Vertical Structure of Typical Stratiform Precipitation Based on Multi-Source Remote Sensing Data

To explore the vertical structure of precipitation in eastern China, multi-source remote sensing has been deployed to Jinan, a city in eastern China. Based on the data of K-band micro-rain radar and Ka-band cloud radar, a typical stratiform cloud precipitation experienced by Jinan city in eastern China from 12:00 on April 22, 2023 to 00:00 on April 23, 2023 is analyzed in detail. The results show that: 1) Both the Micro Rain Radar and the cloud radar can observe the bright band at the altitude of about 2.4 km, and the evolution of the bright band with time and height can be clearly observed. 2) The particles are mainly mixed phase in the bright band, and the particles above the bright band is mainly solid, such as snowflakes, while they are mainly liquid below the bright band, such as raindrops. 3) The characteristics of raindrop size distribution below the bright band show that the evaporation process in the early precipitation period is greater than that in the mature period, and the collisional coalescence in the mature period is more active. 4) Cloud radar can infer near-ground collisional coalescence and other microphysical processes from the radar reflectivity factor, which is consistent with the evolution of raindrop size distribution of Micro Rain Radar at different heights, so the synchronous observation of radar with different wavelengths can better show the microphysical processes of precipitation.

Authors

Institutions

Publication Details

Published
2024-12-20
DOI
https://doi.org/10.1109/icssip63203.2024.11012508
Primary Topic
Cryospheric studies and observations
Type
article
Field-Weighted Citation Impact
0.00

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Vertical Structure of Typical Stratiform Precipitation Based on Multi-Source Remote Sensing Data

Jun Wang, Hong Wang, Weiqiang Zhu
Cryospheric studies and observations
article

Vertical Structure of Typical Stratiform Precipitation Based on Multi-Source Remote Sensing Data

Jun Wang, Hong Wang, Weiqiang Zhu
article en

Abstract

To explore the vertical structure of precipitation in eastern China, multi-source remote sensing has been deployed to Jinan, a city in eastern China. Based on the data of K-band micro-rain radar and Ka-band cloud radar, a typical stratiform cloud precipitation experienced by Jinan city in eastern China from 12:00 on April 22, 2023 to 00:00 on April 23, 2023 is analyzed in detail. The results show that: 1) Both the Micro Rain Radar and the cloud radar can observe the bright band at the altitude of about 2.4 km, and the evolution of the bright band with time and height can be clearly observed. 2) The particles are mainly mixed phase in the bright band, and the particles above the bright band is mainly solid, such as snowflakes, while they are mainly liquid below the bright band, such as raindrops. 3) The characteristics of raindrop size distribution below the bright band show that the evaporation process in the early precipitation period is greater than that in the mature period, and the collisional coalescence in the mature period is more active. 4) Cloud radar can infer near-ground collisional coalescence and other microphysical processes from the radar reflectivity factor, which is consistent with the evolution of raindrop size distribution of Micro Rain Radar at different heights, so the synchronous observation of radar with different wavelengths can better show the microphysical processes of precipitation.

Shandong Province Meteorological Bureau (CN)
China Meteorological Administration
Climate action
Openalex Percentile: Top 70%
Cryospheric studies and observations
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.