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- 01HSTRBDG8DRYQ9RZTX1HKGD58 classification C3.
- 01HSTRBDG8DRYQ9RZTX1HKGD58 date "2024".
- 01HSTRBDG8DRYQ9RZTX1HKGD58 language "eng".
- 01HSTRBDG8DRYQ9RZTX1HKGD58 type conference.
- 01HSTRBDG8DRYQ9RZTX1HKGD58 hasPart 01J6H5Q84GGFTVSC593DRCM135.pdf.
- 01HSTRBDG8DRYQ9RZTX1HKGD58 subject "Physics and Astronomy".
- 01HSTRBDG8DRYQ9RZTX1HKGD58 doi "10.5194/egusphere-egu24-3829".
- 01HSTRBDG8DRYQ9RZTX1HKGD58 presentedAt urn:uuid:55aebba3-bb50-4347-aa87-545eba4a30e3.
- 01HSTRBDG8DRYQ9RZTX1HKGD58 abstract "Working with and analysing data from non-traditional measurement networks, such as urban climate networks, can be challenging and time consuming. After undertaking an observational campaign, researchers often face the issue of missing data due to technical problems such as power cuts or data communication issues. Additionally, data from low-cost networks or crowdsourced data need quality control to avoid the inclusion of measurement errors and biases, which often leads to additional gaps in the time series. Moreover, data storage formats and temporal measurement frequencies are often not consistent or synchronised when comparing data of different measurement networks. MetObs, an open-source Python toolkit, was developed to overcome these issues and fully exploits such valuable datasets. MetObs aims to provide a framework for the entire flow from raw sensor data to a dedicated analysis, with the possibility to apply it to various types of non-traditional networks without any formatting issues. To obtain a clean dataset, the time resolution is firstly resampled to the desired resolution, followed by identifying erroneous and missing records. Finally, missing records are filled in with the most suitable or preferred gap-filling method. Dedicated software for quality control, such as TITAN and CrowdQC+, already existed prior to the development of MetObs and are therefore implemented in the toolkit instead of being reinvented. The toolkit makes it moreover possible to generate analytics with the possibility to incorporate geographical data and create various graphics for the analysis of the meteorological measurements. MetObs was developed in such a way that people without a coding background can utilise it to get insight into their own meteorological measurements by following examples and using tutorials. At the same time, it allows more experienced data scientists to tweak the functionalities in such a way that the toolkit provides a pipeline for their dedicated use case.".
- 01HSTRBDG8DRYQ9RZTX1HKGD58 author 04C6BA44-F0EE-11E1-A9DE-61C894A0A6B4.
- 01HSTRBDG8DRYQ9RZTX1HKGD58 author 0f409364-db55-11ea-8935-bfa8e61fdb0e.
- 01HSTRBDG8DRYQ9RZTX1HKGD58 author 28566F74-413E-11E5-BA5D-5972B5D1D7B1.
- 01HSTRBDG8DRYQ9RZTX1HKGD58 author 2DDC8598-68E0-11E7-9F78-3A77AD28A064.
- 01HSTRBDG8DRYQ9RZTX1HKGD58 author 9E7AB6E6-EA75-11E2-8F0A-16AD10BDE39D.
- 01HSTRBDG8DRYQ9RZTX1HKGD58 author E0896008-1B2D-11E3-B8E9-78BC10BDE39D.
- 01HSTRBDG8DRYQ9RZTX1HKGD58 author bf6692ee-f60c-11ea-9d9a-d814a4a46df4.
- 01HSTRBDG8DRYQ9RZTX1HKGD58 author f681cd52-1802-11ec-945e-8adee4c9e39a.
- 01HSTRBDG8DRYQ9RZTX1HKGD58 dateCreated "2024-03-25T12:11:20Z".
- 01HSTRBDG8DRYQ9RZTX1HKGD58 dateModified "2024-11-28T00:06:55Z".
- 01HSTRBDG8DRYQ9RZTX1HKGD58 name "Fast analysis of urban meteorological observations with the user-friendly MetObs-toolkit".
- 01HSTRBDG8DRYQ9RZTX1HKGD58 pagination urn:uuid:79e372a5-fe6b-4856-889c-78be7495b636.
- 01HSTRBDG8DRYQ9RZTX1HKGD58 publisher urn:uuid:19a3f0f5-30d5-4f29-a681-d11526b58981.
- 01HSTRBDG8DRYQ9RZTX1HKGD58 sameAs LU-01HSTRBDG8DRYQ9RZTX1HKGD58.
- 01HSTRBDG8DRYQ9RZTX1HKGD58 sourceOrganization urn:uuid:2b1a6370-c90a-456d-92b1-b851154da5ea.
- 01HSTRBDG8DRYQ9RZTX1HKGD58 type C3.