U structure used in this experiment. The feature extraction of the MODL-U contains five sets of convolution modules and interspersed with two sets of up and downsampling operations in the middle of the convolution modules. Spatial downsampling is to increase the receptive field of the convolution kernel in the spatial range in order to capture more extensive weather-like system characteristics. The temporal downsampling is used to increase the time's receptive field of the convolution kernel to capture weather-like system information with a longer life cycle. The model can have the ability to process multi-scale information at the same time after upsampling small-scale features and splicing with large-scale features, and then performing convolution operations. Therefore, through the scale transformation of the space-time dimension, the characteristics of weather-like systems of different scales are integrated together. The middle layer output of MODL-U has three different shape feature maps. Among them, the feature maps with shape \(53\times 18\times 14\) are weather-like system features with smaller scales and shorter life cycles. The feature maps with shape \(53\times 7\times 7\) are weather-like system features with large or medium scales and shorter life cycles. The feature maps with shape \(26\times 7\times 7\) are weather-like system features with large or medium scales and longer life cycles. The weather-like system information that is extracted by the convolution kernel is not similar in structure to the weather system in meteorology but has similarities in function. Additionally, it is associated with the division of the training data set, so that multi-fold cross-validation must be used to ensure the effectiveness of the model. Figure 7: Three-Dimensional (3D) Fully Convolutional Neural Network based on U-Net Structure. The 7-channel tensor on the left is the input of the model, which is the seven weather elements of the numerical forecast model. The single-channel tensor on the right is the output of the model, which is the forecast result of MODL-PLAIN. The three green tensors in the middle are the output feature tensors of the middle convolutional layer.