Researchers from the Hefei Institutes of Physical Science (HFIPS) of the Chinese Academy of Sciences (CAS) have developed the world's first global model to predict and visualize the intensity of atmospheric optical turbulence.

The results were published in Monthly Notices of the Royal Astronomical Society.

Scintillation, beam wander, and beam jitter are caused by optical turbulence in the Earth's atmosphere. These disturbances greatly affect the performance of atmospheric optoelectronic systems, making ground-based applications difficult. Many methods exist to detect atmospheric optical turbulence, but global features such as intensity, probability distribution, and spatiotemporal fluctuations are difficult to obtain.

In this study, the researchers developed a global atmospheric optical turbulence prediction model to address this problem. Using historical detection datasets and cutting-edge big data fusion analysis technology, coupled with the European Fifth-Generation Atmospheric Reanalysis dataset, the model overcame the limitations previously encountered in determining wide-area atmospheric optical turbulence characteristics.

The model's predictions provided insight into the spatial distribution characteristics of global atmospheric optical turbulence parameters.

"With this model, we found more regions with favorable atmospheric optical turbulence conditions beyond the traditional astronomical observatory sites," said QING Chun, first author of the study.

By integrating with global numerical weather prediction models, this novel model also enabled the prediction of temporal variations in global atmospheric optical turbulence characteristics.

The researchers believe that it will provide valuable scientific support for the medium- to long-term operation of large-scale ground-based advanced optoelectronic systems.

Global median atmospheric coherence length from the general turbulence model. (Image by QING Chun) 

Cumulative probability of atmospheric coherence length at astronomical sites. (Image by QING Chun)