Details:

Journal Title:
Remote Sensing
Personal Author:
Liu, Zijing ; Min, Min ; Li, Jun ; Sun, Fenglin ; Di, Di ; ... More +
Liu, Zijing ; Min, Min ; Li, Jun ; Sun, Fenglin ; Di, Di ; Ai, Yufei ; Li, Zhenglong ; Qin, Danyu ; Li, Guicai ; Lin, Yinjing ; Zhang, Xiaolin Less -
NOAA Program & Office:
CIMSS (Cooperative Institute for Meteorological Satellite Studies)
Description:
Accurate and prior identification of local severe storm systems in pre-convection environments using geostationary satellite imagery measurements is a challenging task. Methodologies for “convective initiation” identification have already been developed and explored for operational nowcasting applications; however, warning of such convective systems using the new generation of geostationary satellite imagery measurements in pre-convection environments is still not well studied. In this investigation, the Random Forest (RF) machine learning algorithm is used to develop a predictive statistical model for tracking and identifying three different types of convective storm systems (weak, medium, and severe) over East Asia by combining spatially-temporally collocated Himawari-8 (H08) measurements and Numerical Weather Prediction (NWP) forecast data. The Global Precipitation Measurement (GPM) gridded product is used as a benchmark to train the predictive models based on a sample-balance technique which can adjust or balance the samples of three different convection types to avoid over-fitting any type of dataset. Variables such as brightness temperatures (BTs) from H08 water vapor absorption bands (6.2 μm, 6.9 μm and 7.3 μm) and Total Precipitable Water (TPW) from NWP show relatively high ranks in the predictive model training. These sensitive variables are closely associated with convectively dominated precipitation areas, indicating the importance of predictors from both H08 and NWP data. The final optimal RF model is achieved with an accuracy of 0.79 for classification of all convective storm systems, while the Probability of Detection (POD) of this model for severe and medium convections can reach 0.66 and 0.70, respectively. Two typical sudden convective storm cases in the warm season of 2018 tracked by this algorithm are described, and results indicate that the H08 and NWP based statistical model using the RF algorithm is capable of capturing local burst convective storm systems about 1–2 h earlier than the outbreak of heavy rainfall.
Keywords:
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Convection (Meteorology) Remote Sensing
Source:
Remote Sens. 2019, 11(4), 383
DOI:
https://doi.org/10.3390/rs11040383
Document Type:
Journal Article
Funding:
Grant no. NA15NES4320001
Rights Information:
CC BY
Compliance:
Submitted
Main Document Checksum:
[+]
urn:sha256:09c1c746f6747c5b79f5fd0f196f8a229936cdb1ff088f1403c8618565b5a8df
Download URL:
https://repository.library.noaa.gov/view/noaa/27347/noaa_27347_DS1.pdf
File Type:

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