Version 1
: Received: 30 October 2020 / Approved: 30 October 2020 / Online: 30 October 2020 (14:54:06 CET)
How to cite:
Tsay, J.; Kao, K.; Chao, C.; Chang, Y. Deep Learning for Satellite Rainfall Retrieval Using Himawari-8 Multiple Spectral Channels. Preprints2020, 2020100648. https://doi.org/10.20944/preprints202010.0648.v1
Tsay, J.; Kao, K.; Chao, C.; Chang, Y. Deep Learning for Satellite Rainfall Retrieval Using Himawari-8 Multiple Spectral Channels. Preprints 2020, 2020100648. https://doi.org/10.20944/preprints202010.0648.v1
Tsay, J.; Kao, K.; Chao, C.; Chang, Y. Deep Learning for Satellite Rainfall Retrieval Using Himawari-8 Multiple Spectral Channels. Preprints2020, 2020100648. https://doi.org/10.20944/preprints202010.0648.v1
APA Style
Tsay, J., Kao, K., Chao, C., & Chang, Y. (2020). Deep Learning for Satellite Rainfall Retrieval Using Himawari-8 Multiple Spectral Channels. Preprints. https://doi.org/10.20944/preprints202010.0648.v1
Chicago/Turabian Style
Tsay, J., Chun-Chieh Chao and Yu-Cheng Chang. 2020 "Deep Learning for Satellite Rainfall Retrieval Using Himawari-8 Multiple Spectral Channels" Preprints. https://doi.org/10.20944/preprints202010.0648.v1
Abstract
Rainfall retrieval using geostationary satellites provides critical means to the monitoring of extreme rainfall events. Using the relatively new Himawari 8 meteorological satellite with three times more channels than its predecessors, the deep learning framework of “convolutional autoencoder” (CAE) was applied to the extraction of cloud and precipitation features. The CAE method was incorporated into the Convolution Neural Network version of the PERSIANN precipitation retrieval that uses GOES satellites. By applying the CAE technique with the addition of Residual Blocks and other modifications of deep learning architecture, the presented derivation of PERSIANN operated at the Central Weather Bureau of Taiwan (referred to as PERSIANN-CWB) expands four extra convolution layers to fully use Himawari 8’s infrared and water vapor channels, while preventing degradation of accuracy caused by the deeper network. The development of PERSIANN-CWB was trained over Taiwan for its diverse weather systems and localized rainfall features, and the evaluation reveals an overall improvement from its CNN counterpart and superior performance over all other rainfall retrievals analyzed. Limitation of this model was found in the derivation of typhoon rainfall, an area requiring further research.
Keywords
satellite rainfall retrieval; deep learning; satellite meteorology
Subject
Environmental and Earth Sciences, Atmospheric Science and Meteorology
Copyright:
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.
