For the purpose of improving precipitation estimation, we have developed a deep learning method for merging different modalities of measurements. In a previous paper, we proposed a method that can merge rain gauge measurements with ground-based radar composite and thermal infrared satellite imagery . In this paper, we study the benefits of also using other satellite channels that are not infrared. Because non-infrared channels are not visible during the night, we also study the difference in performance during the day and the night.
Moraux, A, Dewitte, S & Munteanu, A 2021, THE BENEFITS OF ADDITIONAL SATELLITE CHANNELS FOR A MULTIMODAL DEEP LEARNING METHOD FOR PRECIPITATION ESTIMATION. in IEEE M2GARSS 2022. pp. 153-156.
Moraux, A., Dewitte, S., & Munteanu, A. (Accepted/In press). THE BENEFITS OF ADDITIONAL SATELLITE CHANNELS FOR A MULTIMODAL DEEP LEARNING METHOD FOR PRECIPITATION ESTIMATION. In IEEE M2GARSS 2022 (pp. 153-156)
@inproceedings{58d175803f3b487c8fb73a1d38a4fbcd,
title = "THE BENEFITS OF ADDITIONAL SATELLITE CHANNELS FOR A MULTIMODAL DEEP LEARNING METHOD FOR PRECIPITATION ESTIMATION",
abstract = "For the purpose of improving precipitation estimation, we have developed a deep learning method for merging different modalities of measurements. In a previous paper, we proposed a method that can merge rain gauge measurements with ground-based radar composite and thermal infrared satellite imagery . In this paper, we study the benefits of also using other satellite channels that are not infrared. Because non-infrared channels are not visible during the night, we also study the difference in performance during the day and the night.",
author = "Arthur Moraux and Steven Dewitte and Adrian Munteanu",
year = "2021",
month = dec,
day = "2",
language = "English",
pages = "153--156",
booktitle = "IEEE M2GARSS 2022",
}