IDWE_CHM (NRT_F)

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. Users can obtain timely precipitation estimates with the same ~0.1° resolution and methodology consistency as the historical dataset.For a comprehensive description of the project, please refer to:<br><b>An Incremental Dynamic Weighting Ensemble Framework for Long-Term and NRT Precipitation Prediction</b><br>https://figshare.com/projects/An_Incremental_Dynamic_Weighting_Ensemble_Framework_for_Long-Term_and_NRT_Precipitation_Prediction/241619<br>The IDWE_CHM dataset provides <b>four precipitation variables</b>, all derived from the ensemble framework but with slightly different modeling approaches:<b>ENS_Reg</b> – 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.<b>ENS_RegCla1</b>, <b>ENS_RegCla2</b>, <b>ENS_RegCla3</b> – Three variants of a hybrid <i>regression-plus-classification</i> approach (collectively called <b>ENS_RegCla</b>). 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_F) are temporally coordinated with those of the five datasets integrated in the fusion process, with explicit synchronization maintained for the GPM_3IMERGDF dataset (available at: https://gpm1.gesdisc.eosdis.nasa.gov/data/GPM_L3/GPM_3IMERGDF.07), which exhibits relative latency compared to other fused datasets.

IDWE_CHM数据集的近实时（near-real-time, NRT）扩展版本，2023年后持续每日更新。该NRT产品持续对新增数据应用IDWE框架，从而近实时扩展记录。用户可获取时效性强的降水估算结果，其分辨率约为0.1°，方法学与历史数据集保持一致。关于该项目的详细描述，请参考：<br><b>长期与近实时降水预测的增量动态加权集成框架</b><br>https://figshare.com/projects/An_Incremental_Dynamic_Weighting_Ensemble_Framework_for_Long-Term_and_NRT_Precipitation_Prediction/241619<br>IDWE_CHM数据集提供<b>四个降水变量</b>，均源自集成框架，但建模方法略有不同：<b>ENS_Reg</b>——纯回归融合降水估算。该产品通过回归方法对输入数据集（ERA5-Land、IMERG、GSMaP等）进行最优加权与组合生成，无需额外分类步骤，是IDWE方法的基准产品。<b>ENS_RegCla1</b>、<b>ENS_RegCla2</b>、<b>ENS_RegCla3</b>——混合<i>回归加分类</i>方法的三种变体（统称<b>ENS_RegCla</b>）。它们先通过回归融合（如ENS_Reg），再经分类步骤调整估算结果；分类环节采用增量学习（incremental learning）增强，即算法随时间从误差中学习。这三种变体可能对应增量学习的不同配置或阶段，相比纯回归融合，其捕捉降水发生与极值的能力通常更优。IDWE_CHM（NRT_F）的更新在时间上与融合过程中整合的五个数据集保持协调，尤其针对GPM_3IMERGDF数据集（可访问：https://disc.gsfc.nasa.gov/datasets/GPM_3IMERGDF_07/summary?keywords="IMERG final"）维持显式同步——该数据集相比其他融合数据集存在相对延迟。