Automated Diagnostic Reasoning

• Automated diagnostic reasoning is the systematic emulation of expert analysis workflows that decompose tasks, ingest diverse data, and construct detailed narratives for extreme weather events.
• It integrates chain-of-thought planning, closed-loop feedback, and domain-specific toolkits to generate reproducible, auditable analyses of meteorological phenomena.
• Evaluation employs tail-weighted metrics and multivariate scoring to ensure rigorous, context-aware verification of both visual outputs and explanatory narratives.

Automated diagnostic reasoning in the context of extreme weather encompasses the formalization, algorithmic implementation, and evaluation of machine-guided workflows for identifying, interpreting, and explaining high-impact meteorological phenomena. The domain synthesizes concepts from interpretive meteorology, statistical verification, machine learning architectures, multimodal vision-language analysis, and representation-learning to yield expert-level reasoning that is reproducible, auditable, and context-aware.

1. Core Concepts and Definitions

Automated diagnostic reasoning refers to the systematic emulation of expert analysis workflows by AI agents, encompassing task decomposition, data ingestion, computational interpretation, and contextual explanation. In extreme weather, this includes the autonomous synthesis and evaluation of event-specific visualizations and narratives grounded in physical diagnostics and statistical scores. The approach shifts from deterministic or threshold-based flagging to knowledge-augmented, sequence-structured reasoning that illuminates both the causal mechanics and epistemic uncertainties of observed or simulated extremes (Jiang et al., 26 Nov 2025).

Diagnostic reasoning protocols typically consist of:

• Chain-of-Thought (CoT) planning: explicit decomposition of high-level tasks (e.g., "diagnose cyclone genesis") into sequenced expert subtasks.
• Closed-loop reasoning: iterative evaluation incorporating code generation, environmental feedback, programmatic auditing, and narrative synthesis.
• Domain-specific toolkits: targeted computational routines for physically meaningful variables (potential vorticity, anomaly maps, synoptic charts).
• Reporting modules: stepwise interpretation synthesizing intermediate observations into cohesive event explanations.

The operational aim is not prediction or classification per se, but the autonomous construction and validation of explanatory narratives—explicitly linking observed features to underlying meteorological physics with minimal human intervention.

2. System Architectures and Reasoning Workflows

A prototypical system such as the Extreme Weather Expert (EWE) (Jiang et al., 26 Nov 2025) demonstrates this architecture:

• Knowledge-Enhanced Planner: Instantiates chain-of-thought task sequences from exemplar memory, coding event analysis into linear expert-guided subtasks.
• Self-Evolving Closed-Loop Reasoning: Encodes the workflow as a sequence $\tau = ((t_k, a_k, o_k, i_k))_{k=1}^n$, where $t_k$ (thought) yields $a_k$ (action, usually Python code invoking domain routines), which returns $o_k$ (raw output, data or visualization) and receives dual critique via Code Auditor (syntactic validation) and Content Auditor (perceptual assessment), prior to generation of $i_k$ (interpreted narrative).
• Meteorological Toolkit: Provides rigorously-validated functions for diagnosis—synoptic charting, anomaly mapping, vertical cross-sections, PV and IVT calculation—allowing each code step to manipulate raw meteorological inputs and stage sophisticated physical analyses.

This architecture leverages expert-crafted templates and reasoning traces for physical grounding, reducing the risk of LLM-induced error or hallucination. Each reasoning step is audited and subject to regression against benchmarks, underpinning reproducibility and interpretative consistency.

3. Evaluation Methodologies and Metric Design

Accurate scoring of automated diagnostic reasoning surpasses simple accuracy or binary classification, requiring multi-dimensional verification that emphasizes both the occurrence and severity of extremes, and the physical correctness of explanations.

Weighted scoring rules, particularly threshold-weighted CRPS (twCRPS), vertically re-scaled CRPS (vrCRPS), and conditional PIT (cPIT) histograms, enable tail-sensitive evaluation while preserving propriety (Allen et al., 2022):

• twCRPS: Applies an indicator function $w(z)=\mathbf{1}\{z>t\}$ to censor points below a threshold, focusing scoring on tail exceedances.
• vrCRPS: Reweights loss according to outcome proximity to tail; for indicator weights and canonical $x_0$ recovers twCRPS structure.
• cPIT Histograms: Evaluate calibration conditional on exceedance, assessing distributional reliability for subpopulations where observed $y>t$.
• Multivariate Extensions: twES, vrES, and Energy/Variogram scores propagate tail emphasis to compound weather events.

