Macrophysical properties of specific cloud types from radiosonde and surface active remote sensing measurements over the ARM Southern Great Plains site

Jin-Qiang Zhang,Hong-Bin Chen

doi:10.1080/16742834.2017.1239505

Abstract

AbstractAccurate observation of clouds is challenging because of the high variability and complexity of cloud types and occurrences. By using the long-term cloud data collected during the ARM program at the Southern Great Plains central facility during 2001–2010, the consistencies and differences in the macrophysical properties of clouds between radiosonde and ground-based active remote sensing are quantitatively evaluated according to six cloud types: low; mid-low (ML); high-mid-low; mid; high-mid (HM); and high. A similar variability trend is exhibited by the radiosonde and surface observations for the cloud fractions of the six cloud types. However, the magnitudes of the differences between the two methods are different among the six cloud types, with the largest difference seen in the high clouds. The distribution of the cloud-base height of the ML, mid, and HM clouds agrees in both methods, whereas large differences are seen in the cloud-top height for the ML and high clouds. The cloud thickness vari...

Highlights

Clouds play a crucial role in the earth–atmosphere system owing to their effect on the energy budget and hydrological cycle (Webster 1994)
By using data collected over the Southern Great Plains (SGP) ARM site between 2001 and 2010, the macrophysical properties of clouds in terms of cloud fraction, CBH, CTH, and cloud thickness (CT) were compared using radiosonde and ground-based active remote sensing measurements
The results showed that the most high clouds and the least HML clouds are detectable in both the radiosonde and Active Remote Sensing of Cloud (ARSCL) data

Summary

Introduction

Clouds play a crucial role in the earth–atmosphere system owing to their effect on the energy budget and hydrological cycle (Webster 1994). Space-borne passive and active instruments can collect cloud data on the global scale along a satellite track (Weisz et al 2007; Marchand et al 2008). Ground-based instruments collect temporal and long-term cloud data at regional scales. As part of the ARM program of the U.S Department of Energy, surface remote sensing instruments have been deployed for over two decades at the Southern Great Plains (SGP) central facility near Lamont, Oklahoma, which is characterized by a wide variability of cloud types. By combining the data collected by groundbased radar, ceilometers, and lidar, vertical cloud profiles have been generated over this site (Clothiaux et al 2000; Kollias, Tselioudis, and Albrecht 2007) and used in numerous studies (Xi et al 2010; Qian et al 2012; Yoo et al 2013).

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