Next Article in Journal
Derivation of Hyperspectral Profile of Extended Pseudo Invariant Calibration Sites (EPICS) for Use in Sensor Calibration
Previous Article in Journal
Regression Tree CNN for Estimation of Ground Sampling Distance Based on Floating-Point Representation
Previous Article in Special Issue
Estimation of Vegetation Latent Heat Flux over Three Forest Sites in ChinaFLUX using Satellite Microwave Vegetation Water Content Index
Open AccessArticle

Convective/Stratiform Precipitation Classification Using Ground-Based Doppler Radar Data Based on the K-Nearest Neighbor Algorithm

Research and Development Center of Earth System Model (RDCM), College of Atmospheric Sciences, Lanzhou University, Lanzhou 730000, China
*
Author to whom correspondence should be addressed.
Remote Sens. 2019, 11(19), 2277; https://doi.org/10.3390/rs11192277
Received: 2 August 2019 / Revised: 7 September 2019 / Accepted: 25 September 2019 / Published: 29 September 2019
(This article belongs to the Special Issue Remote Sensing of the Terrestrial Hydrologic Cycle)
Stratiform and convective rain types are associated with different cloud physical processes, vertical structures, thermodynamic influences and precipitation types. Distinguishing convective and stratiform systems is beneficial to meteorology research and weather forecasting. However, there is no clear boundary between stratiform and convective precipitation. In this study, a machine learning algorithm, K-nearest neighbor (KNN), is used to classify precipitation types. Six Doppler radar (WSR-98D/SA) data sets from Jiangsu, Guangzhou and Anhui Provinces in China were used as training and classification samples, and the 2A23 product of the Tropical Precipitation Measurement Mission (TRMM) was used to obtain the training labels and evaluate the classification performance. Classifying precipitation types using KNN requires three steps. First, features are selected from the radar data by comparing the range of each variable for different precipitation types. Second, the same unclassified samples are classified with different k values to choose the best-performing k. Finally, the unclassified samples are put into the KNN algorithm with the best k to classify precipitation types, and the classification performance is evaluated. Three types of cases, squall line, embedded convective and stratiform cases, are classified by KNN. The KNN method can accurately classify the location and area of stratiform and convective systems. For stratiform classifications, KNN has a 95% probability of detection, 8% false alarm rate, and 87% cumulative success index; for convective classifications, KNN yields a 78% probability of detection, a 13% false alarm rate, and a 69% cumulative success index. These results imply that KNN can correctly classify almost all stratiform precipitation and most convective precipitation types. This result suggests that KNN has great potential in classifying precipitation types. View Full-Text
Keywords: precipitation classification; K-nearest neighbor; Doppler radar; Tropical Precipitation Measurement Mission (TRMM) precipitation classification; K-nearest neighbor; Doppler radar; Tropical Precipitation Measurement Mission (TRMM)
Show Figures

Graphical abstract

MDPI and ACS Style

Yang, Z.; Liu, P.; Yang, Y. Convective/Stratiform Precipitation Classification Using Ground-Based Doppler Radar Data Based on the K-Nearest Neighbor Algorithm. Remote Sens. 2019, 11, 2277.

Show more citation formats Show less citations formats
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Back to TopTop