Asset Details
Do you wish to reserve the book?
Satellite‐Based Diagnostics of Precipitation Process in Mixed‐Phase Clouds: Extension From Warm Rain Process Statistics
Hey, we have placed the reservation for you!
By the way, why not check out events that you can attend while you pick your title.
Oops! Something went wrong.
Looks like we were not able to place the reservation. Kindly try again later.
Are you sure you want to remove the book from the shelf?
Satellite‐Based Diagnostics of Precipitation Process in Mixed‐Phase Clouds: Extension From Warm Rain Process Statistics
Do you wish to request the book?
Satellite‐Based Diagnostics of Precipitation Process in Mixed‐Phase Clouds: Extension From Warm Rain Process Statistics
Please be aware that the book you have requested cannot be checked out. If you would like to checkout this book, you can reserve another copy
We have requested the book for you!
Your request is successful and it will be processed during the Library working hours. Please check the status of your request in My Requests.
Oops! Something went wrong.
Looks like we were not able to place your request. Kindly try again later.
Journal Article
Satellite‐Based Diagnostics of Precipitation Process in Mixed‐Phase Clouds: Extension From Warm Rain Process Statistics
2024
Overview
This study proposes a methodology for analyzing the precipitation process in mixed‐phase clouds using multisensor satellite data, including radar, lidar, and imager. By leveraging a specific multivariate statistic, we elucidate the vertical microphysical structures of mixed‐phase clouds and their transitions associated with cloud particle growth and phase change. Expanding upon previous warm rain process diagnostics, we integrate cloud thermodynamic phase information from lidar and imager, representing the phase near the cloud top and column, respectively, to classify the vertical microphysical structures obtained from radar. Our global composite analysis reveals a systematic transition from non‐precipitating to precipitating characteristics with increasing ice phase fraction of the cloud column, rather than near the cloud top, and increasing cloud‐top particle size. These findings offer valuable observational references for evaluating numerical models in precipitation physics. Plain Language Summary To ensure a robust assessment of precipitation physics within global climate and numerical weather prediction models, it is imperative to diagnose the precipitation process using satellite observations on a global scale. Here, we propose a new methodology to address this requirement using multisensor satellite measurements to extend our previously developed method for warm liquid‐phase rain into more general ice‐containing mixed‐phase precipitation. For this purpose, satellite‐based information on the cloud thermodynamic phase obtained from the lidar and imager was exploited to statistically classify the vertical profile characteristics of precipitation observed by radar. The results showed that precipitation tended to occur more efficiently with an increasing ice‐phase fraction of the cloud‐column and the cloud‐top particle size. The statistics derived from observations provide a benchmark for evaluating model precipitation physics, facilitating process‐oriented assessments of numerical models. Key Points The mixed‐phase precipitation was diagnosed using a combination of multisensor satellite measurements by radar, lidar, and imager Multivariate statistics were constructed to display the radar reflectivity classified by cloud thermodynamic phase and cloud particle size The statistics show more precipitating character with higher ice‐phase fraction of the cloud optical thickness and cloud‐top particle size
