  High-fidelity simulated astronomical images are an important tool in
developing and measuring the performance of image-processing algorithms,
particularly for high precision measurements of cosmic shear -- correlated
distortions of images of distant galaxies due to weak gravitational lensing
caused by the large-scale mass distribution in the Universe. For unbiased
measurements of cosmic shear, all other sources of correlated image distortions
must be modeled or removed. One such source is the correlated blurring of
images due to optical turbulence in the atmosphere, which dominates the
point-spread function (PSF) for ground-based instruments. In this work, we
leverage data from weather forecasting models to produce wind speeds and
directions, and turbulence parameters, that are realistically correlated with
altitude. To study the resulting correlations in the size and shape of the PSF,
we generate simulated images of the PSF across a ~10 square-degree field of
view -- the size of the camera focal plane for the Vera C. Rubin Observatory in
Chile -- using weather data and historical seeing for a geographic location
near the Observatory. We make quantitative predictions for two-point
correlation functions (2PCF) that are used in analyses of cosmic shear. We
observe a strong anisotropy in the two-dimensional 2PCF, which is expected
based on observations in real images, and study the dependence of the
orientation of the anisotropy on dominant wind directions near the ground and
at higher altitudes.
  The code repository for producing the correlated weather parameters for input
to simulations (psf-weather-station) is public at
https://github.com/LSSTDESC/psf-weather-station.
