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Atmospheric Measurement Techniques An interactive open-access journal of the European Geosciences Union
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https://doi.org/10.5194/amt-2020-420
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/amt-2020-420
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

  14 Nov 2020

14 Nov 2020

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This preprint is currently under review for the journal AMT.

Applying machine learning methods to detect convection using GOES-16 ABI data

Yoonjin Lee1, Christian D. Kummerow1,2, and Imme Ebert-Uphoff2,3 Yoonjin Lee et al.
  • 1Department of Atmospheric Science, Colorado State University, Fort Collins, Colorado, USA
  • 2Cooperative Institute for Research in the Atmosphere, Fort Collins, Colorado, USA
  • 3Department of Electrical and Computer Engineering, Colorado State University, Fort Collins, Colorado, USA

Abstract. An ability to accurately detect convective regions is essential for initializing models for short term precipitation forecasts. Radar data are commonly used to detect convection, but radars that provide high temporal resolution data are mostly available over land and the quality of the data tends to degrade over mountainous regions. On the other hand, geostationary satellite data are available nearly anywhere and in near-real time. Current operational geostationary satellites, the Geostationary Operational Environmental Satellite-16 (GOES-16) and -17 provide high spatial and temporal resolution data, but only of cloud top properties. One-minute data, however, allow us to observe convection from visible and infrared data even without vertical information of the convective system. Existing detection algorithms using visible and infrared data look for static features of convective clouds such as overshooting top or lumpy cloud top surface, or cloud growth that occurs over periods of 30 minutes to an hour. This study represents a proof-of-concept that Artificial Intelligence (AI) is able, when given high spatial and temporal resolution data from GOES-16, to learn physical properties of convective clouds and automate the detection process.

A neural network model with convolutional layers is proposed to identify convection from the high-temporal resolution GOES-16 data. The model takes five temporal images from channel 2 (0.65 μm) and 14 (11.2 μm) as inputs and produces a map of convective regions. In order to provide products comparable to the radar products, it is trained against Multi-Radar Multi-Sensor (MRMS), which is a radar-based product that uses rather sophisticated method to classify precipitation types. Two channels from GOES-16, each related to cloud optical depth (channel 2) and cloud top height (channel 14), are expected to best represent features of convective clouds: high reflectance, lumpy cloud top surface, and low cloud top temperature. The model has correctly learned those features of convective clouds, and resulted reasonably low false alarm ratio (FAR) and high probability of detection (POD). However, FAR and POD can vary depending on the threshold, and a proper threshold needs to be chosen based on the purpose.

Yoonjin Lee et al.

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Latest update: 29 Nov 2020
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Short summary
Convective clouds are usually associated with intense rain that can cause severe damages, and thus, it is important to accurately detect convective clouds. This study develops a machine learning model that can identify convective clouds from five temporal visible and infrared images as humans can point at convective regions by finding bright and bubbling area. Results look promising when compared to radar-derived products, which are commonly used for detecting convection.
Convective clouds are usually associated with intense rain that can cause severe damages, and...
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