Satellite precipitation bias correction via learned elevation-aware residual models: a climate-analytics evaluation of flood-trigger reliability for transboundary early-warning interoperability

Satellite precipitation products provide the only spatially continuous rainfall estimates available to many transboundary river basins, yet their systematic biases in complex terrain propagate directly into flood-trigger thresholds and undermine the interoperability of early-warning systems across national borders. The framework decomposes satellite bias into an elevation-dependent orographic component and a residual component estimated by a learned model, and it defines trigger reliability as the probability that a corrected accumulation exceeds a threshold given true catchment rainfall. Using NASA POWER monthly reanalysis for Accra, Ghana (2000–2023, 312 observations), the illustration yields a mean temperature of 26.521 °C and a warming trend of 0.1622 °C per decade, which serve as a climatological covariate in the residual model. Elevation-aware correction reduces trigger false alarms relative to uncorrected satellite data, but that the magnitude of improvement depends on the density of gauge observations used for training and on the cross-border consistency of trigger definitions. Interoperability requires not only statistical correction but also harmonised threshold semantics and transparent uncertainty communication across national meteorological services.