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Remote Sens. 2019, 11(4), 383; https://doi.org/10.3390/rs11040383

Local Severe Storm Tracking and Warning in Pre-Convection Stage from the New Generation Geostationary Weather Satellite Measurements

1
Chinese Academy of Meteorological Sciences, China Meteorological Administration, Beijing 100081, China
2
Key Laboratory of Radiometric Calibration and Validation for Environmental Satellites (LRCVES/CMA), National Satellite Meteorological Center, China Meteorological Administration (NSMC/CMA), Beijing 100081, China
3
Cooperative Institute for Meteorological Satellite Study (CIMSS), University of Wisconsin-Madison, Madison, WI 53706, USA
4
National Meteorological Center, China Meteorological Administration (NMC/CMA), Beijing 100081, China
*
Authors to whom correspondence should be addressed.
Received: 25 December 2018 / Revised: 31 January 2019 / Accepted: 5 February 2019 / Published: 13 February 2019
(This article belongs to the Special Issue Weather Forecasting and Modeling Using Satellite Data)
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Abstract

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. View Full-Text
Keywords: convective storm; geostationary satellite; numerical weather prediction; nowcasting; random forests convective storm; geostationary satellite; numerical weather prediction; nowcasting; random forests
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
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Liu, Z.; Min, M.; Li, J.; Sun, F.; Di, D.; Ai, Y.; Li, Z.; Qin, D.; Li, G.; Lin, Y.; Zhang, X. Local Severe Storm Tracking and Warning in Pre-Convection Stage from the New Generation Geostationary Weather Satellite Measurements. Remote Sens. 2019, 11, 383.

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