Kavli Affiliate: Li Xin Li
| First 5 Authors: Sencan Sun, Congyi Nai, Baoxiang Pan, Wentao Li, Xin Li
| Summary:
Precipitation remains one of the most challenging climate variables to
observe and predict accurately. Existing datasets face intricate trade-offs:
gauge observations are relatively trustworthy but sparse, satellites provide
global coverage with retrieval uncertainties, and numerical models offer
physical consistency but are biased and computationally intensive. Here we
introduce PRIMER (Precipitation Record Infinite MERging), a deep generative
framework that fuses these complementary sources to produce accurate,
high-resolution, full-coverage precipitation estimates. PRIMER employs a
coordinate-based diffusion model that learns from arbitrary spatial locations
and associated precipitation values, enabling seamless integration of gridded
data and irregular gauge observations. Through two-stage training–first
learning large-scale patterns, then refining with accurate gauge
measurements–PRIMER captures both large-scale climatology and local precision.
Once trained, it can downscale forecasts, interpolate sparse observations, and
correct systematic biases within a principled Bayesian framework. Using gauge
observations as ground truth, PRIMER effectively corrects biases in existing
datasets, yielding statistically significant error reductions at most stations
and furthermore enhancing the spatial coherence of precipitation fields.
Crucially, it generalizes without retraining, correcting biases in operational
forecasts it has never seen. This demonstrates how generative AI can transform
Earth system science by combining imperfect data, providing a scalable solution
for global precipitation monitoring and prediction.
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