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Tropical Overshooting Cloud-Top Height Retrieval from Himawari-8 Imagery Based on Random Forest Model

Authors :
Gaoyun Wang
Hongqing Wang
Yizhou Zhuang
Qiong Wu
Siyue Chen
Haokai Kang
Source :
Atmosphere, Vol 12, Iss 2, p 173 (2021)
Publication Year :
2021
Publisher :
MDPI AG, 2021.

Abstract

Tropical overshooting convection has a strong impact on both heat budget and moisture distribution in the upper troposphere and lower stratosphere, and it can pose a great risk to aviation safety. Cloud-top height is one of the essential concerns of overshooting convection for both the climate system and the aviation weather forecast. The main purpose of our work is to verify the application of the machine learning method, taking the random forest (RF) model as an instance, in overshooting cloud-top height retrieval from Himawari-8 data. By using collocated CloudSat observations as a reference, we utilize several infrared indicators of Himawari-8 that are commonly recognized to relate to cloud-top height, along with some temporal and geographical parameters (latitude, month, satellite zenith angle, etc.), as predictors to construct and validate the model. Analysis of variable importance shows that the brightness temperature of 6.2 um acts as the dominant predictor, followed by satellite zenith angle, brightness temperature of 13.3 um, latitude, and month. In the comparison between the RF model and the traditional single-channel interpolation method, retrievals from the RF model agree well with observation with a high correlation coefficient (0.92), small RMSE (222 m), and small MAE (164 m), while these metrics from traditional single-channel interpolation method shows lower skills (0.70, 1305 m, and 1179 m). This work presents a new sight of overshooting cloud-top height retrieval based on the machine learning method.

Details

Language :
English
ISSN :
20734433
Volume :
12
Issue :
2
Database :
Directory of Open Access Journals
Journal :
Atmosphere
Publication Type :
Academic Journal
Accession number :
edsdoj.4f542eefac3a4885a464db87cbe95bcb
Document Type :
article
Full Text :
https://doi.org/10.3390/atmos12020173