Ye-Cheng Zhang

Achieving state-of-the-art satellite-to-radar precipitation retrieval while slashing computational costs, WaveOp-LiteFM redefines efficiency in meteorological modeling.

Query-conditioned forecasting can reduce temperature prediction bias by 17% while enabling flexible lead times and resolutions.

Language models exhibit a surprising bias towards cities with expansive infrastructure and rapid growth, revealing their implicit urban assumptions.

Integrating dynamic meteorological semantics with a self-evolving retrieval framework boosts precipitation estimation accuracy in radar-sparse regions.