Synergy of AI and Numerical Weather Prediction: Observations, Modeling, and Forecasting
A special issue of Atmosphere (ISSN 2073-4433). This special issue belongs to the section "Atmospheric Techniques, Instruments, and Modeling".
Deadline for manuscript submissions: 31 July 2026 | Viewed by 187
Special Issue Editors
Interests: numerical weather prediction (NWP); atmospheric modeling (WRF); data assimilation; medium-range forecasting; high-performance computing (HPC) in meteorology; climate variability; extreme weather events
Special Issue Information
Dear Colleagues,
The field of meteorology is undergoing a profound transformation, driven by the rapid advancements in Artificial Intelligence (AI) and machine learning. However, traditional Numerical Weather Prediction (NWP) systems (e.g., GFS, WRF, RegCM) and classical objective analysis methods (e.g., OI, Barnes, STMAS) remain the foundational backbone of operational forecasting.
This Special Issue aims to explore the synergy between these paradigms. We seek cutting-edge research that not only showcases AI-native innovations but also demonstrates how AI can enhance, correct, or hybridize with physics-based models. Contributions establishing rigorous benchmarks between AI approaches and standard dynamical models are highly encouraged.
A unique focus of this Special Issue will be on addressing practical challenges in the Global South (including South America, Africa, Central and Southeast Asia). We welcome submissions applying these hybrid or AI-driven techniques in data-sparse environments for high-impact sectors like agriculture, renewable energy, and disaster risk reduction.
We invite original research and reviews on topics that include the following:
- Synergy of AI and NWP: Deep learning for bias correction, downscaling, and parameterization replacement in models like WRF and RegCM.
- Advanced Data Assimilation and Objective Analysis: AI-enhanced methods for assimilating multi-source data, compared with or integrated into traditional schemes like STMAS, Barnes, and Optimal Interpolation (OI).
- AI-Native Forecasting: Development and validation of pure data-driven models (e.g., Transformers, Graph Neural Networks) against operational baselines (e.g., GFS).
- AI for Meteorological Observations: Quality control and feature extraction from radar/satellite data using computer vision.
- Short-to-Long Term Forecasting: AI and hybrid applications in nowcasting, S2S forecasting, and climate projection.
- High-Impact Weather: Forecasting extreme events (cyclones, floods) using combined physical and data-driven approaches.
- Model Interpretability (XAI): Understanding the "black box" of AI in the context of atmospheric physics.
Dr. Lei Bai
Dr. Gang Wang
Guest Editors
Manuscript Submission Information
Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 250 words) can be sent to the Editorial Office for assessment.
Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Atmosphere is an international peer-reviewed open access monthly journal published by MDPI.
Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.
Keywords
- artificial intelligence (AI)
- machine learning
- numerical weather prediction (NWP)
- WRF/RegCM/GRAPES/PALM
- data assimilation
- objective analysis (STMAS/Barnes/OI)
- global south
- extreme weather events
- hybrid modeling
- deep learning
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