Improving Earth System Model Precipitation Simulation through Conditional Diffusion Models for Downscaling and Bias Correction

Core Concepts

A novel machine learning framework based on conditional diffusion models can effectively downscale and bias-correct Earth system model precipitation fields, preserving large-scale patterns while correcting small-scale biases.

Abstract

The content presents a novel machine learning framework for simultaneously downscaling and bias-correcting Earth system model (ESM) precipitation fields. The key aspects are: The framework maps observational (OBS) and ESM data to a shared embedding space where they are unbiased towards each other in terms of statistical distributions and spatial patterns. A conditional diffusion model is trained to approximate the inverse of the mapping from the embedding space to the high-resolution observational data. This allows applying the trained diffusion model to ESM data in the embedding space to obtain downscaled and bias-corrected precipitation fields. The method preserves the large-scale spatial patterns of the ESM while correcting small-scale biases, outperforming traditional bias correction and downscaling approaches, especially for extreme precipitation events. The framework is flexible and can be applied to any ESM, as the training is independent of the specific ESM used.

Stats

The mean absolute bias of the diffusion model-corrected GFDL precipitation fields compared to ERA5 is 0.29 mm/d, a significant improvement over the original GFDL bias of 0.69 mm/d. The diffusion model-corrected fields closely match the spatial patterns and statistical distributions of the high-resolution ERA5 observations.

Quotes

"Our diffusion model hence accurately preserves the large-scale precipitation content, while successfully correcting small-scale structure of the precipitation fields, as well as statistical biases in terms of histograms and latitude / longitude profiles." "Our framework demonstrates comparable skill to the QM-based benchmark in correcting the latitude and longitude profiles, for which QM is near optimal by construction. Comparing the histograms shows a similar performance of our diffusion model compared to the benchmark while even slightly outperforming it for extreme values."

Key Insights Distilled From

Conditional diffusion models for downscaling & bias correction of Earth system model precipitation

by Michael Aich... at arxiv.org 04-24-2024

https://arxiv.org/pdf/2404.14416.pdf

Deeper Inquiries

How can the proposed framework be extended to correct and downscale other climate variables beyond precipitation, such as temperature or wind fields?

The proposed framework can be extended to correct and downscale other climate variables by following a similar approach to what was done for precipitation. The key steps involved in extending the framework to other variables include: Data Pre-processing: Pre-process the target variable data (e.g., temperature or wind fields) and the Earth System Model (ESM) data in a similar manner as done for precipitation. This includes standardizing the data, transforming it to a common range, and ensuring consistency in units. Embedding Framework: Construct transformations f and g that map the observational and ESM data to a shared embedding space. Ensure that the embedded datasets are unbiased towards each other and align in terms of statistical properties and spatial patterns. Network Architecture and Training: Utilize a Denoising Diffusion Probabilistic Model (DDPM) architecture conditioned on the low-resolution images of the target variable. Train the diffusion model to approximate the inverse transformation f−1 and correct biases while preserving large-scale patterns. Noising Scale Determination: Determine the spatial scale for adding noise in the embedding transformations based on the spectral characteristics of the target variable and the ESM data. Evaluation and Validation: Evaluate the performance of the diffusion model in bias correction and downscaling for the specific climate variable. Compare the results with benchmark methods to assess the effectiveness of the framework. By following these steps and adapting the framework to the characteristics of the specific climate variable, the proposed approach can be extended to correct and downscale other variables beyond precipitation.

What are the limitations of the conditional diffusion model approach, and how could it be further improved to handle more complex spatial-temporal dependencies in climate data?

Limitations of the Conditional Diffusion Model Approach: Computational Complexity: Training a conditional diffusion model can be computationally intensive, especially for high-resolution climate data and complex spatial-temporal dependencies. Limited Training Data: The availability of paired observational and ESM data for training the model may be limited, leading to challenges in capturing the full range of variability in climate patterns. Generalization: The model's ability to generalize to unseen data and handle extreme events or rare phenomena may be limited by the training data distribution. Improvements for Handling Complex Dependencies: Incorporating Temporal Information: Enhance the model to incorporate temporal dependencies by considering time-series data and sequential patterns in climate variables. Ensemble Learning: Implement ensemble learning techniques to combine multiple diffusion models or other machine learning models to capture a broader range of spatial-temporal dependencies. Adaptive Noising Strategies: Develop adaptive strategies for determining the amount of noise added in the embedding transformations based on the complexity of the spatial-temporal patterns in the data. Hybrid Models: Explore the integration of different types of models, such as recurrent neural networks or graph neural networks, to capture complex spatial dependencies in climate data. By addressing these limitations and incorporating improvements to handle more complex spatial-temporal dependencies, the conditional diffusion model approach can be enhanced for a wider range of climate variables and scenarios.

Given the ability of the diffusion model to preserve large-scale patterns, how could this be leveraged to better constrain climate projections and improve uncertainty quantification in impact assessments?

The ability of the diffusion model to preserve large-scale patterns can be leveraged to better constrain climate projections and improve uncertainty quantification in impact assessments in the following ways: Improved Downscaling: By preserving large-scale patterns, the diffusion model can provide more accurate downscaling of climate variables from coarse-resolution Earth System Models (ESMs) to finer spatial scales. This can enhance the spatial resolution of climate projections and improve the representation of local climate conditions. Uncertainty Quantification: The preserved large-scale patterns can serve as a reference for quantifying uncertainties in climate projections. By comparing the downscaled and bias-corrected outputs with the original ESM data, uncertainties in the model simulations can be identified and quantified more effectively. Robust Impact Assessments: The accurate preservation of large-scale patterns can lead to more reliable impact assessments for various sectors, such as agriculture, water resource management, and infrastructure planning. Improved downscaled data can provide stakeholders with more precise information for decision-making. Ensemble Approaches: Leveraging the preserved large-scale patterns, ensemble approaches can be employed to generate multiple downscaled projections, considering different sources of uncertainty. This ensemble can provide a range of possible outcomes, enhancing the robustness of impact assessments. Feedback Mechanisms: The preserved large-scale patterns can be used in feedback mechanisms to refine ESMs and improve their performance in capturing regional climate variability. This iterative process can lead to more accurate climate projections and impact assessments over time. By utilizing the preserved large-scale patterns from the diffusion model, climate projections can be better constrained, uncertainties can be quantified more effectively, and impact assessments can be more robust and reliable. This can ultimately support informed decision-making in climate adaptation and mitigation strategies.