A New Machine Learning Algorithm for Numerical Prediction of Near-Earth Environment Sensors along the Inland of East Antarctica

Yuchen Wang,Xueyuan Tang,Xiaomin Chang,Wangxiao Yang,Jingxue Guo,Yinke Dou,Minghu Ding

doi:10.3390/s21030755

Abstract

Accurate short-term small-area meteorological forecasts are essential to ensure the safety of operations and equipment operations in the Antarctic interior. This study proposes a deep learning-based multi-input neural network model to address this problem. The newly proposed model is predicted by combining a stacked autoencoder and a long- and short-term memory network. The self-stacking autoencoder maximises the features and removes redundancy from the target weather station’s sensor data and extracts temporal features from the sensor data using a long- and short-term memory network. The proposed new model evaluates the prediction performance and generalisation capability at four observation sites at different East Antarctic latitudes (including the Antarctic maximum and the coastal region). The performance of five deep learning networks is compared through five evaluation metrics, and the optimal form of input combination is discussed. The results show that the prediction capability of the model outperforms the other models. It provides a new method for short-term meteorological prediction in a small inland Antarctic region.

Highlights

Research and logistical activities in and around Antarctica are heavily dependent on environmental forecasting systems’ reliable forecasts
The set of parameters includes the neighbourhood of five neural network parameters
This study aims to create a new neural network model to be applied to future shortterm parameter predictions at Antarctic multi-sensor weather stations to achieve accurate and efficient camp weather predictions

Summary

Introduction

Research and logistical activities in and around Antarctica are heavily dependent on environmental forecasting systems’ reliable forecasts. The establishment of an integrated multi-sensor environmental prediction system is an urgent task when conducting scientific research activities and station area construction, especially in areas where local weather forecasts are lacking. Prediction of wind speed and temperature has been an issue of particular interest. Due to the unique geography of East Antarctica, real-time forecasting is difficult in small areas. The existing meteorological stations and sensor data processing methods are mostly numerical models. The Australian Bureau of Meteorology uses the Community Climate System Simulation Global (ACCESS-G) Characteristic Numerical Atmospheric Weather Prediction (NWP) suite for Antarctic weather prediction

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