Incremental Learning with Neural Network Algorithm for the Monitoring Pre-Convective Environments Using Geostationary Imager
- 주제(키워드) incremental learning , remote sensing , severe weather , total precipitable water , convective available potential energy
- 주제(기타) Environmental Sciences
- 주제(기타) Geosciences, Multidisciplinary
- 주제(기타) Remote Sensing
- 주제(기타) Imaging Science & Photographic Technology
- 설명문(일반) [Lee, Yeonjin; Ahn, Myoung-Hwan; Lee, Su-Jeong] Ewha Womans Univ, Dept Climate & Energy Syst Engn Social Econ, 52 Ewhayeodae Gil, Seoul 03760, South Korea
- 관리정보기술 faculty
- 등재 SCIE, SCOPUS
- OA유형 gold
- 발행기관 MDPI
- 발행년도 2022
- URI http://www.dcollection.net/handler/ewha/000000190209
- 본문언어 영어
- Published As https://doi.org/10.3390/rs14020387
초록/요약
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.
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