The Met Office currently have ‘present weather’ sensors at their Operational Land Surface sites. These instruments try to provide a present weather code as would traditionally be reported by a human at the station. However, the instruments are somewhat problematic (e.g. suspectable to spiders) and expensive.
Surface sites contain a whole range of additional information, a range of temperature, LCBR measurements, rain gauges etc. Also available is wider information from Weather Radar and NowCasting/Recent NWP output.
This project aims to use machine learning to assimilate this data together and use it to create a ‘virtual sensor’ for present weather. There will also be scope to add additional information, for example nearby roadside camera data, and proposing and trialing new instruments such as cameras.
Future Met Office efforts would then be required to operationalise any output from the project, but has the capacity to reduce the cost, complexity and maintenance of our Land Surface Stations whilst maintaining or even enhancing the ‘Present Weather’ capability.
This project represents the first step of developing a ‘Virtual Sensors’ methodology and there is potential to extend the approach to other problematic parameters (e.g. visibility and grass temperature).
Figure 1: A Met Office present weather sensor deployed in a surface enclosure.
This project is not suitable for CASE funding
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The project will require a review of the state-of-the-art for present weather monitoring both in terms of literature and data availability. The curation of available data will be a large component of the early methodology. This will be achieved by both primary research and interviews with the data experts and will be informed by the choice of machine learning techniques to be explored.
The virtual sensing methodology is a new approach and will require a good understanding of data science techniques to underpin algorithm selection and data partitioning (i.e testing, training and validation). Machine learning is proposed, alternative statistical methods and intercomparison approaches may also be explored.
Present weather is the initial target for this PhD, the main aim of the project is to demonstrate the feasibility of the virtual sensing approach to extend observational capacity across the UK and beyond.
DRs will be awarded CENTA Training Credits (CTCs) for participation in CENTA-provided and ‘free choice’ external training. One CTC can be earned per 3 hours training, and DRs must accrue 100 CTCs across the three and a half years of their PhD.
Training will be provided on methods of observations, including access to the Met Office College courses on such. This will also include infield training with visits to the Camborne Observatory and other sites as required.
Development of trials training, and interaction with operational change management.
If required, it is also expected that the DR would participate in data science / ML training.
The University of Birmingham is a Met Office Academic Partnership University, this is the first CASE studentship related to the Advancing Observations Theme as introduced by the Met Office Research and Innovation Strategy.
Journal:
Zhang, Y., Wang, Y., Zhu, Y., Yang, L., Ge, L. and Luo, C., 2022. Visibility prediction based on machine learning algorithms. Atmosphere, 13(7), p.1125.
Ortega, L., Otero, L.D. and Otero, C., 2019, April. Application of machine learning algorithms for visibility classification. In 2019 IEEE International Systems Conference (SysCon) (pp. 1-5). IEEE.
Ellis, R.A., Sandford, A.P., Jones, G.E., Richards, J., Petzing, J. and Coupland, J.M., 2006. New laser technology to determine present weather parameters. Measurement science and technology, 17(7), p.1715.
Merenti-Välimäki, H.L., Lönnqvist, J. and Laininen, P., 2001. Present weather: comparing human observations and one type of automated sensor. Meteorological Applications, 8(4), pp.491-496.
For any enquiries related to this project please contact Lee Chapman, [email protected].
To apply to this project:
Applications must be submitted by 23:59 GMT on Wednesday 8th January 2025.