TY - JOUR

T1 - Collecting and processing of barometric data from smartphones for potential use in numerical weather prediction data assimilation

AU - Hintz, Kasper S.

AU - Vedel, Henrik

AU - Kaas, Eigil

PY - 2019

Y1 - 2019

N2 - The potential for use of crowd-sourced data in the atmospheric sciences is vastly expanding, including observations from smartphones with barometric sensors. Smartphone pressure observations can potentially help improve numerical weather prediction and aid forecasters. In this contribution a method of collecting data from smartphones is presented, other methods are discussed and guidelines are derived from the experience. Quality control is vital when using crowd-sourced data. Screening methods aimed at smartphone pressure observations are presented. Results from previous studies, showing a substantial but long-term stable bias in combination with high relative accuracy, are confirmed. The collection of Danish smartphone pressure observations has been very successful, with over 6 million observations during a 7 week period. Case studies show that distinct weather patterns can be seen in unprocessed data. The screening method developed reduces the observational noise but filters out the majority of observations. Assimilating smartphone pressure observations in a single case study, using the 3D variational data assimilation system of the HARMONIE numerical weather prediction system, proved to decrease the bias of surface pressure in the model without increasing the root mean square error and the skill of accumulated precipitation increased. It is found that the altitude assignment of smartphones needs improvement.

AB - The potential for use of crowd-sourced data in the atmospheric sciences is vastly expanding, including observations from smartphones with barometric sensors. Smartphone pressure observations can potentially help improve numerical weather prediction and aid forecasters. In this contribution a method of collecting data from smartphones is presented, other methods are discussed and guidelines are derived from the experience. Quality control is vital when using crowd-sourced data. Screening methods aimed at smartphone pressure observations are presented. Results from previous studies, showing a substantial but long-term stable bias in combination with high relative accuracy, are confirmed. The collection of Danish smartphone pressure observations has been very successful, with over 6 million observations during a 7 week period. Case studies show that distinct weather patterns can be seen in unprocessed data. The screening method developed reduces the observational noise but filters out the majority of observations. Assimilating smartphone pressure observations in a single case study, using the 3D variational data assimilation system of the HARMONIE numerical weather prediction system, proved to decrease the bias of surface pressure in the model without increasing the root mean square error and the skill of accumulated precipitation increased. It is found that the altitude assignment of smartphones needs improvement.

KW - crowdsourcing

KW - observations

KW - smartphones

KW - surface pressure

U2 - 10.1002/met.1805

DO - 10.1002/met.1805

M3 - Journal article

AN - SCOPUS:85066907688

VL - 26

SP - 733

EP - 746

JO - Meteorological Applications

JF - Meteorological Applications

SN - 1350-4827

IS - 4

ER -