Publications

Kotarba, AZ; Huu, ZN (2022). Accuracy of Cirrus Detection by Surface-Based Human Observers. JOURNAL OF CLIMATE, 35(11), 3227-3241.

Abstract
The longest cirrus time series are ground-based, visual observations captured by human observers [synoptic observations (SYNOP)]. However, their reliability is impacted by an unfavorable viewing geometry (cloud overlap) and misclassification due to low cloud optical thickness, especially at night. For the very first time, this study assigns a quantitative value to uncertainty. We validate 15 years of SYNOP observations (2006-20) against data from the cloud lidar flown on board the Cloud-Aerosol Lidar and Infrared Pathfinder (CALIPSO) spacecraft. We develop a dedicated method to match SYNOP reports (with a hemispherical field of view) with lidar samples (along-track profiles). Our evaluation of the human eye's sensitivity to cirrus revealed that it is moderate, at best. In perfect conditions (daytime with no mid/low-level clouds) the probability of correct detection was 44%-83% (Cohen's kappa coefficient < 0.6), and this fell to 24%-42% (kappa < 0.3) at night. Lunar illumination improved detection, but only when the moon's phase exceeded 50%. Cirrus optical depth had a clear impact on detection. When clouds at all levels were considered (i.e., real-life conditions), the reliability of the visual method was moderate to poor: it detected 47%-71% of cirrus (kappa < 0.45) during the day and 28%-43% (kappa < 0.2) at night and decreased with an increasing low/midlevel cloud fraction. These kappa coefficients suggest that agreement with CALIPSO data was close to random. Our findings can be directly applied to estimations of cirrus frequency/trends. Our reported probabilities of detection can serve as a benchmark for other ground-based cirrus detection methods.

DOI:
10.1175/JCLI-D-21-0430.1

ISSN:
1520-0442