CASIA OpenIR  > 模式识别国家重点实验室  > 先进时空数据分析与学习
Multi-modal spatio-temporal meteorological forecasting with deep neural network
Xinbang Zhang1,2; Qizhao Jin1,2; Tingzhao Yu3; Shiming Xiang1,2; Qiuming Kuang3; Véronique Prinet1; Chunhong Pan1
Source PublicationISPRS Journal of Photogrammetry and Remote Sensing
2022-03
Pages14
Subtype已录用未发表
Abstract

Meteorological forecasting is a typical and fundamental problem in the remote sensing field. Although many brilliant forecasting methods have been developed, long-term (a few days ahead) meteorological prediction still relies on traditional Numerical Weather Prediction (NWP) that is not competent for the oncoming flood of meteorological data. To improve the forecasting ability faced with meteorological big data, this article adopts the Automated Machine Learning (AutoML) technique and proposes a deep learning framework to model the dynamics of multi-modal meteorological data along spatial and temporal dimensions. Spatially, a convolution based network is developed to extract the spatial context of multi-modal meteorological data. Considering the complex relationship between different modalities, the Neural Architecture Search (NAS) technique is introduced to automate the designing procedure of the fusion network in a purely data-driven manner. As for the temporal dimension, an encoder-decoder structure is built to exhaustively model the temporal dynamics of the embedding sequence. Specializing for the numerical sequence representation transformation, the multi-head attention module endows the proposed model with the ability to forecast future data. Generally speaking, the whole framework could be optimized with the standard back-propagation, yielding an end-to-end learning mechanism. To investigate its feasibility, the proposed model is evaluated with four typical meteorological modalities including temperature, relative humidity, and two components of wind, which are all restricted under the region whose latitude and longitude range from to N and E to E, respectively. Experiments on two datasets with different resolutions verify that deep learning is effective as an operational technique for the meteorological forecasting task.

KeywordMeterological forecasting Deep learning Neural architecture search AutoML
Indexed BySCI
Language英语
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/48955
Collection模式识别国家重点实验室_先进时空数据分析与学习
Corresponding AuthorShiming Xiang
Affiliation1.The Department of National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences
2.The School of Artificial Intelligence, University of Chinese Academy of Sciences
3.The Public Meteorological Service Center, China Meteorological Administration
First Author AffilicationChinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China
Corresponding Author AffilicationChinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China
Recommended Citation
GB/T 7714
Xinbang Zhang,Qizhao Jin,Tingzhao Yu,et al. Multi-modal spatio-temporal meteorological forecasting with deep neural network[J]. ISPRS Journal of Photogrammetry and Remote Sensing,2022:14.
APA Xinbang Zhang.,Qizhao Jin.,Tingzhao Yu.,Shiming Xiang.,Qiuming Kuang.,...&Chunhong Pan.(2022).Multi-modal spatio-temporal meteorological forecasting with deep neural network.ISPRS Journal of Photogrammetry and Remote Sensing,14.
MLA Xinbang Zhang,et al."Multi-modal spatio-temporal meteorological forecasting with deep neural network".ISPRS Journal of Photogrammetry and Remote Sensing (2022):14.
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