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

To explore the issue of performing a noninteractive numerical weather forecast with an operational global model to assist with 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 (GFS) to generate 3-72 hr forecasts for cloud coverage and atmospheric seeing, and we compare them with sequence observations from nine sites from different regions of theworld with different climatic backgrounds in the period from 2008 January to 2009 December. 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 rms error (0.2"-0.4" in most cases), compared with the observation. The probability of forecast with <30% error varies between 40% and 50% for the entire-atmosphere forecast and between 30% and 50% for the free-atmosphere forecast for almost all sites, which is 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 the GFS/AXP model to simulate the effect of turbulence near ground and on a subkilometer scale. Overall, 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 a basic observing reference and has proposed its potential to gain better performance with additional efforts on model refinement.