Combining In Situ and Satellite Observations to Understand the Vertical Structure of Tropical Anvil Cloud Microphysical Properties During the TC4 Experiment

Abstract

Tropical anvil clouds have a profound impact on Earth's weather and climate. Their role in Earth's energy balance and hydrologic cycle is heavily modulated by the vertical structure of the microphysical properties for various hydrometeors in these clouds and their dependence on the ambient environmental conditions. Accurate representations of the variability and covariability of such vertical structures are key to both the satellite remote sensing of cloud and precipitation and numerical modeling of weather and climate, which remain a challenge. This study presents a new method to combine vertically resolved observations from CloudSat radar reflectivity and Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation cloud masks with probability distributions of cloud microphysical properties and the ambient atmospheric conditions from detailed in situ measurements on tropical anvils sampled during the National Aeronautics and Space Administration TC4 (Tropical Composition, Cloud and Climate Coupling) mission. We focus on the microphysical properties of the vertical distribution of ice water content, particle size distributions, and effective sizes for different hydrometeors, including ice particles and supercooled liquid droplets. Results from this method are compared with those from in situ data alone and various CloudSat/Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation cloud retrievals. The sampling limitation of the field experiment and algorithm limitations in the current retrievals is highlighted, especially for the liquid cloud particles, while a generally good agreement with ice cloud microphysical properties is seen from different methods. While the method presented in this study is applied to tropical anvil clouds observed during TC4, it can be readily employed to study a broad range of ice clouds sampled by various field campaigns.

Macro- and microphysical characteristics of snowfall and non-snowfall clouds in the West Tianshan Mountains of China based on cloud radar

“…In the past, many researchers have studied the macro-and microphysical properties of rainfall clouds and non-rainfall clouds, and the conclusions of these studies help to understand clouds and precipitation. However, previous studies have mainly focused on areas with abundant rainfall (Zhang et al 2019b;Luo et al 2009;Yi 2013;Liu et al 2015;Ma et al 2018;Cui et al 2020;Yue et al 2020;Wang et al 2018;Huo et al 2020a, b;Wu et al 2020) and there have been few studies on arid areas instead. In recent years, an increasing number of studies on precipitation and non-precipitation cloud macro-and microphysical properties have been carried out in the arid regions of China, especially in the Qinghai-Tibet Plateau (Yi 2019;Liu et al 2021;Ma et al 2018;Qiu et al 2018;Zhao et al 2016Zhao et al , 2017, but less research has been carried out in Xinjiang.…”

ENTICE Satellite Orbital Simulator to Study Ice Clouds

“…Anvil clouds are defined as clouds with convective cores and spreading clouds at upper levels that look like a blacksmith's anvil (Hartmann, 2016). Competing theories exist on the formation and evolution of tropical anvil clouds, which makes anvil clouds one of the least well‐understood aspects of current atmospheric models (Yue et al., 2020). Specifically, anvil cloud duration and coverage as well as the processes that govern those factors are not well understood.…”

Validation of Ice Cloud Microphysical Properties Retrieval Using a Markov Chain Monte Carlo Algorithm

“…The distribution of macro‐ and micro‐physical cloud properties has significant influence on the atmospheric radiation transfer process (Li et al., 2013; Hong et al., 2016; Yang et al., 2015). Ice clouds properties have been used to investigate their effects on Earth's radiation budget and climate evolution, and to quantify cloud‐climate feedback (Berry & Mace, 2013; Hong et al., 2016; Masunaga & Bony, 2018; Yue et al., 2020). So far, details of the spatial and temporal evolution of ice cloud properties are still beyond understanding.…”