IDWE_CHM (NRT_L)

A near-real-time (NRT) extension of the IDWE_CHM dataset with ongoing daily updates beyond 2023. This NRT product continues to apply the IDWE framework on incoming data, thereby extending the record in near real-time.
For a comprehensive description of the project, please refer to:
An Incremental Dynamic Weighting Ensemble Framework for Long-Term and NRT Precipitation Prediction
https://figshare.com/projects/An_Incremental_Dynamic_Weighting_Ensemble_Framework_for_Long-Term_and_NRT_Precipitation_Prediction/241619

The IDWE_CHM dataset provides four precipitation variables, all derived from the ensemble framework but with slightly different modeling approaches:
ENS_Reg – A purely regression-based merged precipitation estimate. This product is generated by optimally weighting and combining the input datasets (ERA5-Land, IMERG, GSMaP, etc.) using regression, without additional classification. It serves as a baseline for the IDWE approach.ENS_RegCla1, ENS_RegCla2, ENS_RegCla3 – Three variants of a hybrid regression-plus-classification approach (collectively called ENS_RegCla). These are produced by first applying the regression merging (as in ENS_Reg) and then using a classification step to adjust the estimates. The classification is enhanced with incremental learning, meaning the algorithm learns from errors over time. These three variants may correspond to different configurations or epochs of incremental learning, and they generally show improved skill in capturing precipitation occurrence and extremes compared to a regression-only merge.The updates of IDWE_CHM (NRT_L) are temporally coordinated with those of the five datasets integrated in the fusion process, with explicit synchronization maintained for the ERA5-Land dataset (available at: https://cds.climate.copernicus.eu/datasets/reanalysis-era5-land?tab=overview), which exhibits ~5 days latency compared to other fused datasets.