The diurnal cycle of clouds and precipitation at the ARM SGP site: Cloud radar observations and simulations from the multiscale modeling framework

Abstract

Millimeter Wavelength Cloud Radar (MMCR) data from December 1996 to December 2010, collected at the U.S. Department of Energy Atmospheric Radiation Measurement (ARM) program Southern Great Plains (SGP) site, are used to examine the diurnal cycle of hydrometeor occurrence. These data are categorized into clouds (−40 dBZe ≤ reflectivity < −10 dBZe), drizzle and light precipitation (−10 dBZe ≤ reflectivity < 10 dBZe), and heavy precipitation (reflectivity ≥ 10 dBZe). The same criteria are implemented for the observation‐equivalent reflectivity calculated by feeding outputs from a Multiscale Modeling Framework (MMF) climate model into a radar simulator. The MMF model consists of the National Center for Atmospheric Research Community Atmosphere Model with conventional cloud parameterizations replaced by a cloud‐resolving model. We find that a radar simulator combined with the simple reflectivity categories can be an effective approach for evaluating diurnal variations in model hydrometeor occurrence. It is shown that the MMF only marginally captures observed increases in the occurrence of boundary layer clouds after sunrise in spring and autumn and does not capture diurnal changes in boundary layer clouds during the summer. Above the boundary layer, the MMF captures reasonably well diurnal variations in the vertical structure of clouds and light and heavy precipitation in the summer but not in the spring.

Midlatitude Cirrus Clouds at the SACOL Site: Macrophysical Properties and Large‐Scale Atmospheric States

“…C3 is mainly concentrated in the same layers between 4 and 10 km AGL as C1, but maximizes during night and has smaller occurrence during daytime. This variation is similar to the high cloud diurnal cycle in summer season at the ARM SGP site (Zhao et al, ). However, note that C3 at the SACOL site appears more often in cold seasons.…”

The diurnal cycle of cloud profiles over land and ocean between 51° S and 51° N, seen by the CATS spaceborne lidar from the International Space Station

“…In any case, the vertically integrated nature of passive imagery means it cannot resolve the vertical variability of clouds and its diurnal cycle, which is key to better understanding the atmospheric heating rate profile (L'Ecuyer et al, 2008). By comparison, active remote sensing instruments, such as radars and lidars, document the cloud vertical distribution with great accuracy and vertical resolutions finer than 500 m. Long-running datasets from active instruments operated from ground-based sites have led to useful time series and statistics about clouds (e.g., Sassen and Benson, 2001;Hogan et al, 2003;Protat et al, 2009;Dong et al, 2010;Hoareau et al, 2013;Zhao et al, 2017). From space, Liu and Zipser (2008) were able to derive information on the cloud diurnal cycle from the spaceborne Tropical Rainfall Measuring Mission radar, launched in 1997 (Kummerow et al, 1998), but the instrument was not designed to detect clouds with accuracy.…”

Sensitivity of the Amazonian Convective Diurnal Cycle to Its Environment in Observations and Reanalysis

“…The mean state and variability of the CDC is linked to atmospheric state anomalies on various timescales (e.g., Adams et al, ; Betts et al, , ; Derbyshire et al, ; Dodson & Taylor, ; Itterly et al, ; Lintner et al, ; Taylor, , ; Zelinka & Hartmann, ; Zhang & Klein, ; Zhao et al, ). For example, Zhang and Klein () show robust relationships between preconvective environmental parameters and afternoon deep convection in the U.S. Southern Great Plains indicating that higher convective available potential energy (CAPE) is associated with a later onset time and shorter duration of precipitation, whereas higher humidity above the boundary layer leads to an earlier onset time and longer duration of precipitation due to reduced entrainment of developing cumulus clouds (Derbyshire et al, ; Zhang & Klein, ).…”

“…While many studies investigate the relationships between convection and its environment (e.g., Adams et al, 2013Adams et al, , 2015Giangrande et al, 2017;Schiro et al, 2016;Strong et al, 2005;Xie et al, 2014;Zelinka & Hartmann, 2009;Zhang & Klein, 2010), relatively few studies systematically analyze the relationship between the CDC and atmospheric conditions. The mean state and variability of the CDC is linked to atmospheric state anomalies on various timescales (e.g., Adams et al, 2017;Betts et al, 2015Betts et al, , 2017Derbyshire et al, 2004;Itterly et al, 2016;Lintner et al, 2017;Taylor, 2014aTaylor, , 2014bZelinka & Hartmann, 2009;Zhang & Klein, 2010;Zhao et al, 2017). For example, Zhang and Klein (2010) show robust relationships between preconvective environmental parameters and afternoon deep convection in the U.S. Southern Great Plains indicating that higher convective available potential energy (CAPE) is associated with a later onset time and shorter duration of precipitation, whereas higher humidity above the boundary layer leads to an earlier onset time and longer duration of precipitation due to reduced entrainment of developing cumulus clouds (Derbyshire et al, 2004;Zhang & Klein, 2010).…”