Preprints
https://doi.org/10.5194/egusphere-2024-439
https://doi.org/10.5194/egusphere-2024-439
07 Mar 2024
 | 07 Mar 2024
Status: this preprint is open for discussion and under review for Atmospheric Measurement Techniques (AMT).

Deep learning tool: Reconstruction of long missing climate data based on multilayer perceptron (MLP)

Zhang Yan, Xu Tianxin, Zhang Chenjia, and Ma Daokun

Abstract. Long-term monitoring of climate data is significant for grasping the law and development trend of climate change and guaranteeing food security. However, some weather stations lack monitoring data for even decades. In this study, 62 years of historical monitoring data from 105 weather stations in Xinjiang were used for missing sequence prediction, validating proposed data reconstruction tool. First of all, study area was divided into three parts according to the climatic characteristics and geographical locations. A deep learning tool based on multilayer perceptron (MLP) was established to reconstruct meteorological data with three time scales (Short term, cycle and long term) and one spatio dimension as inputing, filling in long sequence blank data. By designing an end-to-end model to autonomously detect the locations of missing data and make rolling predictions, we obtained complete meteorological monitoring data of Xinjiang from 1961 to 2022. Seven kinds of parameter reconstructed include maximum temperature (Max_T), minimum temperature (Min_T), mean temperature (Ave _ T), average water vapor pressure (Ave _ WVP), relative humidity (Ave _ RH), average wind speed (10 m Ave _ WS), and sunshine duration (Sun_H). The quality of reconstructed data was evaluated by calculating correlation coefficient with the monitored sequences of nearest station. Results show that, proposed model reached satisfied average correlation coefficient for Max_T, Min_T, Ave _ T and Ave _ WVP parameters are 0.969, 0.961, 0.971 and 0.942 respectively. The average correlation coefficient of Sun_H and Ave _ RH are 0.720 and 0.789. Although it is difficult to predict extreme values, it can still capture the period and trend; the reconstruction effect of 10 m Ave _ WS is poor, with the average similarity of 0.488. Finally, we published the trained parameter files and prediction codes as a micro service on the Agricultural Smart Brain platform, which provides firstly a deep learning tool for rapid and reliable reconstruction of meteorological monitoring data.

Zhang Yan, Xu Tianxin, Zhang Chenjia, and Ma Daokun

Status: open (until 09 May 2024)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on egusphere-2024-439', Anonymous Referee #1, 20 Mar 2024 reply
Zhang Yan, Xu Tianxin, Zhang Chenjia, and Ma Daokun
Zhang Yan, Xu Tianxin, Zhang Chenjia, and Ma Daokun

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Short summary
A deep learning tool based on multilayer perceptron (MLP) is established for the meteorological data reconstruction at three time scales. Seven parameter reconstruction methods were used to validate the proposed data reconstruction tool. Finally, the trained parameter files and prediction code were released as microservices on the Agricultural Smart Brain platform, which provides the first deep learning tool for rapid and reliable reconstruction of meteorological monitoring data.