Matches in SemOpenAlex for { <https://semopenalex.org/work/W4224942899> ?p ?o ?g. }
Showing items 1 to 55 of
55
with 100 items per page.
- W4224942899 abstract "<strong class=journal-contentHeaderColor>Abstract.</strong> Remote sensing of water vapour using the Global Navigation Satellite System (GNSS) is a well-established technique and reliable data source for numerical weather prediction (NWP). However, one of the phenomena rarely studied using GNSS are foehn winds. Since foehn winds are associated with significant humidity gradients between two sides of a mountain range, tropospheric estimates from GNSS are also affected by their occurrence. Time series reveal characteristic features like distinctive minima and maxima as well as a significant decrease in the correlation between the stations. However, detecting such signals becomes increasingly difficult for large datasets. Therefore, we suggest the application of machine learning algorithms for the detection and prediction of foehn events by means of GNSS troposphere products. This initial study develops a new, machine learning-based method for detection and prediction of foehn events at the Swiss station Altdorf by utilising long-term time series of high-quality GNSS troposphere products. Data from the Automated GNSS Network Switzerland (AGNES) and various GNSS sites from neighbouring countries as well as records of an operational foehn index are used to investigate the performance of several different classification algorithms based on appropriate statistical metrics. The two best-performing algorithms are fine tuned and tested in four dedicated experiments using different feature setups. The results are promising, especially when reprocessed GNSS products are utilised and the most dense station setup is used. Detection- and alarm-based measures reach levels between 66â%â80â% for both tested algorithms and thus are comparable to those from studies using data from meteorological stations and NWP. For operational prediction, limitations due to the availability and quality of GNSS products in near-real time (NRT) exist. However, they might be mitigated to a significant extent by provision of additional NRT products and improved data processing in the future. Results also outline benefits for the results when including geographically relevant stations (e.g. high-altitude stations) in the utilised datasets." @default.
- W4224942899 created "2022-04-28" @default.
- W4224942899 date "2022-04-27" @default.
- W4224942899 modified "2023-09-23" @default.
- W4224942899 title "Comment on amt-2022-33" @default.
- W4224942899 doi "https://doi.org/10.5194/amt-2022-33-rc2" @default.
- W4224942899 hasPublicationYear "2022" @default.
- W4224942899 type Work @default.
- W4224942899 citedByCount "0" @default.
- W4224942899 crossrefType "peer-review" @default.
- W4224942899 hasBestOaLocation W42249428991 @default.
- W4224942899 hasConcept C127313418 @default.
- W4224942899 hasConcept C127413603 @default.
- W4224942899 hasConcept C14279187 @default.
- W4224942899 hasConcept C146978453 @default.
- W4224942899 hasConcept C153294291 @default.
- W4224942899 hasConcept C19269812 @default.
- W4224942899 hasConcept C205649164 @default.
- W4224942899 hasConcept C2778027091 @default.
- W4224942899 hasConcept C39432304 @default.
- W4224942899 hasConcept C41008148 @default.
- W4224942899 hasConcept C60229501 @default.
- W4224942899 hasConcept C62649853 @default.
- W4224942899 hasConcept C76155785 @default.
- W4224942899 hasConcept C9075549 @default.
- W4224942899 hasConceptScore W4224942899C127313418 @default.
- W4224942899 hasConceptScore W4224942899C127413603 @default.
- W4224942899 hasConceptScore W4224942899C14279187 @default.
- W4224942899 hasConceptScore W4224942899C146978453 @default.
- W4224942899 hasConceptScore W4224942899C153294291 @default.
- W4224942899 hasConceptScore W4224942899C19269812 @default.
- W4224942899 hasConceptScore W4224942899C205649164 @default.
- W4224942899 hasConceptScore W4224942899C2778027091 @default.
- W4224942899 hasConceptScore W4224942899C39432304 @default.
- W4224942899 hasConceptScore W4224942899C41008148 @default.
- W4224942899 hasConceptScore W4224942899C60229501 @default.
- W4224942899 hasConceptScore W4224942899C62649853 @default.
- W4224942899 hasConceptScore W4224942899C76155785 @default.
- W4224942899 hasConceptScore W4224942899C9075549 @default.
- W4224942899 hasLocation W42249428991 @default.
- W4224942899 hasOpenAccess W4224942899 @default.
- W4224942899 hasPrimaryLocation W42249428991 @default.
- W4224942899 hasRelatedWork W1660767182 @default.
- W4224942899 hasRelatedWork W2465131005 @default.
- W4224942899 hasRelatedWork W2557372182 @default.
- W4224942899 hasRelatedWork W2560488779 @default.
- W4224942899 hasRelatedWork W279123267 @default.
- W4224942899 hasRelatedWork W2904460134 @default.
- W4224942899 hasRelatedWork W2987288285 @default.
- W4224942899 hasRelatedWork W3200675407 @default.
- W4224942899 hasRelatedWork W4312801260 @default.
- W4224942899 hasRelatedWork W4321490964 @default.
- W4224942899 isParatext "false" @default.
- W4224942899 isRetracted "false" @default.
- W4224942899 workType "peer-review" @default.