Micro-Weather Risk Mapping for Low-Level Flight via Urban Model Selection

Resolve Bottlenecks,
Find Innovative SolutionsGenerate Solutions

Solution Overview

Problem

Existing weather forecasting methods are inefficient and resource-intensive for very low-level aerial vehicles, particularly in urban environments, due to the complexity of urban canopy layers and micro-scale weather conditions, leading to unnecessary flight cancellations and delays.

Innovation Solution

A method for micro-weather risk mapping using hyperlocal wind information and predetermined micro-weather models to generate local risk zones, enabling automated path planning and flight management for very low-level aerial vehicles.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Measurement precision

If traditional microscale weather forecast models are used for very low-level aerial vehicles, then weather forecast accuracy is improved, but computing resources and time consumption increase substantially

Engineering Contradiction:

Improveweather forecast accuracyVSAvoidcomputing resources

Core Design Contradiction:

Measurement precisionVSUse of energy by moving object

Solution Approach 1:

The patent segments the weather forecasting problem into two parts: (1) use regional-scale models for broad weather patterns, and (2) use simplified microscale models only for local urban canopy effects. This segmentation allows the system to obtain sufficient accuracy for very low-level flight without the exponential computing cost of running full microscale models everywhere.

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

The patent applies local quality by using different model complexities in different spatial contexts: regional models for large-scale conditions and simplified microscale models only where urban canopy effects are present. This ensures computational resources are focused only where high-resolution modeling is actually needed for safety.

Inventive Principle:

Principle #3Local quality

2Measurement precision

If high detail and additional environmental complexity are included in weather models, then forecast accuracy for microscale conditions is improved, but computing cost increases exponentially

Engineering Contradiction:

Improvemicroscale forecast accuracyVSAvoidcomputing efficiency

Core Design Contradiction:

Measurement precisionVSProductivity

Solution Approach 1:

The patent segments the modeling domain by spatial scale and complexity, applying detailed microscale models only to urban canopy regions where they are necessary, while using coarser regional models for surrounding areas. This maintains microscale accuracy where needed while preserving overall computing efficiency.

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

The patent changes model parameters dynamically based on location and conditions, adjusting the level of detail and complexity according to the specific urban environment being modeled. This allows the system to maintain necessary accuracy while adapting computational resources to match the actual complexity of each scenario.

Inventive Principle:

Principle #35Parameter changes

3Productivity

If resource constraints are accepted for smaller aerial vehicle flights, then operational efficiency is improved, but reliable forecast lead times cannot be achieved

Engineering Contradiction:

Improveoperational efficiencyVSAvoidforecast lead time

Core Design Contradiction:

ProductivityVSLoss of time

Solution Approach 1:

The patent implements preliminary action by pre-computing and storing microscale model results for various urban canopy configurations and weather scenarios. When a flight is planned, the system can quickly retrieve and apply pre-computed microweather adjustments rather than running full models in real-time, thus maintaining reliability without sacrificing operational efficiency.

Inventive Principle:

Principle #10Preliminary action

4Productivity

If model output statistics approaches are used for downscaling regional weather reports, then computational efficiency is improved, but applicability to very low-level flight is lost due to observation station limitations

Engineering Contradiction:

Improvecomputational efficiencyVSAvoidapplicability to very low-level flight

Core Design Contradiction:

ProductivityVSAdaptability or versatility

Solution Approach 1:

The patent introduces an intermediary approach by using simplified physics-based microscale models that bridge the gap between regional model outputs and very low-level flight conditions. These intermediary models capture essential urban canopy effects without requiring dense observation networks, making them applicable to helicopter and drone operations at altitudes below 150 meters.

Inventive Principle:

Principle #24Intermediary (Mediator)

Data Source

PatentUS12387614B2Micro weather risk mapping for very low-level aerial vehicles

Publication Date: 2025.08.12 THE BOEING CO

AI summary

Micro-weather risk mapping for very low-level aerial vehicles includes receiving data indicating a first geographic area and obtaining meteorologic data for a second geographic area. The first geographic area is smaller than and entirely bounded by the second geographic area. Mapping includes determining topographic parameters associated with the first geographic area. Mapping includes performing a comparison of the topographic parameters associated with the first geographic area to topographic parameters associated with a plurality of predetermined micro-weather models. Mapping includes selecting a micro-weather model from among the plurality of predetermined micro-weather models based on the comparison. Mapping includes determining, based on the meteorologic data for the second geographic area and the micro-weather model, risk data indicative of risk of particular meteorological conditions in the first geographic area.