# A Model Output Deep Learning Method for Grid Temperature Forecasts in Tianjin Area
## 1 Introduction  
In recent years, most of the meteorological forecasting methods are based on numerical weather prediction (NWP). NWP uses the observations to estimate future atmospheric behavior that is based on the current state and mathematical and physical principles [1]. Since the proposal of numerical forecasting models in the early 20 th century, numerical forecasting technology has made significant progress with the rapid development of modeling technology [2; 3], observation technology [4; 5], and computer technology [6; 7; 8]. Numerical weather prediction has become a crucial part of meteorological forecasting technology. However, NWP has some inevitable errors, including initial condition error and model error [9]. The quality of the observations, quality of the observational network, and the handling of observationand background information by the data assimilation system (DAS) are different types of initial condition errors which can be estimated through a variety of means. [PERSON] and [PERSON] [10] pointed out that these uncertainties may be related to the grid process. [PERSON] et al. [11] proposed that some uncertainties may be related to the physical and dynamic processes that are missed or not yet discovered by the numerical model. Ensemble forecasting, a universal numerical forecast technique, is widely applied to operational forecasting in order to recompense for the shortcomings of the NWP physical model and estimate the uncertainty of the forecast. The European Centre for Medium-Range Weather Forecasts (ECMWF) and the National Centers for Environmental Prediction (NCEP) have extensively tested such methods and put them into the operations [12; 13]. While the growth of initial condition error can be estimated, the growth of model error is more difficult to determine. Therefore, forecasters are still required to interpret and revise the numerical forecasting in combination with their meteorological knowledge and forecasting experience for the forecast area in order to improve the weather forecast's accuracy before the weather forecast is released [14], which is called weather consultation. As weather forecasting becomes increasingly useful as a guide in different fields, more types of observation data and greater spatiotemporal resolution forecasts are needed. Continuously upgraded observation equipment provides a large amount of historical data to support NWP models. However, the way of manual weather consultation has become a hindrance due to the slow speed and heavy workload. The emergence of intelligent post-processing algorithms provides the possibility to meet the needs of weather forecasting. Hence, suitable post-processing algorithms for weather consultation are needed to help the manual process of weather consultation [15; 16; 17]. Typical numerical forecast post-processing methods include the frequency matching method, Model Output Statistics (MOS) [18], Anomaly Numerical-correction with Observations (ANO) [19], Bayesian Model Averaging (BMA) [20], the Kalman filter [21; 22], and Model Output Machine Learning (MOML) [23]. However, the success of MOS is based on a large amount of accurate historical observation data. Each update of numerical forecasting models will invalidate the previous historical data. The continuous update of numerical forecasting models prevents MOS from collecting a large amount of historical data. The failure to analyze the potential high-dimensional features in the observation data is also a limitation of MOS. The disadvantage of ANO is that they are not likely to predict sudden changes in the forecast error caused by rapid transitions from one weather regime to another though the ANO does not need too much data and it has a lower computational complexity. The assumption of prior probability is the limitation of BMA. Different prior probabilities generally have a greater impact on the results of the BMA method. BMA sometimes returns poor forecast results due to the bad settings of the prior probabilities.