Characterizing Vertical Particle Structure of Precipitating Cloud System From Multiplatform Measurements of A‐Train Constellation

Abstract

Multiplatform measurements of the active and passive instruments from A‐Train were employed to observationally characterize particle structures over a spectrum of precipitating clouds from shallow cumulus to deep convection. Radar reflectivity profiles were composited as a function of temperature, with particle type superimposed to depict how storm regimes exert different particle habit structures. The deep convective system was found to have a relatively simple structure, which is dominated by randomly oriented ice, followed by snow and rain at the bottom, whereas the shallow cold system consisting high cloud tops with precipitation far below contains various hydrometeors, such as ice plates and drizzles. The deep convective system was further analyzed to demonstrate how the vertical microphysical structure tends to systematically transition from nonprecipitating to precipitating characteristics with differing cloud top buoyancies indicative of the “life stage” of convective development. The analysis offers a link between dynamical characteristics of convective systems and their inner hydrometeors structures.

Real-Time Visualization Optimization Management Simulation of Big Data Stream on Industrial Heritage Cloud Platform

“…e LSTM neural network algorithm is used to predict the bus time from the starting point to the target location [18]. A prediction model based on RNN for obtaining information is proposed, which can achieve high accuracy prediction for messages [19]. Although the LSTM algorithm overcomes the problems of RNN gradient vanishing and gradient explosion, its structure is too complex and the model parameters are too many, so the training time is doubled.…”

Vertical Structure of Tropical Deep Convective Systems at Different Life Stages From CloudSat Observations

“…Then, the of each deep convective cloud profile within the convective pillar is averaged as to represent the cloud top buoyancy of the whole DCS. Based on the obtained , we categorize the DCS into three life stages following Kikuchi and Suzuki (2019): (a) “developing stage,” ≥ 3.0°C; (b) “mature stage,” −3.0°C ≤ < 3.0°C; and (c) “dissipating stage,” < −3.0°C. These three stages account for approximately 34%, 40%, and 26% of the total one‐convective‐pillar DCS samples, respectively.…”

“…Thus, they proposed a method to recognize the different statuses of CloudSat‐observed convective clouds, which is mainly determined by the buoyancy at the cloud top. Subsequently, Kikuchi and Suzuki (2019) recently modified the method and separated convective clouds into developing, mature, and dissipating stages, studying the particle types of precipitating cloud systems. These ideas inspire us to study the vertical structures of convective clouds associated with connected anvil clouds (i.e., the whole DCS) at different life stages from CloudSat observations.…”

Southern Ocean Surface Temperatures and Cloud Biases in Climate Models Connected to the Representation of Glacial Deep Ocean Circulation

“…The temperature dependence of the cloud phase ratio is derived from the total number of water cloud occurrences divided by the total number of water and ice cloud occurrences (Hirakata et al 2014). The global The dataset has been used for atmospheric studies such as cloud microphysics analysis (Okamoto et al 2010;Kikuchi and Suzuki 2019), satellite algorithm development and evaluation (Cesana et al 2016;Kikuchi et al 2017) and model evaluation (Watanabe et al 2010). The data show a substantial amount of supercooled cloud liquid water in the moderately cold temperature range from 2308 to 2158C, which is consistent with other studies (Fig.…”