Open AccessError Characteristics of Ceilometer-Based Observations of Cloud Amount
Author(s) -
Timothy J. Wagner,
Jessica M. Kleiss
Publication year - 2016
Publication title -
journal of atmospheric and oceanic technology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.774
H-Index - 124
eISSN - 1520-0426
pISSN - 0739-0572
DOI - 10.1175/jtech-d-15-0258.1
Subject(s) - ceilometer , overcast , environmental science , cloud cover , lidar , sky , remote sensing , meteorology , cloud fraction , homogeneity (statistics) , cloud base , cirrus , magnitude (astronomy) , cloud computing , geography , statistics , computer science , mathematics , physics , astronomy , operating system
Ceilometer observations of cloud cover are an important component of the automated weather observation network. However, the accuracy of its measurements of cloud amount is impacted by the limited vertical range and areal extent of its observations. A multiyear collocated dataset of observations from a laser ceilometer, a total sky imager (TSI), and a micropulse lidar (MPL) at the Atmospheric Radiation Measurement (ARM) Southern Great Plains (SGP) Central Facility is used to simulate the observations of operational ceilometers and to analyze the magnitude of the errors associated with ceilometer-based observations of cloud amount. The limited areal coverage of ceilometers results in error when skies are heterogeneous, but these errors are small compared to those caused by the limited vertical range: observations of clear sky or few clouds are often in error as the instrument cannot detect the presence of upper-level clouds. The varying quantities of upper-level clouds mean that errors are diurnally and seasonally dependent, with the greatest error at the SGP site happening in the morning and summer, respectively. Overall, the spatial homogeneity and low base of stratus clouds means that ceilometer-based observations of overcast skies are the most accurate, with a root-mean-square error of cloud fraction in overcast conditions an order of magnitude lower than for the dataset as a whole.