A Pilot Study on Meteorological Support for the Low-Altitude Economy—Consistency of Meteorological Measurements on UAS with Numerical Simulation Results

Meteorological measurements from Unmanned Aircraft Systems (UASs) increase the volume of observations available for validating and improving high-spatiotemporal-resolution models. Accurate model forecasts for UAS operations are essential to the successful development of the low-altitude economy (LAE). In this study, two UAS test flights were analyzed to assess the consistency between UAS measurements and Regional Atmospheric Modeling System model outputs, thereby evaluating model forecast skill. UAS measurements were compared with ground-based anemometer and radiosonde observations to meet the World Meteorological Organization observational requirements at both the Threshold and Goal levels. Model-forecast turbulence exhibited strong agreement with atmospheric turbulence derived from high-frequency UAS wind data. The numerical weather prediction model at high spatial and temporal resolution is found to have sufficiently accurate forecasts to support UAS operation. A computational fluid dynamics model was also tested for high-resolution wind and turbulence forecasting; however, it did not yield improvements over the meteorological model. This work represents the first study of its kind for LAE applications in Hong Kong, and further statistical analyses are planned.

Authors

Institutions

Publication Details

Journal
Atmosphere
Published
2026-01-20
DOI
https://doi.org/10.3390/atmos17010107
Primary Topic
Aerospace and Aviation Technology
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

A Pilot Study on Meteorological Support for the Low-Altitude Economy—Consistency of Meteorological Measurements on UAS with Numerical Simulation Results

Pak Wai CHAN, Jingyu He, Ming Chun Lam, Wai Hung Leung et al.
Atmosphere
Aerospace and Aviation Technology
article

A Pilot Study on Meteorological Support for the Low-Altitude Economy—Consistency of Meteorological Measurements on UAS with Numerical Simulation Results

Pak Wai CHAN, Jingyu He, Ming Chun Lam, Wai Hung Leung, Kai Kwong Lai, Ka Wai Lo, Qiu Sheng Li
article en

Abstract

Meteorological measurements from Unmanned Aircraft Systems (UASs) increase the volume of observations available for validating and improving high-spatiotemporal-resolution models. Accurate model forecasts for UAS operations are essential to the successful development of the low-altitude economy (LAE). In this study, two UAS test flights were analyzed to assess the consistency between UAS measurements and Regional Atmospheric Modeling System model outputs, thereby evaluating model forecast skill. UAS measurements were compared with ground-based anemometer and radiosonde observations to meet the World Meteorological Organization observational requirements at both the Threshold and Goal levels. Model-forecast turbulence exhibited strong agreement with atmospheric turbulence derived from high-frequency UAS wind data. The numerical weather prediction model at high spatial and temporal resolution is found to have sufficiently accurate forecasts to support UAS operation. A computational fluid dynamics model was also tested for high-resolution wind and turbulence forecasting; however, it did not yield improvements over the meteorological model. This work represents the first study of its kind for LAE applications in Hong Kong, and further statistical analyses are planned.

AtmosphereVol. 17(1)
City University of Hong Kong (HK), Hong Kong Observatory (CN)
Openalex Percentile: Top 86%
Aerospace and Aviation Technology
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.