arxiv_ml 95% Match Research Paper Climate Scientists,Meteorologists,Machine Learning Researchers,Urban Planners 3 weeks ago

Probabilistic Super-Resolution for Urban Micrometeorology via a Schr\"odinger Bridge

generative-ai › diffusion

📄 Abstract

Abstract: This study employs a neural network that represents the solution to a Schr\"odinger bridge problem to perform super-resolution of 2-m temperature in an urban area. Schr\"odinger bridges generally describe transformations between two data distributions based on diffusion processes. We use a specific Schr\"odinger-bridge model (SM) that directly transforms low-resolution data into high-resolution data, unlike denoising diffusion probabilistic models (simply, diffusion models; DMs) that generate high-resolution data from Gaussian noise. Low-resolution and high-resolution data were obtained from separate numerical simulations with a physics-based model under common initial and boundary conditions. Compared with a DM, the SM attains comparable accuracy at one-fifth the computational cost, requiring 50 neural-network evaluations per datum for the DM and only 10 for the SM. Furthermore, high-resolution samples generated by the SM exhibit larger variance, implying superior uncertainty quantification relative to the DM. Owing to the reduced computational cost of the SM, our results suggest the feasibility of real-time ensemble micrometeorological prediction using SM-based super-resolution.

Authors (2)

Yuki Yasuda

Ryo Onishi

Submitted

October 14, 2025

arXiv Category

physics.ao-ph

arXiv PDF

Key Contributions

This paper introduces a novel Schrodinger Bridge Model (SM) for super-resolution of urban temperature, which directly transforms low-resolution data to high-resolution data. The SM achieves comparable accuracy to standard diffusion models but with significantly reduced computational cost (one-fifth) and generates samples with larger variance, indicating superior probabilistic representation.

Business Value

Enables more accurate and computationally efficient high-resolution climate predictions for urban planning, disaster management, and energy optimization, leading to better resource allocation and risk mitigation.

Paper Metadata

Innovation Type

Algorithmic Innovation

Deployment Feasibility

Potentially high, as it offers computational advantages over existing diffusion models, making real-time or near-real-time applications more feasible.

Limitations Addressed

Computational cost of diffusion models for super-resolution tasks and the limited variance in generated samples from standard diffusion models.

Performance Gains

Achieves comparable accuracy at one-fifth the computational cost compared to Diffusion Models.

Technical Tags

Schrodinger BridgeSuper-ResolutionNeural NetworksDiffusion ModelsProbabilistic ModelingMicrometeorologyUrban ClimateTime Series ForecastingGenerative ModelsStochastic Processes

Research Topics

Generative ModelingSuper-ResolutionProbabilistic InferenceClimate ScienceMachine Learning Applications

Methods & Architectures

Schrodinger Bridge Model (SM)Denoising Diffusion Probabilistic Models (DDPM)Neural Network Evaluation Neural Network

Applications & Tasks

Urban Micrometeorology Climate Science Environmental Monitoring Super-ResolutionData GenerationForecasting 2-m Temperature Super-ResolutionUrban Climate Modeling

Related Fields

Climate ScienceMeteorologyMachine LearningGenerative ModelsStochastic Processes

Keywords

Schrodinger BridgeSuper-ResolutionUrban MicrometeorologyTemperature ForecastingDiffusion ModelsProbabilistic Super-ResolutionNeural NetworksGenerative ModelsComputational EfficiencyClimate ModelingUrban PlanningStochastic Differential Equations

Academic Context

#Generative Modeling#Super-Resolution#Probabilistic Inference#Climate Science#Machine Learning Applications

Commercial Potential

Potential Products

High-resolution urban climate forecasting serviceAdvanced weather simulation software

Target Industries

EnergyUrban PlanningEnvironmental ConsultingInsurance

Use Case Examples

Predicting localized temperature variations in cities for energy demand management.Improving microclimate models for urban heat island effect analysis.

Competitive Edge

Offers a more computationally efficient alternative to existing diffusion models for super-resolution tasks, while maintaining comparable accuracy and improving sample diversity.

Market Opportunity

Growing market for climate modeling and urban analytics tools.

Revenue Models

SaaS for forecasting serviceslicensing of models.

Resource Requirements

Compute Needs

Moderate to High (for training and inference, but less than standard DMs)

Data Requirements

Paired low-resolution and high-resolution meteorological data.

Deployment Constraints

Requires accurate low-resolution input data; model performance may depend on the quality and representativeness of training data.

Scalability

Likely scalable due to reduced computational cost compared to standard diffusion models.

Production Readiness

Maturity Level

Research

Time to Market

1-3 years

Patent Potential

Moderate (novel algorithmic approach)

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