# RAIN-F+: The Data-Driven Precipitation Prediction Model for Integrated Weather Observations  
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690 & 0.024 & 0.047 \\ Ra+Im & **0.907** & **0.627** & 0.660 & 0.742 & 0.699 & 0.709 & **0.534** & **0.609** & 0.538 & **0.131** & **0.211** \\ Ra+Sf & 0.930 & 0.617 & 0.649 & 0.757 & 0.699 & 0.747 & 0.468 & 0.576 & 0.687 & 0.011 & 0.021 \\ Ra+Hi & **0.907** & 0.622 & 0.665 & 0.746 & 0.703 & 0.733 & 0.502 & 0.596 & **0.701** & 0.054 & 0.100
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an input resolution of 64 \(\times\) 64 showed slightly better performance for F1-scores of rain rate over 5 mm/h and the prediction results after three hours. For the comparisons depending on the rain rate thresholds, in Tables 1-3, the prediction results with the rain rate greater than 0.1 mm/h are not significantly different depending on the dataset for all input resolutions. However, the scores from the multi-modal dataset for the rain rate greater than 5.0 mm/h showed better performance compared to the results using only the radar dataset. In addition, the maximum scores from the fusion dataset, depending on the rain rate thresholds, have similar values regardless of resolution. It suggests that no trend explains which combination of a fusion dataset has a significant benefit. Therefore, the question of what to fuse is a matter when applying multi-modal information in the training process. Moreover, we confirmed that the recall scores dropped notably, while precision scores slightly decreased as the greater threshold is used. This means that the false negative increased much more than the false positive. A false negative (positive) is the opposite error where the prediction incorrectly fails to indicate the presence (absence) of rainfall over the threshold. Thus, the rainfall regions in the prediction results are not correctly detected when the rain becomes heavier. This trend can also be found in Figure 8, which shows the three examples of predicted rain maps depending on the fusion dataset and the radar rain map calculated from observed radar reflectivity for three different resolutions. For the references, Figure 9 represents the rain maps from the RAIN-F+ dataset at the same precipitation case with Figure 8. It is confirmed that there are differences among reference rain maps because the resolution and characteristics are different. The Radar and IMERG observations are considered instantaneous measurements. However, the IMERG data are from the GPM mission with the low-Earth orbit satellites that travel and take approximately 90 min to circle the entire Earth in order to measure global precipitation, while radar measures the same region at every observation time. Therefore, the observation times over the same region from radar and the IMERG can be different. Moreover, the rain rate from surface observations is a cumulative value from the past one hour. Among these reference rain observations, this study trained the supervised model with the radar observation as ground truth because the radar observation provides the 2D rain map with the highest resolution. Figure 8 showed that the predicted rain map does not represent the accurate location of heavy rainfall shaded in red color and detailed features of the precipitation system. Compared with the radar observations, the heavy rainfall regions are predicted over the continuous area with blurred features. Blurring is a well-known problem in the image prediction task due to the average process in the loss function. In addition, the resolutions of surface observations, the IMERG, and Himawari products in the RAIN-F+  
Figure 7: The F1-scores with the rain rate threshold of 0.1 mm/h depending on the fused dataset and the input resolution for the prediction time (R: Radar, I: IMERG; \(\lx@sectionsign\): Surface observations; H: Himawari). dataset are lower than radar observations. It may cause additional blurred features of prediction in this study.