{"total":15,"items":[{"citing_arxiv_id":"2606.27094","ref_index":57,"ref_count":1,"confidence":0.88,"is_internal_anchor":false,"paper_title":"Learning Climate Variability from Scarce Data with Diffusion Models: A Test Case for ENSO","primary_cat":"physics.ao-ph","submitted_at":"2026-06-25T14:31:17+00:00","verdict":"UNVERDICTED","verdict_confidence":"MODERATE","novelty_score":5.0,"formal_verification":"none","one_line_summary":"Diffusion models recover known ENSO variability structure from synthetic LIM data when given enough samples, but require pre-training on CMIP6 plus fine-tuning to match observations with the ~700 samples available in ERSSTv5.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2606.26421","ref_index":29,"ref_count":1,"confidence":0.88,"is_internal_anchor":false,"paper_title":"Otter Weather: Skillful and Computationally Efficient Medium-Range Weather Forecasting","primary_cat":"cs.LG","submitted_at":"2026-06-24T22:23:13+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":5.0,"formal_verification":"none","one_line_summary":"Otter Weather is a spatiotemporal model that outperforms NWP baselines by 9.6% at 24h lead with under 3.5 A100-days training and extends efficiency gains to probabilistic forecasting via CRPS.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2606.25937","ref_index":24,"ref_count":1,"confidence":0.88,"is_internal_anchor":false,"paper_title":"Event-Aware Loss Design for Forecasting of Convective Precipitation and Lightning","primary_cat":"physics.ao-ph","submitted_at":"2026-06-24T15:14:53+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":4.0,"formal_verification":"none","one_line_summary":"A multi-task Patch-cGAN with lightning-derived spatial loss weighting improves post-processed forecasts of intense precipitation and lightning occurrence over the Korean Peninsula in summer 2025.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2606.21170","ref_index":37,"ref_count":1,"confidence":0.88,"is_internal_anchor":false,"paper_title":"Towards Fair Comparisons of AI- and Physics-Based Weather Models for Extreme Events via the Weighted Potential CRPS","primary_cat":"stat.AP","submitted_at":"2026-06-19T07:18:47+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":5.0,"formal_verification":"none","one_line_summary":"Extends Potential CRPS with weights and IDR post-processing to enable fair comparisons of AIWP and NWP models on extreme weather, finding AI models more informative across most variables and thresholds.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2606.19026","ref_index":23,"ref_count":1,"confidence":0.88,"is_internal_anchor":false,"paper_title":"A Hybrid LSTM--Vision Transformer Architecture for Predicting HRRR Forecast Errors","primary_cat":"cs.LG","submitted_at":"2026-06-17T12:51:46+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"Hybrid LSTM-ViT model using mesonet surface data and profiler vertical profiles improves HRRR forecast error prediction for precipitation, wind speed, and temperature, with roughly twofold skill gain for precipitation over baseline LSTM.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2606.02886","ref_index":33,"ref_count":1,"confidence":0.88,"is_internal_anchor":false,"paper_title":"Scalable Uncertainty Quantification for Extreme Weather Forecasting via Empirical Neural Tangent Kernels","primary_cat":"cs.LG","submitted_at":"2026-06-01T20:57:06+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"NTK-UQ produces 31-37% sharper 90% prediction intervals than split conformal prediction for extreme weather forecasts, with adaptive scaling via architecture-dependent eigenvalue truncation and ICA decomposition of last-layer features.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2605.22248","ref_index":38,"ref_count":1,"confidence":0.88,"is_internal_anchor":false,"paper_title":"No Epoch Like the Present: Robust Climate Emulation Requires Out-of-Distribution Generalisation","primary_cat":"cs.LG","submitted_at":"2026-05-21T09:54:57+00:00","verdict":"CONDITIONAL","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"ML climate emulators degrade under seasonal distribution shifts that proxy long-term climate change, but physically motivated compositional decompositions improve out-of-distribution performance with modest in-distribution trade-offs.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2605.16163","ref_index":48,"ref_count":1,"confidence":0.88,"is_internal_anchor":false,"paper_title":"SwAIther-Precip: Lead-Time-Aware Bias Correction Enables Kilometer-Scale Downscaling of Global AI Precipitation Forecasts over Switzerland","primary_cat":"physics.ao-ph","submitted_at":"2026-05-15T16:42:45+00:00","verdict":null,"verdict_confidence":null,"novelty_score":null,"formal_verification":null,"one_line_summary":null,"context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2605.01599","ref_index":25,"ref_count":1,"confidence":0.88,"is_internal_anchor":false,"paper_title":"Cast3: Translating numerical weather prediction principles into data-driven forecasting","primary_cat":"physics.ao-ph","submitted_at":"2026-05-02T20:17:16+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":7.0,"formal_verification":"none","one_line_summary":"Cast3 translates NWP principles into a data-driven model using cubed-sphere grids, super-ensembles, and generative nudging to achieve state-of-the-art ensemble predictions that outperform baselines.","context_count":1,"top_context_role":"background","top_context_polarity":"background","context_text":": AIFS-CRPS: ensemble forecasting using a model trained with a loss function based on the continuous ranked probability