Probabilistic Deep Learning for Drought Forecasting: Role of Internal Climate Variability

• URL: http://arxiv.org/abs/2608.01864v1
• Date: Mon, 03 Aug 2026 08:14:25 GMT
• Title: Probabilistic Deep Learning for Drought Forecasting: Role of Internal Climate Variability
• Abstract summary: We propose a deep-learning-based forecasting framework for European drought prediction.<n>We extend it with an uncertainty-aware drought bound that explicitly incorporates internal forecast variability from a large climate model ensemble.<n>Our results show that internal variability should be treated as a forecast quantity in its own right.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Predicting drought risk is essential for anticipating impacts on water resources, agriculture, ecosystems, and climate adaptation planning. Yet drought forecasts remain uncertain because variability can substantially alter regional precipitation and evaporative demand. Treating this variability as unstructured noise ignores the fact that internal variability has spatial, seasonal, and temporal structure and thus contains information that can be used to improve drought forecasting. We propose a deep-learning-based forecasting framework for European drought prediction and extend it with an uncertainty-aware drought bound that explicitly incorporates internal forecast variability from a large climate model ensemble. This bound represents a physically plausible lower-tail trajectory of future drought conditions and marks how severe drought could plausibly become under an unfavourable realisation of internal variability, giving adaptation planning a conservative, risk-averse reference. We compare the proposed bound with a lower bound derived from reanalysis data only and show that our proposed ensemble-informed bound is better calibrated across most regions and seasons. This is specifically true during anomalously dry conditions, when historical reanalysis alone underestimates lower-tail drought risk. Our results show that internal variability should be treated as a forecast quantity in its own right. More broadly, large ensembles provide a practical way to transfer physically plausible climate variability into machine-learning drought forecasts, yielding risk-aware bounds that are more informative for drought assessment under shifting climate conditions.

Related papers

• EO-WM: A Physically Informed World Model for Probabilistic Earth Observation Forecasting [50.31500063307306]
  We introduce EO-WM, a video diffusion transformer for multispectral Earth Observation forecasting.<n> EO-WM incorporates a physically informed conditioning framework that represents meteorological forcing through a climatological baseline, weather anomalies, and cumulative physical stress signals.<n>Experiments show that EO-WM reduces the error in predicted Normalized Difference Vegetation Index (NDVI) decline amplitude by a relative 5.63% and improves directional hit rate by a relative 7.80%.
  arXiv  Detail & Related papers  (2026-06-25T16:53:30Z)
• Forecasting Fails: Unveiling Evasion Attacks in Weather Prediction Models [60.728124907335]
  This work introduces Weather Adaptive Adversarial Perturbation Optimization (WAAPO), a novel framework for generating targeted adversarial perturbations.<n>WAAPO achieves this by incorporating constraints for channel sparsity, spatial localization, and smoothness, ensuring that perturbations remain physically realistic and imperceptible.<n>Our experiments highlight critical vulnerabilities in AI-driven forecasting models, where small perturbations to initial conditions can result in significant deviations.
  arXiv  Detail & Related papers  (2025-12-09T17:20:56Z)
• Forecasting precipitation in the Arctic using probabilistic machine learning informed by causal climate drivers [0.0]
  This study proposes a probabilistic machine learning framework for modeling and predicting the dynamics and severity of precipitation.<n>To account for uncertainty, we employ the conformal prediction method, which enables the generation of calibrated non-parametric prediction intervals.
  arXiv  Detail & Related papers  (2025-10-28T10:05:34Z)
• Probabilistic bias adjustment of seasonal predictions of Arctic Sea Ice Concentration [0.0]
  Seasonal prediction systems often show biases and forecast complex-temporal errors.<n>We introduce a probabilistic error correction framework based on a conditional Variational Autoencoder model.<n>We show that the adjusted forecasts are better calibrated to the observational distribution, and have smaller errors than climatological mean adjusted forecasts.
  arXiv  Detail & Related papers  (2025-10-10T22:17:29Z)
• DroughtSet: Understanding Drought Through Spatial-Temporal Learning [10.430055605915895]
  Drought is one of the most destructive and expensive natural disasters.<n>We propose a new dataset, DroughtSet, which integrates relevant predictive features and three drought indices.<n>Our model learns from the spatial and temporal information of physical and biological features to predict three types of droughts simultaneously.
  arXiv  Detail & Related papers  (2024-12-19T17:24:15Z)
• CasCast: Skillful High-resolution Precipitation Nowcasting via Cascaded Modelling [93.65319031345197]
  We propose CasCast, a cascaded framework composed of a deterministic and a probabilistic part to decouple predictions for mesoscale precipitation distributions and small-scale patterns. CasCast significantly surpasses the baseline (up to +91.8%) for regional extreme-precipitation nowcasting.
  arXiv  Detail & Related papers  (2024-02-06T08:30:47Z)
• Long-term drought prediction using deep neural networks based on geospatial weather data [75.38539438000072]
  High-quality drought forecasting up to a year in advance is critical for agriculture planning and insurance. We tackle drought data by introducing an end-to-end approach that adopts a systematic end-to-end approach. Key findings are the exceptional performance of a Transformer model, EarthFormer, in making accurate short-term (up to six months) forecasts.
  arXiv  Detail & Related papers  (2023-09-12T13:28:06Z)
• Deep Ensembles to Improve Uncertainty Quantification of Statistical Downscaling Models under Climate Change Conditions [0.0]
  We propose deep ensembles as a simple method to improve the uncertainty quantification of statistical downscaling models. Deep ensembles allow for a better risk assessment, highly demanded by sectoral applications to tackle climate change.
  arXiv  Detail & Related papers  (2023-04-27T19:53:18Z)
• Multi-scale Digital Twin: Developing a fast and physics-informed surrogate model for groundwater contamination with uncertain climate models [53.44486283038738]
  Climate change exacerbates the long-term soil management problem of groundwater contamination. We develop a physics-informed machine learning surrogate model using U-Net enhanced Fourier Neural Contaminated (PDENO) In parallel, we develop a convolutional autoencoder combined with climate data to reduce the dimensionality of climatic region similarities across the United States.
  arXiv  Detail & Related papers  (2022-11-20T06:46:35Z)
• Learning Interpretable Deep State Space Model for Probabilistic Time Series Forecasting [98.57851612518758]
  Probabilistic time series forecasting involves estimating the distribution of future based on its history. We propose a deep state space model for probabilistic time series forecasting whereby the non-linear emission model and transition model are parameterized by networks. We show in experiments that our model produces accurate and sharp probabilistic forecasts.
  arXiv  Detail & Related papers  (2021-01-31T06:49:33Z)
• Improving seasonal forecast using probabilistic deep learning [1.1988695717766686]
  We develop a probabilistic deep neural network model to enhance seasonal forecast capability and forecast diagnosis. By leveraging complex physical relationships encoded in climate simulations, our model demonstrates favorable deterministic and probabilistic skill. We give a more definitive answer to how the El Nino/Southern Oscillation, the dominant mode of seasonal variability, modulates global seasonal predictability.
  arXiv  Detail & Related papers  (2020-10-27T21:02:26Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.