###### Abstract  
Human beings attempt to accurately predict the weather based on their knowledge of climate. The Norwegian Meteorological Institute is responsible for climate-related matters in Norway, and among its contributions is the numerical weather forecast, which is presented in a 2.5 km grid. To conduct a post-processing process that improves the resolution of the forecast and reduces its error, the Institute has developed the GRIDPP tool, which reduces the resolution to 1 km and introduces a correction based on altitude and meteorological station measurements. The present work aims to improve the current post-processing approach of the air temperature parameter by employing neural networks, using meteorological station measurements. Two neural network architectures developed and tested: a multilayer perceptron and a convolutional neural network. Both architectures are able to achieve a smaller error than the original product. These results open doors for the Institute to plan for the practical implementation of this solution on its product for specific scenarios where the traditional numerical methods historically produce large errors. Among the test samples where the GRIDPP error is higher than 3 K, the proposed solution achieves a smaller error in 74.8% of these samples. neural network; post-processing; weather; meteorological data; air temperature +
Footnote †: journal:  
variation was much greater than that observed, but this effort was useful to identify several issues that would be faced on the path toward accurate weather forecasting. [PERSON]'s experiment showed that the used data were not sufficient to represent the actual atmosphere's state. It was possible to perceive that the process required a large amount of calculations and that the calculations should be fast, and minor errors at the beginning of the process could develop into huge errors in the final forecasts. Only in 1950, in the United States of America, scientists made the first weather forecast for one day ahead using a computer--more precisely, the famous Electronic Numerical Integrator and Computer, better known as ENIAC. Today, it is possible to divide weather prediction into three main steps: analysis, forecast and post-processing. * The analysis step is necessary because meteorological observations are not regular, and mathematical models require that the data provided are uniform--that is, with regular horizontal spacing over the globe--so mathematical and computational procedures are used so that the data have the necessary format. * In the forecast stage, the goal is to use the numerical models to solve the equations that describe the behavior of the atmosphere. This step is often solved using a grid point form. To minimize the effects of atmospheric chaos and considering that atmospheric measurements are not fully accurate, several simulations are performed for the same periods, with small changes in the initial conditions of the atmosphere. Finally, the average of the results is considered. * The post-processing, which is the focus of this work, consists in using the output of the numerical models either for study or for assembling meteorological bulletins that will be used by the final user. Post-processing is a common step that is taken in several fields, and meteorology is not an exception. [PERSON] [3] suggests that statistical post-processing techniques are nowadays key components of the forecasting suites in many National Meteorological Services, for most of them with the objective of correcting the impact of different types of errors on the forecasts. The Norwegian Meteorological Institute (MET) is the public institution in Norway responsible for weather prediction, monitoring the climate and conducting meteorological research since 1866 [4]. Because of this, the MET is in charge of providing the NWP in Norway. Therefore, the MET produces the MetCoOp Ensemble Prediction System (MEPS, which covers Scandinavia and Finland, and the AROME-Artic, which covers the Svalbard area [5]).