score. npj Artificial Intelligence2(1), 18 (2026) https://doi.org/10.1038/s44387-026-00073-7 [24] Bonavita, M.: On Some Limitations of Current Machine Learning Weather Pre- diction Models. Geophysical Research Letters51(12), 2023-107377 (2024) https: //doi.org/10.1029/2023GL107377 [25] Rasp, S., Hoyer, S., Merose, A., Langmore, I., Battaglia, P., Russell, T., Sanchez- Gonzalez, A., Yang, V., Carver, R., Agrawal, S., Chantry, M., Ben Bouallegue, Z., Dueben, P., Bromberg, C., Sisk, J., Barrington, L., Bell, A., Sha, F.: Weather- Bench 2: A Benchmark for the Next Generation of Data-Driven Global Weather Models. Journal of Advances in Modeling Earth Systems16(6), 2023-004019"},{"citing_arxiv_id":"2605.01126","ref_index":6,"ref_count":1,"confidence":0.88,"is_internal_anchor":false,"paper_title":"Extreme Weather Bench: A framework and benchmark for evaluation of high-impact weather","primary_cat":"cs.LG","submitted_at":"2026-05-01T21:52:00+00:00","verdict":"ACCEPT","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"Extreme Weather Bench supplies standardized case studies, observational data, impact metrics, and code to evaluate weather models on high-impact hazards.","context_count":1,"top_context_role":"background","top_context_polarity":"background","context_text":"Forecasting the wide variety of high-impact weather events experienced globally is a challenge for both Artificial Intelligence (AI) and Numerical Weather Pre- diction (NWP) models and it is critical that such models be properly verified before deployment. Although AI weather models are rapidly evolving (e.g.,[1], [2],[3], [4]), much of their evaluation is currently done either with a global-scale evaluation (e.g., [5],[6]) or by hand-picking a small number of case studies or a region (e.g.,[2],[7],[8]). A widely-used open-source benchmark suite focusing on high-impact weather will help to drive the science forward for all scales of weather models, as it has for other AI fields (e.g. [9]). Here we introduce Extreme Weather Bench (EWB), a new community-driven benchmark suite that facilitates model"},{"citing_arxiv_id":"2604.20467","ref_index":13,"ref_count":1,"confidence":0.88,"is_internal_anchor":false,"paper_title":"Mechanistic Interpretability Tool for AI Weather Models","primary_cat":"physics.ao-ph","submitted_at":"2026-04-22T11:54:09+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":5.0,"formal_verification":"none","one_line_summary":"An open-source tool is developed for mechanistic interpretability of AI weather models, demonstrated on GraphCast by identifying latent directions corresponding to interpretable weather features.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2604.16238","ref_index":10,"ref_count":1,"confidence":0.88,"is_internal_anchor":false,"paper_title":"Enhancing AI and Dynamical Subseasonal Forecasts with Probabilistic Bias Correction","primary_cat":"cs.LG","submitted_at":"2026-04-17T16:58:06+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"Probabilistic bias correction doubles AI subseasonal forecast skill and wins a 2025 international competition by correcting biases in ECMWF models for pressure, temperature, and precipitation.","context_count":1,"top_context_role":"background","top_context_polarity":"background","context_text":", Turner, R.E., Perdikaris, P.: A foundation model for the Earth system. Nature641, 1180-1187 (2025) https://doi.org/10. 1038/s41586-025-09005-y [9] Chen, L., Zhong, X., Zhang, F., Cheng, Y ., Xu, Y ., Qi, Y ., Li, H.: Fuxi: a cascade machine learning forecasting system for 15-day global weather forecast. npj Climate and Atmospheric Science6(1), 190 (2023) [10] Rasp, S., Hoyer, S., Merose, A., Langmore, I., Battaglia, P., Russell, T., Sanchez-Gonzalez, A., Yang, V ., Carver, R., Agrawal, S., Chantry, M., Ben Bouallegue, Z., Dueben, P., Bromberg, C., Sisk, J., Barrington, L., Bell, A., Sha, F.: Weatherbench 2: A benchmark for the next generation of data-driven global weather models. Journal of Advances in Modeling Earth Systems16(6), 2023-004019 (2024) https://doi."},{"citing_arxiv_id":"2604.09041","ref_index":47,"ref_count":1,"confidence":0.88,"is_internal_anchor":false,"paper_title":"U-Cast: A Surprisingly Simple and Efficient Frontier Probabilistic AI Weather Forecaster","primary_cat":"cs.LG","submitted_at":"2026-04-10T07:02:20+00:00","verdict":null,"verdict_confidence":null,"novelty_score":null,"formal_verification":null,"one_line_summary":null,"context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2604.06567","ref_index":34,"ref_count":2,"confidence":0.88,"is_internal_anchor":false,"paper_title":"A PMP-inspired Evaluation Framework for Assessing Deep-Learning Earth System Models","primary_cat":"physics.ao-ph","submitted_at":"2026-04-08T01:34:43+00:00","verdict":null,"verdict_confidence":null,"novelty_score":null,"formal_verification":null,"one_line_summary":null,"context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2601.17636","ref_index":9,"ref_count":1,"confidence":0.88,"is_internal_anchor":false,"paper_title":"HealDA: Highlighting the importance of initial errors in end-to-end AI weather forecasts","primary_cat":"physics.ao-ph","submitted_at":"2026-01-25T00:07:26+00:00","verdict":"CONDITIONAL","verdict_confidence":"MODERATE","novelty_score":6.0,"formal_verification":"none","one_line_summary":"HealDA supplies ML-based initial conditions for AI weather models that produce forecasts trailing ERA5-initialized runs by less than one day of effective lead time, with the skill gap arising mainly from initial error size.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null}],"limit":50,"offset":0}