39 citations as recorded by crossref.

1. A deep learning pipeline for rainfall estimation from surveillance audio M. Fiallos-Salguero et al. https://doi.org/10.1016/j.jhydrol.2025.133921
2. Bias-correcting the high-resolution urban meteorology for impacts dataset (HUMID) temperature data using the personal weather stations (PWS) network and a spatial temporal Bayesian regression coupled with random forest M. Mathieu-Campbell et al. https://doi.org/10.1088/2515-7620/ae8e5d
3. Probabilistic modelling of atmosphere–surface coupling with a copula Bayesian network L. Mack et al. https://doi.org/10.1016/j.envsoft.2026.107003
4. Quality control and correction method for air temperature data from a citizen science weather station network in Leuven, Belgium E. Beele et al. https://doi.org/10.5194/essd-14-4681-2022
5. Ensemble‐based statistical interpolation of atmospheric variables near the surface C. Lussana et al. https://doi.org/10.1002/qj.5046
6. Machine learning-based tsunami inundation prediction derived from offshore observations I. Mulia et al. https://doi.org/10.1038/s41467-022-33253-5
7. Analysis of nocturnal urban heat advection using crowd weather stations J. Kittner et al. https://doi.org/10.1002/qj.5065
8. CrowdQC+—A Quality-Control for Crowdsourced Air-Temperature Observations Enabling World-Wide Urban Climate Applications D. Fenner et al. https://doi.org/10.3389/fenvs.2021.720747
9. Ensemble-based statistical interpolation with Gaussian anamorphosis for the spatial analysis of precipitation C. Lussana et al. https://doi.org/10.5194/npg-28-61-2021
10. Assimilation of temperature and relative humidity observations from personal weather stations in AROME-France A. Demortier et al. https://doi.org/10.5194/nhess-25-429-2025
11. Evaluation of city-scale PALM model simulations and intra-urban thermal variability in Vienna, Austria using operational and crowdsourced data M. Žuvela-Aloise et al. https://doi.org/10.1016/j.uclim.2024.102245
12. Comprehensive compilation and quality assessment of street-level urban air temperature measurements across European networks S. Amini et al. https://doi.org/10.1038/s41597-026-06804-4
13. Unlocking crop protection models for decision support: web infrastructure and integration H. Huitu et al. https://doi.org/10.1093/insilicoplants/diag006
14. Quantifying Local and Mesoscale Drivers of the Urban Heat Island of Moscow with Reference and Crowdsourced Observations M. Varentsov et al. https://doi.org/10.3389/fenvs.2021.716968
15. Rainfall nowcasting models: state of the art and possible future perspectives D. De Luca et al. https://doi.org/10.1080/02626667.2025.2490780
16. Towards high-resolution gridded climatology stemming from the combination of official and crowdsourced weather observations using multi-fidelity methods D. van Beekvelt et al. https://doi.org/10.1371/journal.pclm.0000216
17. High-Resolution Air Temperature Forecasts in Urban Areas: A Meteorological Perspective on Their Added Value S. Oswald et al. https://doi.org/10.3390/atmos15121544
18. “Come as you are”: Reconsidering the need for complex quality control when gridding crowd‐sourced weather data J. de Baar & G. van der Schrier https://doi.org/10.1002/qj.4890
19. Adaptation of RainGaugeQC algorithms for quality control of rain gauge data from professional and non-professional measurement networks K. Ośródka et al. https://doi.org/10.5194/amt-18-3229-2025
20. Automatic quality control of telemetric rain gauge data providing quantitative quality information (RainGaugeQC) K. Ośródka et al. https://doi.org/10.5194/amt-15-5581-2022
21. A machine learning approach using autoencoders to perform quality control on meteorological data T. Spohn et al. https://doi.org/10.1017/eds.2026.10030
22. Comparison of temperature and humidity measurements from two separate weather stations at Camden Airport, Australia M. Riley & A. Rea https://doi.org/10.1071/ES24013
23. Observations from Personal Weather Stations—EUMETNET Interests and Experience C. Hahn et al. https://doi.org/10.3390/cli10120192
24. From proof‐of‐concept to proof‐of‐value: Approaching third‐party data to operational workflows of national meteorological services I. Garcia‐Marti et al. https://doi.org/10.1002/joc.7757
25. Using machine learning to produce a very high resolution land-cover map for Ireland E. Walsh et al. https://doi.org/10.5194/asr-18-65-2021
26. Quality management system and design of an integrated mesoscale meteorological network in Central Italy L. Silvestri et al. https://doi.org/10.1002/met.2060
27. The importance of crowdsourced observations for urban climate services T. Mitchell & M. Fry https://doi.org/10.1002/joc.8390
28. A Quality Control Method Applicable to Precipitation at Hydrological Rainfall Stations 莉. 赵 https://doi.org/10.12677/ccrl.2024.135124
29. Accuracy of crowdsourced air temperature observations from multiple networks R. Agrawal et al. https://doi.org/10.1016/j.enbuild.2026.117905
30. Assessing the Trustworthiness of Crowdsourced Rainfall Networks: A Reputation System Approach A. Chen et al. https://doi.org/10.1029/2021WR029721
31. Exploratory analysis of citizen observations of hourly precipitation over Scandinavia C. Lussana et al. https://doi.org/10.5194/asr-20-35-2023
32. MetObs - a Python toolkit for using non-traditional meteorological observations T. Vergauwen et al. https://doi.org/10.21105/joss.05916
33. The hectometric modelling challenge: Gaps in the current state of the art and ways forward towards the implementation of 100‐m scale weather and climate models H. Lean et al. https://doi.org/10.1002/qj.4858
34. Merging weather radar data and opportunistic rainfall sensor data to enhance rainfall estimates J. Nielsen et al. https://doi.org/10.1016/j.atmosres.2024.107228
35. Personal weather stations in hydrology and meteorology: a review of applications, challenges and future directions P. Mazzoglio et al. https://doi.org/10.1080/02626667.2026.2665735
36. Have you ever seen the rain? Observing a record convective rainfall with national and local monitoring networks and opportunistic sensors L. Petersson Wårdh et al. https://doi.org/10.5194/amt-19-461-2026
37. Merging with crowdsourced rain gauge data improves pan-European radar precipitation estimates A. Overeem et al. https://doi.org/10.5194/hess-28-649-2024
38. Using Gradient Boosting for gap-filling to analyze temperature and humidity patterns in an urban weather station network in Freiburg, Germany M. Plein et al. https://doi.org/10.1016/j.uclim.2025.102496
39. Areal reduction factors from gridded data products J. Lutz et al. https://doi.org/10.1016/j.jhydrol.2024.131177