Recently, precipitation nowcasting has gained significant attention. For instance, the demand for precise precipitation nowcasting is significantly increasing in South Korea since the economic damage has been severe in recent days because of frequent and unexpected heavy rainfall. In this paper, we propose a U-Net based deep learning model that predicts from a numerical model and then corrects the data using the U-Net based deep learning model so that it can improve the accuracy of the final prediction. We use two data sets: reanalysis data and LDAPS(Local Data Assimilation and Prediction System) prediction data. Both data sets are grid-based data that covers the whole South Korea region. We first experiment with reanalysis data to identify that our U-Net model can find atmospheric dynamics patterns, even if it is not image data. Next, we use LDAPS prediction data and apply it to the U-Net model. Because LDAPS prediction data is also a prediction, we essentially conduct correcting task for this data. To this aim, a learnable layer is added at the front of the U-Net model and concatenated with the input batch to learn location-specific information. The experiment shows that the U-Net based model can find patterns using reanalysis data. Further, it has the potential to improve the accuracy of LDAPS prediction data. We also find that the learnable layer enhances test accuracy.