Towards NoahMP-AI: Enhancing Land Surface Model Prediction with Deep Learning
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arXiv
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| Format: | Preprint |
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2025
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| _version_ | 1866911073732395008 |
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| author | Mbarak, Mahmoud Singh, Manmeet Sudharsan, Naveen Yang, Zong-Liang |
| author_facet | Mbarak, Mahmoud Singh, Manmeet Sudharsan, Naveen Yang, Zong-Liang |
| contents | Accurate soil moisture prediction during extreme events remains a critical challenge for earth system modeling, with profound implications for drought monitoring, flood forecasting, and climate adaptation strategies. While land surface models (LSMs) provide physically-based predictions, they exhibit systematic biases during extreme conditions when their parameterizations operate outside calibrated ranges. Here we present NoahMP-AI, a physics-guided deep learning framework that addresses this challenge by leveraging the complete Noah-MP land surface model as a comprehensive physics-based feature generator while using machine learning to correct structural limitations against satellite observations. We employ a 3D U-Net architecture that processes Noah-MP outputs (soil moisture, latent heat flux, and sensible heat flux) to predict SMAP soil moisture across two contrasting extreme events: a prolonged drought (March-September 2022) and Hurricane Beryl (July 2024) over Texas. When comparing NoahMP-AI with NoahMP, our results demonstrate an increase in R-squared values from -0.7 to 0.5 during drought conditions, while maintaining physical consistency and spatial coherence. The framework's ability to preserve Noah-MP's physical relationships while learning observation-based corrections represents a significant advance in hybrid earth system modeling. This work establishes both a practical tool for operational forecasting and a benchmark for investigating the optimal integration of physics-based understanding with data-driven learning in environmental prediction systems. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2506_12919 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Towards NoahMP-AI: Enhancing Land Surface Model Prediction with Deep Learning Mbarak, Mahmoud Singh, Manmeet Sudharsan, Naveen Yang, Zong-Liang Atmospheric and Oceanic Physics Accurate soil moisture prediction during extreme events remains a critical challenge for earth system modeling, with profound implications for drought monitoring, flood forecasting, and climate adaptation strategies. While land surface models (LSMs) provide physically-based predictions, they exhibit systematic biases during extreme conditions when their parameterizations operate outside calibrated ranges. Here we present NoahMP-AI, a physics-guided deep learning framework that addresses this challenge by leveraging the complete Noah-MP land surface model as a comprehensive physics-based feature generator while using machine learning to correct structural limitations against satellite observations. We employ a 3D U-Net architecture that processes Noah-MP outputs (soil moisture, latent heat flux, and sensible heat flux) to predict SMAP soil moisture across two contrasting extreme events: a prolonged drought (March-September 2022) and Hurricane Beryl (July 2024) over Texas. When comparing NoahMP-AI with NoahMP, our results demonstrate an increase in R-squared values from -0.7 to 0.5 during drought conditions, while maintaining physical consistency and spatial coherence. The framework's ability to preserve Noah-MP's physical relationships while learning observation-based corrections represents a significant advance in hybrid earth system modeling. This work establishes both a practical tool for operational forecasting and a benchmark for investigating the optimal integration of physics-based understanding with data-driven learning in environmental prediction systems. |
| title | Towards NoahMP-AI: Enhancing Land Surface Model Prediction with Deep Learning |
| topic | Atmospheric and Oceanic Physics |
| url | https://arxiv.org/abs/2506.12919 |