EGU24-3905, updated on 08 Mar 2024
https://doi.org/10.5194/egusphere-egu24-3905
EGU General Assembly 2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.

Performing Quality Control on Meteorological Data Using Machine Learning Techniques

Teresa Spohn1, John O’Donoghue2, Kevin Horan3, Tim Charnecki1, Conor Lally1, and Merlin Haslam1
Teresa Spohn et al.
  • 1Met Eireann, Observations, Dublin, Ireland (teresa.spohn@met.ie)
  • 2University of Limerick, Limerick, Ireland
  • 3Maynooth University, Maynooth, Ireland

Quality control (QC) on data has historically been a tedious and time-consuming task. With the currently available computer processing power and machine learning algorithms, it is possible to make QC far faster and more efficient, providing high-quality data in near real time to end users. Many organisations are already using such systems with great success, although the rapid expansion of machine learning continues to open new avenues for improvement. The aim of this project is to create a QC system for Met Eireann, the Irish Meteorological Office, which incorporates the latest machine learning techniques, combined with expert human supervision, to produce the highest possible quality meteorological data.

Presented here are the ideas and concepts we intend to implement to create the QC system, showing the results of the initial trials on air temperature data. The project is still in the earliest stages of development and will benefit from input and feedback from others with experience working on similar projects.

How to cite: Spohn, T., O’Donoghue, J., Horan, K., Charnecki, T., Lally, C., and Haslam, M.: Performing Quality Control on Meteorological Data Using Machine Learning Techniques, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-3905, https://doi.org/10.5194/egusphere-egu24-3905, 2024.