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Updated: Jan 7, 2026
06:10
Using Generative Art to Convey Past and Future Climate Transitions
Published on: March 31, 2023
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Fusion of multi-source precipitation records via coordinate-based generative models.
Sencan Sun1, Congyi Nai2, Baoxiang Pan3
1Ministry of Education Key Laboratory for Earth System Modeling, Department of Earth System Science, Tsinghua University, Beijing, China.
Nature Communications
|December 29, 2025
Summary
A new framework called PRIMER (Precipitation Records Infinite MERging) effectively combines sparse gauge data, satellite observations, and model outputs. This approach improves precipitation estimates and corrects biases in climate data, enhancing prediction accuracy.
Area of Science:
- Climate science
- Data fusion
- Machine learning
Background:
- Precipitation observation and prediction are challenging due to data limitations.
- Gauges are accurate but sparse; satellites offer coverage with uncertainties; models are consistent but biased.
Purpose of the Study:
- To introduce PRIMER, a novel framework for fusing multi-source precipitation data.
- To enhance the accuracy and reliability of precipitation datasets.
Main Methods:
- PRIMER utilizes a coordinate-based diffusion model for data fusion.
- A two-stage training process learns large-scale patterns and refines with gauge data.
- Posterior sampling integrates fused records for analysis.
Main Results:
- PRIMER significantly reduces errors in existing precipitation datasets at gauge sites.
- The framework successfully downscales reanalysis and future climate scenario data.
- It generalizes to correct operational forecast biases without retraining.
Conclusions:
- PRIMER transforms imperfect, multi-source data into a strength for improved precipitation analysis.
- This data fusion approach offers a powerful tool for climate research and prediction.
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