# RAIN-F+: The Data-Driven Precipitation Prediction Model for Integrated Weather Observations
## 5 Discussion and Future Work  
There are various types of weather observation dataset that provide different characteristics of the atmospheric state. This study is an attempt to use all available weather observation data for precipitation prediction. We generated the RAIN-F+ dataset, which is the fusion dataset from four different types of weather observation related to precipitation. We evaluated the performance of ablation studies with different combinations of a fusion dataset in order to explore the influence of the different modalities. The benchmark model is trained and validated with the radar reflectivity product as reference data because the radar observation provides the most accurate measurements for precipitation. The results showed that the RAIN-F+ dataset still has the limitation to predict rainfall for rain rate over 10 mm/h. It may be caused by the small number of rain rate pixels over 10 mm/h in the training dataset. However, with the multi-modality, there is the possibility to improve the performance comparing with the radar-only dataset, which shows significant underestimation for the rain rate over 10 mm/h. Since the RAIN-F+ dataset provides the atmospheric state variables, including temperature, humidity, wind from surface observations, and radiance with clouds and water vapor information from a geostationary satellite, multi-modal information from RAIN-F+ helps to improve the precipitation prediction performance over  
Figure 10: The scatter plots of prediction results depending on the fused data sources and the input resolution of \(32\times 32\) (upper row), \(64\times 64\) (middle row), and \(256\times 256\) (bottom row).  
time. This result suggested that data fusion for multi-modality is essential for precipitation prediction when applying data-driven approaches. The primary purpose of this study is to introduce the RAIN-F+ dataset and the benchmark model for the fusion dataset. Therefore, we only used the early fusion method for simple approaches and validated the results with only radar observations. In the future, we aim to apply various fusion methods in order to evaluate the performance improvement depending on the combination of the fusion dataset and in order to validate the benchmark model with different precipitation products for the purpose of finding the proper reference data for precipitation. In addition, we will consider integrating the model parameters and the topography information for the next version of the RAIN-F dataset and examine the effect of each parameter for prediction performance depending on the various precipitation system categories.  
All the authors made significant contributions to the work. [PERSON], [PERSON], [PERSON], and [PERSON] designed the research. [PERSON] analyzed the results. In terms of methodology, [PERSON], [PERSON], and [PERSON] performed the experiments. [PERSON] wrote the paper. [PERSON], [PERSON], and [PERSON] provided suggestions for the preparation and revision of the paper. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The RAIN-F version1 is available on the KISTI DATAON website ([[https://dataon.kisti.re.kr/](https://dataon.kisti.re.kr/)]([https://dataon.kisti.re.kr/](https://dataon.kisti.re.kr/)), accessed on 7 September 2021), and RAIN-F+ is available upon request from the corresponding author.  
This study is supported by the National Supercomputing Center with supercomputing resources including technical support (KSC-2020-CRE-0276).  
The authors declare no conflicts of interest.