Forecasting Cloud Cover and Atmospheric Seeing for Astronomical Observing: Application and Evaluation of the Global Forecast System

TL;DR

This study evaluates the Global Forecast System's ability to predict cloud cover and atmospheric seeing for astronomical observations, demonstrating moderate accuracy and potential for improvement with model refinement.

Contribution

It applies and assesses the GFS model with specific cloud and seeing schemes for astronomical forecasting across multiple sites.

Findings

Cloud cover forecast accuracy varies from 50% to 85%.

Seeing forecast errors are generally within 0.3 arcseconds.

Forecast probability of less than 30% error is 40-60% across sites.

Abstract

To explore the issue of performing a non-interactive numerical weather forecast with an operational global model in assist of astronomical observing, we use the Xu-Randall cloud scheme and the Trinquet-Vernin AXP seeing model with the global numerical output from the Global Forecast System to generate 3-72h forecasts for cloud coverage and atmospheric seeing, and compare them with sequence observations from 9 sites from different regions of the world with different climatic background in the period of January 2008 to December 2009. The evaluation shows that the proportion of prefect forecast of cloud cover forecast varies from ~50% to ~85%. The probability of cloud detection is estimated to be around ~30% to ~90%, while the false alarm rate is generally moderate and is much lower than the probability of detection in most cases. The seeing forecast has a moderate mean difference (absolute mean difference <0.3" in most cases) and root-mean-square-error or RMSE (0.2"-0.4" in most cases) comparing with the observation. The probability of forecast with <30% error varies between 40% to 60% for entire atmosphere forecast and 40% to 50% for free atmosphere forecast for almost all sites, which being placed in the better cluster among major seeing models. However, the forecast errors are quite large for a few particular sites. Further analysis suggests that the error might primarily be caused by the poor capability of GFS/AXP model to simulate the effect of turbulence near ground and on sub-kilometer scale. In all, although the quality of the GFS model forecast may not be comparable with the human-participated forecast at this moment, our study has illustrated its suitability for basic observing reference, and has proposed its potential to gain better performance with additional efforts on model refinement.