These scores are complemented by step-wise expert rubrics—evaluating code fidelity, visualization quality, and interpretive depth—aggregated per event and per reasoning stage (Jiang et al., 26 Nov 2025). Quantitative case studies confirm that tail-weighted metrics, when combined with conditional reliability diagrams, afford a principled toolkit for system comparison and operational warning design.

4. Model Architectures, Scenario Generation, and Representation Learning

Scenario generation for extreme weather diagnostic reasoning rests upon interchangeable modular frameworks (Zadrozny et al., 2021), deep learning prediction suites (Ni et al., 2 Aug 2025, Verma et al., 2023), and unsupervised, physics-based segmentation of event skeletons (Rupe et al., 2019).

In modular scenario pipelines, sequential blocks allow alternative instantiations for seasonal means, event thresholds (via GEV, EVT, ML anomaly detection), stochastic or generative weather synthesis (Markov chain baseline, conditional GANs), and quantitative verification. Conditionally generative models (GAN, diffusion, VAE) admit explicit threshold control in extreme-event simulation, while hybrid architectures combine physics-based post-processing and multiscale bias correction for GCM outputs (Blanchard et al., 2022).

Representation learning extends to unsupervised segmentation: local causal states, computed via equivalence classes of past lightcone predictability, delineate the structural “skeleton” of spatiotemporal extremes, facilitating physically-interpretable identification without labeling (Rupe et al., 2019). Such decompositions are interpretable and scalable, crucial for event localization or for downstream classification of event classes via rule-based or Bayesian fusion across observables.

5. Vision-Language Reasoning and Human-Interpretable Analysis

Visual analysis of meteorological data, particularly heatmaps and anomalous contours, is a core aspect of automated reasoning. Diagnostic reasoning systems increasingly integrate vision-LLMs (VLMs) fine-tuned on domain-specific VQA benchmarks (Chen et al., 14 Jun 2024). The SPOT algorithm underpins precise geometric extraction of colored regions from heatmaps, reducing contours to sparse geo-clusters and allowing structured querying of regions via location, intensity, categorical indices, and detailed paragraphic description.

Benchmarks like ClimateIQA provide high-resolution annotated heatmaps with geographically anchored region names and meteorological variable legends. Climate-Zoo VLMs, trained on such data, attain >90% accuracy in core diagnostic tasks—geography, severity verification, enumeration, and narrative description (Chen et al., 14 Jun 2024). This enables transparent, actionable communication of extreme events to both technical and lay audiences.

6. Language-Based Reasoning, Interpretability, and Real-World Analytics

Automated diagnostic reasoning is extended to natural language analysis with frameworks such as ClimaEmpact (Varshney et al., 27 Apr 2025), wherein small LLMs (SLMs) aligned with LLM-generated chain-of-thought examples are fine-tuned to deliver vulnerability, impact/emergency categorization, granular keyword labeling, and emotion analytics from unstructured news corpora. Two-stage EWRA fine-tuning (implicit, then explicit reasoning) ensures models weight domain definition and structured explanation, yielding outputs traceable to expert reasoning paths and exceeding generic task-specific baselines.

This architecture supports real-time, explainable text analytics at sub-national granularity, bridging the data gap and interpretability demands in operational risk management and stakeholder communications.

7. Practical Integration, Limitations, and Outlook

Automated diagnostic reasoning assumes a central role in extreme weather expert systems and decision-support chains (Zadrozny et al., 2021, Jiang et al., 26 Nov 2025). Integration with scenario generators, ensemble modelers, vision-language explainers, and structured language analytics solidifies the foundation for resilient, scalable, and interpretable meteorological analysis.

Key strengths:

• Modular, auditable reasoning pipelines, validated by proper tail-weighted metrics.
• Scalable unsupervised representation learning for event skeleton extraction.
• Real-time, multimodal VLMs for visual–linguistic event explanation.
• Integrated SLM-driven analytics for granular impact assessment.

Principal limitations:

• Dependence on internal LLM knowledge and exemplar memory for physical grounding.
• Scope limited by toolkit coverage (IPCC event categories, missing rarer phenomena).
• Computational overhead imposed by closed-loop, multi-stage reasoning and high-resolution data flows.

Future directions emphasize the democratization of expert-level diagnostics (web services, LMIC deployment), real-time sensor and data stream integration, continual LLM updating, and extension to compound and multi-variable extremes. The synthesis of statistical rigor, tailored machine learning, and transparent expert reasoning constitutes the current frontier in automated diagnostic analysis for extreme weather events.