# A Model Output Deep Learning Method for Grid Temperature Forecasts in Tianjin Area
## 1 Introduction
The use of MOML to post-process NWP requires the extraction of hidden high-dimensional features in large amounts of historical data which required meteorological knowledge [24]. Cyclone, wind shear, etc. can all be regarded as high-dimensional features extracted through meteorological knowledge, but only professionals can extract them. Therefore, the complex pre-processing that is related to feature engineering is a deficiency of MOML. The post-processing methods that are mentioned above can be considered as machine learning algorithms. Machine learning algorithms build a mathematical model based on sample data, known as \"training data\", in order to make predictions or decisions [25]. In the post-processing, machine learning algorithms build the model based on historical data to map the relationships between forecast field and observation data. As a branch of machine learning methods, deep learning has demonstrated its remarkable capacities and massive potential in many different fields in recent years. The development of deep learning has also promoted the historical process of artificial intelligence because of its extensive application possibilities [26]. Deep learning has made breakthroughs in many fields, especially in computer vision (CV) [27], speech recognition [28], and natural language processing (NLP) [29]. The emergence of deep learning provides a new approach for several meteorological problems. In recent years, many papers have tried to use deep learning to reduce the model error and initial condition error of NWP. For example, [PERSON] et al. [30] used a multilayer feedforward neural network in order to predict Chile's degree of drought in 2018. [PERSON] et al. [31] used autoencoders to predict temperature and humidity in Nevada. [PERSON] et al. [32] implemented convolutional neural networks to the forecasts of cloud cover. [PERSON] et al. [33] trained a deep belief network in the prediction of wind power. However, the above-mentioned deep learning models that are applied to weather forecasting are gradually replaced by the new models due to slow convergence and easy over-fitting in the CV and NLP areas. In addition, these algorithms establish the mapping relationship between the forecast field and a single weather station. The idea of directly establishing a mapping from the forecast area to the observation area, which can be called as end-to-end post-processing, has not been tried. In this paper, a model output deep learning (MODL) method is proposed while using two state-of-the-art deep learning techniques, namely Three-Dimensional (3D) Fully Convolutional Neural Networks and U-Net Structure, to correct the numerical prediction. Fully convolutional neural networks, recurrent neural networks, and fully connected neural networks are common deep learning methods to solve pixel-level classification and regression problems. A recurrent neural network is difficult to converge because of the complex structure. The fully connected neural network cannot handle large input tensors due to the large number of parameter requirements. When compared with the fully connected neural network and recurrent neural network, the fully convolutional network that was proposed by [PERSON] et al. [34] has a better performance on pixel-level regression. Thus, the 3D Fully Convolutional Network (FCN) is widely used to process 3D data. [PERSON] et al. [35] used this structure to detect vehicles in the point cloud. [PERSON] et al. [36] trained a regression 3D FCN automatic segmentation of lumbar vertebrae from CT images. NWP data post-processing includes three dimensions of time and space, so 3D FCN is also suitable for NWP post-processing. [PERSON], [PERSON], and [PERSON] [37] proposed a U-net structure for biomedical image segmentation. U-net codec structure can fuse multi-scale features in the network. Multi-scale and multi-lifecycle meteorological features need to be comprehensively analyzed during the post-processing of NWP.