**5. Discussion**

ST-CORAbico is a spatiotemporal object-based bias correction method that was designed to reduce the displacement and volume systematic errors of storm events detected by SPP. In comparison to spatial object-based bias correction methods e.g., [37,38], the inclusion of the temporal component of the storm event reduced additional error effects due to timing and orientation, improving the efficiency of the bias correction.

This research incorporated a multivariate kernel distribution algorithm into ST-CORA to segment the storm event using the four dimensions of the storm event. In comparison to binary segmentation in the previous version, ST-CORA with KDE segmentation was able to delineate intense storm events by removing unreal storm configurations as a consequence of false merging and false separation of storms due to the multidimensional connected labelling component algorithm. Based on the analysis of KDE threshold delineation and the connected intensity, we found that storm events that were segmented by the 25th percentile of the distribution showed a good result for segmenting intense storms with strong connection. However, further improvement is required.

The implementation of ST-CORAbico described the individual error correction due to displacement and volume. Results in the Lower Mekong basin indicated that volume errors were the main error correction, primarily resulting from the high overestimation of GPM-IMERG. These results agreed with multiple findings regarding hourly GPM-IMERG in monsoonal areas [70,71]. Overall, volume and displacement errors effectively contributed to the reduction of bias and RMSE, demonstrating the importance of reducing both of these systematic errors in satellite correction.

We acknowledge certain limitations of the study. Firstly, the uncertainty arising from the spatial interpolation method that was used for rain gauge values was not fully addressed in this research. Volume and especially displacement corrections in ST-CORAbico can be affected by the type of interpolation methods used to represent the spatiotemporal distribution of the observed storm. A dense rain gauge network can reduce the level of uncertainty; however, it is important to evaluate the impact of the type of interpolation method on the performance of ST-CORAbico, as mentioned above. Another limitation arises from the sensitivity of *IoU* percentage to match observed and estimated storm events. Higher levels do not always correspond to similar events, which affects the bias correction. This process required an in-depth sensitivity analysis of *IoU* in order to reduce the automatic storm matching. Additional analysis is required in order to identify why there is a strong correlation between observed and predicted storms in a spatiotemporal environment. In this study, we validated the performance of ST-CORAbico by comparing its performance against two widely used probabilistic methods. However, error metrics were calculated using the observed values, as there is no independent validation dataset available. Further implementations should consider an independent dataset to validate the error correction of the ST-CORAbico method.

This study was conducted in collaboration with the SERVIR-Mekong project and the Mekong River Commissions (MRC). SERVIR-Mekong is harnessing space and geospatial technologies to help decision-makers and key civil society groups to integrate geospatial information into their decision-making, planning, and communication. The application of this methodology can be used for various scientific purposes, including flood risk and water management. More specifically, the methodology enhances the input rainfall data, which are a crucial component of flood and drought early warning systems, landslide monitoring, as well as other water-related decision support systems. Future work will include the integration of machine learning technologies for near real-time bias correction of rainfall data when field data are scarce. In this regard, machine learning models will be trained and optimised using legacy field data and deployed on a near real-time basis.

#### **6. Conclusions**

We proposed a new spatiotemporal bias correction method for storm prediction detected by satellites. The method, called Spatiotemporal Contiguous Object-based Rainfall Analysis for bias correction (ST-CORAbico), analyses the main spatiotemporal characteristics of the observed and estimated storm events to correct systematic error sources due to displacement and volume. This methodology has two main elements: storm analysis for the segmentation and classification of storm event; and bias correction for correcting error due displacement and volume. In the storm analysis, we applied the ST-CORA method with a multivariate kernel segmentation in order to identify the spatiotemporal structure of the storm event. This method was applied over the Lower Mekong basin in Thailand to correct the GPM-IMERG Early version during the monsoon seasons from 2014 to 2017. The performance of ST-CORAbico was evaluated against the Distribution Transformation and the Gamma Quantile Mapping methods based on the reduction of RMSE, bias, and correlation coefficient. The results were divided by classifying the storm events into short- and long-lived storm events while using the k-means cluster analysis method.

We classified 68 storms (56%) as short-lived storms and 52 (44%) as long-lived events. The results of both storm event types showed that ST-CORAbico reduced the RMSE and bias of GPM-IMERG. Volume correction was the major error source due to the overestimation present in GPM-IMERG. Location error was most important in the reduction of the bias. ST-CORAbico displayed a marginal impact on the spatial structure of the satellite-derived rainfall, showing the original structure of the rainfall data.

The comparison of ST-CORAbico with the Distribution Transformation and the Gamma Quantile Mapping methods showed that ST-CORAbico had the lowest RMSE as well as the lowest bias in both short and long-lived events. In terms of the correlation coefficient, ST-CORAbico and DT had a lower impact on the correlation coefficient, especially for short-lived events.

ST-CORAbico improves the accuracy of satellite-derived near real-time information on storm events. It can be used in various flood monitoring and water management applications. Our future studies will also incorporate machine learning methods and related technologies in order to correct storm events in real-time, in situations where field observation data are scarce.

**Author Contributions:** Conceptualization, M.L.-B., G.A.C. and A.P.; Data curation, M.L.-B. and S.J.; Funding acquisition, P.T., L.S., S.K. and W.C.; Investigation, M.L., G.A.C., A.P., F.C., C.M., D.P.S. and S.B.-A.; Methodology, M.L.-B.; Project administration, C.M. and P.T.; Resources, M.L.-B., S.J., P.T., L.H.S., S.K., S.B.-A. and W.C.; Supervision, G.A.C., A.P., F.C., C.M., P.T., A.M., D.P.S., W.C., R.U. and D.P.S.; Visualization, C.M., S.B.-A. and W.C.; Writing—original draft, M.L.-B., A.P., F.C. and A.M.; Writing—review and editing, M.L.-B., G.A.C., A.P., F.C., C.M., S.J., P.T., A.M., D.S., L.H.S., K.S., S.B.-A., R.U. and D.P.S. All authors have read and agreed to the published version of the manuscript.

**Funding:** This work is part of a PhD study of the first author and was partially funded by the Colombian Administrative Department of Science, Technology and Innovation (COLCIENCIAS) under Grant number 646.

**Acknowledgments:** The authors would like to acknowledge the Asian Disaster Preparedness Center, in Thailand and the USAID-NASA SERVIR-Mekong program for the support provided. We would like to thank Hannah Priestley for her meticulous style corrections. GPM-IMERG information was extracted from SERVIR-Mekong's Virtual Gauge and Stream Gauge Data service tool (VRSGS). Additionally, we would like to thanks the Hydroinformatics Institute in Thailand for the provision of hourly rain gauge data and also the space agencies responsible for the satellite data used in this research.

**Conflicts of Interest:** The authors declare no conflict of interest.
