Error Correction of Meteorological Data Obtained with Mini-AWSs Based on Machine Learning

Abstract

Severe weather events occur more frequently due to climate change; therefore, accurate weather forecasts are necessary, in addition to the development of numerical weather prediction (NWP) of the past several decades. A method to improve the accuracy of weather forecasts based on NWP is the collection of more meteorological data by reducing the observation interval. However, in many areas, it is economically and locally difficult to collect observation data by installing automatic weather stations (AWSs). We developed a Mini-AWS, much smaller than AWSs, to complement the shortcomings of AWSs. The installation and maintenance costs of Mini-AWSs are lower than those of AWSs; Mini-AWSs have fewer spatial constraints with respect to the installation than AWSs. However, it is necessary to correct the data collected with Mini-AWSs because they might be affected by the external environment depending on the installation area. In this paper, we propose a novel error correction of atmospheric pressure data observed with a Mini-AWS based on machine learning. Using the proposed method, we obtained corrected atmospheric pressure data, reaching the standard of the World Meteorological Organization (WMO; ±0.1 hPa), and confirmed the potential of corrected atmospheric pressure data as an auxiliary resource for AWSs.

A General Overview of the Risk-Reduction Strategies for Floods and Droughts

“…The common approach is to adaptively correct remote sensing-based data with ground-based observations [108,109]. Recent studies have used data-driven or data-mining techniques to identify possible data bias or extremes and further correct them simultaneously to meet the needs of disaster response [110][111][112]. Finally, because of rapid progress in the internet of things (IoT), sensors and associated components are becoming cheaper than ever before.…”

Spatiotemporal Approaches for Quality Control and Error Correction of Atmospheric Data through Machine Learning

“…In addition, interpolation is used to accordingly replace error data with more accurate data as needed. To interpolate more accurately than traditional methods such as linear interpolation, there is an approach based on machine learning [3][4][5][6][7][8][9]. e studies on time series data using machine learning have been largely conducted for forecasting weather and stock price [10][11][12][13][14].…”

“…Using five methods, including support vector machine (SVM) and random forest, they were able to predict fires through data mining applied to meteorological data acquired from an observation post in the northeast region of Portugal. Spatial correction through machine learning is a subject of active research [4][5][6]. Kim et al [4] proposed a correction method for air pressure data acquired from microelectromechanical pressure sensors embedded in smartphones.…”

“…For the correction method, linear regression was used and correction was performed based on QC conducted on spatial location, temperature, humidity, and individual users. To overcome the shortcomings of standard automatic weather stations (AWSs), Ha et al [5] constructed a small mobile mini-AWS. Mini-AWSs have advantageous installation, operation, and maintenance costs, as well as reduced installation space requirements.…”

Detecting Anomalies in Meteorological Data Using Support Vector Regression

“…Abnormal data identified during the quality control process are examined thoroughly by an expert and may become the subject of further research. If an abnormality is detected due to an error in the measurement process, it is necessary to replace the observed value with a corrected value [8][9][10][11]. Quality control of meteorological observations can also be regarded as anomaly detection [12] because anomalous values are of substantial interest to researchers.…”