Development of cloud-type classification algorithm in conjunction with LASSO project
Poster PDF
Authors
Kyo-Sun Sunny Lim — Korean Atomic Energy Research Institute (KAERI)
Laura Dian Riihimaki — CIRES | NOAA ESRL GML
Jessica M Kleiss — Lewis and Clark College
Larry Berg — Pacific Northwest National Laboratory
Yunyan Zhang — Lawrence Livermore National Laboratory
Yan Shi — Pacific Northwest National Laboratory
Category
ARM next generation – Megasite and LES activities
Description
Various cloud types have different radiative forcing, thus an accurate cloud-type classification is an important task to understand the role of clouds on the energy budget, and the regional/global hydrological cycle. In order to provide a long-term database of cloud types over the Atmospheric Radiation Measurement (ARM) Southern Great Plains (SGP) site, we develop a cloud-type classification algorithm based on typical values of the cloud top, cloud base, and the physical thickness of cloud layers, which vary according to the different cloud types, using the cloud layer information from Active Remote Sensing of Clouds (ARSCL).
Low clouds determined using the cloud-type classification algorithm can be further categorized into different cloud types using cloud fraction information from Total Sky Imager (TSI) and ceilometer. We automate the selection of shallow cumulus periods during spring and summer to complement the LES ARM Symbiotic Simulation and Observation (LASSO) project. The automatically identified shallow cumulus periods have been compared with manually selected shallow cumulus periods (Berg and Kassianov, 2008; Zhang and Klein, 2013) and show the promising results of 70 percent agreement, 20 percent false-positives, and 10 percent missed.
