16 citations as recorded by crossref.

1. ANOVA (Benova) correction in relative homogenization: Why it is indispensable P. Domonkos & L. Joelsson https://doi.org/10.1002/joc.8594
2. Relative Homogenization of Climatic Time Series P. Domonkos https://doi.org/10.3390/atmos15080957
3. Homogenizing GPS Integrated Water Vapor Time Series: Benchmarking Break Detection Methods on Synthetic Data Sets R. Van Malderen et al. https://doi.org/10.1029/2020EA001121
4. Multidecadal trend analysis of in situ aerosol radiative properties around the world M. Collaud Coen et al. https://doi.org/10.5194/acp-20-8867-2020
5. Automatic Homogenization of Time Series: How to Use Metadata? P. Domonkos https://doi.org/10.3390/atmos13091379
6. A New Adaptive Absolute Method for Homogenizing GNSS‐Derived Precipitable Water Vapor Time Series D. Zhu et al. https://doi.org/10.1029/2021EA001716
7. A new method to study inhomogeneities in climate records: Brownian motion or random deviations? R. Lindau & V. Venema https://doi.org/10.1002/joc.6105
8. A new homogenized daily data set for temperature variability assessment in Italy G. Fioravanti et al. https://doi.org/10.1002/joc.6177
9. Time Series Homogenization with ACMANT: Comparative Testing of Two Recent Versions in Large-Size Synthetic Temperature Datasets P. Domonkos https://doi.org/10.3390/cli11110224
10. CADTEP: A new daily quality‐controlled and homogenized climate database for Catalonia (1950–2021) M. Prohom et al. https://doi.org/10.1002/joc.8116
11. Homogenising meteorological variables: Impact on trends and associated climate indices O. Adeyeri et al. https://doi.org/10.1016/j.jhydrol.2022.127585
12. Application of homogenization methods for Ireland's monthly precipitation records: Comparison of break detection results J. Coll et al. https://doi.org/10.1002/joc.6575
13. Benova and Cenova Models in the Homogenization of Climatic Time Series P. Domonkos https://doi.org/10.3390/cli13100199
14. Insights from 20 years of temperature parallel measurements in Mauritius around the turn of the 20th century S. Awe et al. https://doi.org/10.5194/cp-18-793-2022
15. Random trend errors in climate station data due to inhomogeneities R. Lindau & V. Venema https://doi.org/10.1002/joc.6340
16. When it comes to Earth observations in AI for disaster risk reduction, is it feast or famine? A topical review M. Kuglitsch et al. https://doi.org/10.1088/1748-9326/acf601