## 5 Conclusions  
This work proposed a neural network solution for improving the post-processing of air temperature forecasts. Results showed that it is possible to reduce the forecast error, paving the way for more accurate meteorological products. In addition, it allows the forecast estimation for any area of interest, since the proposed model uses latitude and longitude coordinates as inputs. This has the potential of reducing the computational effort for calculating the forecast for a point outside the original grid, since traditional numerical models use static grids. This should be investigated in future work.  
The MLP model achieved an RMSE of 1.786 K, against 1.886 K for the benchmark, which is a tool developed by the Norwegian Meteorological Institute that uses many more weather stations than the proposed solution in this work. In addition, it was able to achieve a smaller error in 74.8% of the samples where the error of the GRIDPP product was greater than 3 K. In these cases, the reduction in the RMSE was of approximately 0.5 K. In terms of air temperature forecast, 0.5 K is a significant error reduction. This opens the way for the practical implementation of the proposed solution in combination with the current GRIDPP product of the Norwegian Meteorological Institute.  
As future work, it is desirable to use more air temperature observations, such as public and private sensor data providers, homemade weather stations, sensors embedded in cars and IoT devices that could make the measurements available in the cloud. In addition, other architectures of CNN and MLP models should be evaluated, as well as new architectures, such as graph convolutional neural networks. These do not rely on the input being a regular matrix and could take advantage of neighborhood information, as the air temperature likely exhibits spatial behavior.  
Conceptualization, F.A.A.-A. and G.A.; methodology, F.A.A.-A. and G.A.; software, G.A.; validation, F.A.A.-A. and G.A.; formal analysis, F.A.A.-A. and G.A.; investigation, F.A.A.-A. and G.A.; resources, F.A.A.-A. and G.A.; data curation, F.A.A.-A. and G.A.; writing--original draft preparation, G.A.; writing--review and editing, F.A.A.-A.; visualization, G.A.; supervision, F.A.A.-A.; project administration, F.A.A.-A.; funding acquisition, F.A.A.-A. All authors have read and agreed to the published version of the manuscript.  
Funding for this work was provided by the Research Council of Norway (AutonoWeather project, grant number 301575).  
Not applicable.  
The final dataset resulting from the steps described here in Section 2.1 is available at [[https://doi.org/10.34740/kaggle/dsv/3635866](https://doi.org/10.34740/kaggle/dsv/3635866)]([https://doi.org/10.34740/kaggle/dsv/3635866](https://doi.org/10.34740/kaggle/dsv/3635866)) (accessed on 14 July 2022).  
We would like to acknowledge the support from the Norwegian Meteorological Institute.  
The authors declare no conflict of interest.  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} NN & Neural Network \\ MLP & Multilayer Perceptron \\ CNN & Convolutional Neural Network \\ MSE & Mean Square Error \\ RMSE & Root Mean Square Error \\ CSV & Comma Separated Value \\ ETL & Extract-Transform-Load \\ MET & Norwegian Meteorological Institute \\ \end{tabular}