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Accuracy of Cirrus Detection by Surface-Based Human Observers

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  • 1 a Space Research Centre, Polish Academy of Sciences (CBK PAN), Warsaw, Poland
  • | 2 b Institute of Geography and Spatial Management, Jagiellonian University, Krakow, Poland
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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.

Significance Statement

Cirrus clouds heat the atmosphere, so any increase in their frequency will contribute to climate warming. The longest cirrus time series (including the presatellite era) are surface-based detections by a human observer at a meteorological station. Our study is the first to quantitatively evaluate the reliability of these observations. Our results show that, because of the viewing geometry (cloud overlap) and human eye sensitivity, reliability ranges from moderate at best to very low. Nighttime detections are especially unreliable, as well as those in the presence of low/midlevel cloud. Cirrus frequencies and trends calculated from visual observations should, thus, be considered with caution.

© 2022 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Andrzej Z. Kotarba, akotarba@cbk.waw.pl

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.

Significance Statement

Cirrus clouds heat the atmosphere, so any increase in their frequency will contribute to climate warming. The longest cirrus time series (including the presatellite era) are surface-based detections by a human observer at a meteorological station. Our study is the first to quantitatively evaluate the reliability of these observations. Our results show that, because of the viewing geometry (cloud overlap) and human eye sensitivity, reliability ranges from moderate at best to very low. Nighttime detections are especially unreliable, as well as those in the presence of low/midlevel cloud. Cirrus frequencies and trends calculated from visual observations should, thus, be considered with caution.

© 2022 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Andrzej Z. Kotarba, akotarba@cbk.waw.pl

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