Abstract
Early warning of severe weather caused by intense convective weather systems is challenging. To help such activities, meteorological satellites with high temporal and spatial resolution have been utilized for the monitoring of instability trends along with water vapor variation. The current study proposes a retrieval algorithm based on an artificial neural network (ANN) model to quickly and efficiently derive total precipitable water (TPW) and convective available potential energy (CAPE) from Korea’s second geostationary satellite imagery measurements (GEO-KOMPSAT-2A/Advanced Meteorological Imager (AMI)). To overcome the limitations of the traditional static (ST) learning method such as exhaustive learning, impractical, and not matching in a sequence data, we applied an ANN model with incremental (INC) learning. The INC ANN uses a dynamic dataset that begins with the existing weight information transferred from a previously learned model when new samples emerge. To prevent sudden changes in the distribution of learning data, this method uses a sliding window that moves along the data with a window of a fixed size. Through an empirical test, the update cycle and the window size of the model are set to be one day and ten days, respectively. For the preparation of learning datasets, nine infrared brightness temperatures of AMI, six dual channel differences, temporal and geographic information, and a satellite zenith angle are used as input variables, and the TPW and CAPE from ECMWF model reanalysis (ERA5) data are used as the corresponding target values over the clear-sky conditions in the Northeast Asia region for about one year. Through the accuracy tests with radiosonde observation for one year, the INC NN results demonstrate improved performance (the accuracy of TPW and CAPE decreased by approximately 26% and 26% for bias and about 13% and 12% for RMSE, respectively) when compared to the ST learning. Evaluation results using ERA5 data also reveal more stable error statistics over time and overall reduced error distribution compared with ST ANN.
Highlights
Severe weather events caused by convection—thunderstorm, lightning, heavy rainfall, hail, and convective gust—are serious threats and hazards to life and property, and building an early warning system to predict thermodynamically unstable weather systems is quite an important task to reduce the damage and risk
Various research associated with severe convective weather in the Korean peninsula suggest Total precipitable water (TPW) with more than 45 mm and strong convective available potential energy (CAPE) ranging from 1000 to 2500 J/kg are applied to forecast the localized heavy rainfall events [3,4,5]
The retrieval algorithm of TPW and CAPE based on the artificial neural network (ANN) model was developed using Advanced Meteorological Imager (AMI), a pseudo-sounding imager onboard the geostationary GK2A satellite, over Northeast Asia to monitor the pre-convective environments
Summary
Severe weather events caused by convection—thunderstorm, lightning, heavy rainfall, hail, and convective gust—are serious threats and hazards to life and property, and building an early warning system to predict thermodynamically unstable weather systems is quite an important task to reduce the damage and risk. Total precipitable water (TPW), which is vertically integrated moisture in the atmosphere and represents the distribution of water content in the atmosphere, and convective available potential energy (CAPE), indicating the degree of atmospheric instability, are used to understand the current weather conditions and Remote Sens. A more recent study identified the thermodynamic conditions for severe convective occurrences over the Khulna region during monsoon season using statistically estimated parameters (values of TPW > 50 mm and CAPE > 2500 J/kg [1]) and extreme rainfall events with more than. Various research associated with severe convective weather in the Korean peninsula suggest TPW with more than 45 mm and strong CAPE ranging from 1000 to 2500 J/kg are applied to forecast the localized heavy rainfall events [3,4,5]
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