Hybrid bias correction of daily satellite precipitation over Indonesia

This dashboard presents the findings of a hybrid bias-correction framework for daily satellite precipitation, worked through the Indonesian archipelago as a case study. Satellite rainfall from IMERG-L is corrected toward gauge observations in four stages - Linear Scaling, Empirical Quantile Mapping, a Generalized Pareto tail, and a Convolutional Neural Network refinement (together, LSEQM+DL) - then validated against an independent network of BMKG stations.

The pages let you explore what the correction achieves and where its limits lie: the daily distribution moves onto the gauge, the day-by-day timing does not, and much of that timing ceiling turns out to be a fixable calendar-window artefact rather than a fundamental limit. Every figure is computed from the same processing pipeline.

What the correction fixes - and what it structurally cannot

The corrected product moves to the gauge distribution, but the day-by-day timing does not improve: Pearson r stays near across every stage. Most of that ceiling, though, is a fixable calendar-window artefact - re-aggregating IMERG-L to the local-day window lifts r against BMKG from to (at h).

Study area & data coverage

180 BMKG stations (7 island groups) over the 0.1° IMERG footprint; hover a station for its details.

How to read this dashboard

A slide deck can carry the introduction and methods; these pages carry the findings. Move through them with the sidebar.