A Snowfall Detection Algorithm for Fengyun-3D Microwave Sounders with Differentiated Atmospheric Temperature Conditions

Precipitation in different phases has varying effects on runoff. However, monitoring surface snowfall poses a significant challenge, highlighting the importance of developing a snowfall detection algorithm. The objective of this study is develop a snowfall detection algorithm for the Microwave Tempe...

Full description

Saved in:
Bibliographic Details
Published inWater (Basel) Vol. 15; no. 13; p. 2315
Main Authors Ji, Qingwen, Ma, Ziqiang, Xu, Jintao, Yan, Songkun, Li, Xiaoqing
Format Journal Article
LanguageEnglish
Published Basel MDPI AG 01.07.2023
Subjects
Algorithms
Atmospheric temperature
Brightness temperature
Classification
Datasets
Decision making
Decision trees
Fengyun-3D
Humidity
Hydrologic data
Machine learning
Meteorological satellites
MWHS
MWTS
Precipitation
Precipitation (Meteorology)
Rain
Regression analysis
Snow
Snowfall
snowfall detection
Spatial distribution
Online AccessGet full text

Cover

More Information
Summary:Precipitation in different phases has varying effects on runoff. However, monitoring surface snowfall poses a significant challenge, highlighting the importance of developing a snowfall detection algorithm. The objective of this study is develop a snowfall detection algorithm for the Microwave Temperature Sounder-2 (MWTS-II) and the Microwave Humidity Sounder-2 (MWHS-II) onboard the FY-3D satellite while considering the differentiated atmosphere temperature conditions. The results show that: (1) The brightness temperature (TB) of MWTS Channel 3 is well-suited for pre-classifying atmospheric temperatures, and significant differences in TB distribution exist between the two pre-classification subsets. (2) Among six machine classifiers examined, the random forest classifier exhibits favorable classification performance on both the validation set (accuracy: 0.76, recall: 0.76, F1 score: 0.75) and test set (accuracy: 0.80, recall: 0.44, F1 score: 0.44). (3) The application of the snowfall detection algorithm showcases a reasonable spatial distribution and outperforms the IMERG and ERA5 snowfall data.
ISSN:2073-4441
2073-4441
DOI:10.3390/w15132315