Correcting Air-Pressure Data Collected by MEMS Sensors in Smartphones

Abstract

We present a novel correction method for air-pressure data collected by microelectromechanical pressure sensors embedded in Android-based smartphones, in order to render them usable as meteorological data. The first step of the proposed correction method involves removing the mechanically derived outliers existing beyond the physical limits and those existing outside 3σ, as well as a reduction to the mean sea level pressure using the altitude data from digital elevation models. The second correction step involves classifying data by location and linear-regression analysis utilizing the temperature and humidity sensed by the smartphone to reduce correction errors by performing the analysis according to personalized settings. Air-pressure data obtained from smartphones is subject to several influential factors, depending on the users’ external environment. However, once corrected for spatial location, temperature, and humidity and for individual users after a comprehensive quality control, the corrected air-pressure data was highly reliable as an auxiliary resource for automatic weather stations.

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

“…Lee et al [15] corrected abnormal meteorological data using machine learning and obtained better results in comparison with those obtained using traditional interpolation methods. Earlier studies on the correction of atmospheric smartphone data conducted by the present research team [16,17] also yielded results within the standard error. In this study, atmospheric pressure data were collected with Mini-AWSs, preprocessed, and errorcorrected based on machine learning using the atmospheric pressure measured at the nearest AWS as the reference value.…”

Collecting and processing of barometric data from smartphones for potential use in numerical weather prediction data assimilation

“…However, one disadvantage is that it is time-consuming and, with a few exceptions, the scientific community is in general not prepared for advertising its own apps and keeping the conversion rate and retention rate high. This is most evident when comparing Kim et al (2015) where only 11,000 observations per day on average were collected over 240 days. Here, the SMAPS as described was included in an existing widely used app, resulting in a high number of pressure observations.…”

“…The studies of Kim et al (2015) and McNicholas and Mass (2018) both obtained SPOs through a dedicated app for the purpose. The great advantage of their approach is the comfort of being able to tune parameters.…”

“…In this study, measurements from a period of 7 s were used to calculate the mean, which was then sent to the database. It is not clear whether Kim et al (2015) did any averaging. Madaus and Mass (2017) used 15-40 s. However, in their case data were collected in the background every hour by default, while in our case the barometer was only accessed when the app was in use.…”

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.…”