Applying machine learning methods to detect convection using Geostationary Operational Environmental Satellite-16 (GOES-16) advanced baseline imager (ABI) data
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 Satellite-17, provide high-spatial- and high-temporal-resolution data but only of cloud top properties; 1 min 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 min to an
hour. This study represents a proof of concept that artificial intelligence
(AI) is able, when given high-spatial- and high-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  µ m) and 14 (11.2  µ m) 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 a 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 in a 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.