Generative Modeling of Microweather Wind Velocities for Urban Air Mobility

2025-03-04

AI Summary

Urban Air Mobility (UAM) faces significant safety risks due to the high spatial heterogeneity and stochasticity of local microscale wind, which are difficult to monitor in real time. To address this, we propose a lightweight, probabilistic generative model that efficiently maps coarse-grained meteorological forecasts to high-resolution, spatiotemporally resolved microscale wind fields. Methodologically, we introduce the first integration of Denoising Diffusion Probabilistic Models (DDPM), Flow Matching, and Gaussian Mixture Models (GMM), conditioned on SoDAR-measured microscale wind data and co-located macro-scale weather forecasts—requiring only temporary on-site sensing, with no permanent infrastructure. Experiments demonstrate substantial improvements over conventional deterministic approaches, particularly in turbulence representation, short-term wind variability capture, and physical plausibility. The model enables deployable, real-time wind risk assessment for UAM operations.

Technology Category

• Planning, Routing, and Scheduling: Planning with Language Models
• Application Domains: Mobility, Driving & Flight
• Reasoning under Uncertainty: Relational Probabilistic Models

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs

Abstract

Motivated by the pursuit of safe, reliable, and weather-tolerant urban air mobility (UAM) solutions, this work proposes a generative modeling approach for characterizing microweather wind velocities. Microweather, or the weather conditions in highly localized areas, is particularly complex in urban environments owing to the chaotic and turbulent nature of wind flows. Furthermore, traditional means of assessing local wind fields are not generally viable solutions for UAM applications: 1) field measurements that would rely on permanent wind profiling systems in operational air space are not practical, 2) physics-based models that simulate fluid dynamics at a sufficiently high resolution are not computationally tractable, and 3) data-driven modeling approaches that are largely deterministic ignore the inherent variability in turbulent flows that dictates UAM reliability. Thus, advancements in predictive capabilities are needed to help mitigate the unique operational safety risks that microweather winds pose for smaller, lighter weight UAM aircraft. This work aims to model microweather wind velocities in a manner that is computationally-efficient, captures random variability, and would only require a temporary, rather than permanent, field measurement campaign. Inspired by recent breakthroughs in conditional generative AI such as text-to-image generation, the proposed approach learns a probabilistic macro-to-microweather mapping between regional weather forecasts and measured local wind velocities using generative modeling (denoising diffusion probabilistic models, flow matching, and Gaussian mixture models). A simple proof of concept was implemented using a dataset comprised of local (micro) measurements from a Sonic Detection and Ranging (SoDAR) wind profiler along with (macro) forecast data from a nearby weather station over the same time period.

Problem

• Generative modeling for urban microweather wind velocities.
• Addressing computational and measurement challenges in UAM safety.
• Capturing random variability in turbulent urban wind flows.

Innovation

• Generative modeling for microweather wind velocities
• Uses temporary field measurements, not permanent
• Combines macro forecasts with micro measurements