8 citations as recorded by crossref.

1. Effect of Machine Learning Training Data on Water Vapor Profile Estimation Using Ground-Based Microwave Radiometer M. Minowa et al. https://doi.org/10.1007/s44393-025-00001-z
2. A Vertically Structured Machine Learning Approach for Cloud Liquid and Ice Water Content Profiling Z. Pan et al. https://doi.org/10.3390/rs18132177
3. Instrument uncertainties of network-suitable ground-based microwave radiometers: overview, quantification, and mitigation strategies T. Böck et al. https://doi.org/10.5194/amt-18-6251-2025
4. Microwave Radiometer Observations of Cloud Liquid Water Content in Hong Kong: Fog, Spring-Time Clouds, Rainstorms, and Typhoon P. Chan et al. https://doi.org/10.3390/app152111478
5. Enhancing GNSS water vapour retrieval via synergistic microwave radiometry: thermodynamic error diagnosis and bias correction A. Parde et al. https://doi.org/10.5194/amt-19-4517-2026
6. How do convective cold pools influence the atmospheric boundary layer near two wind turbines in northern Germany? J. Thayer et al. https://doi.org/10.5194/wes-10-2237-2025
7. Correcting Atmospheric Temperature and Vapor Density Profiles of Ground-Based Microwave Radiometer in Diverse Skies by Regression Model and Artificial Neural Network Methods G. Xu et al. https://doi.org/10.3390/rs17233819
8. Development and evaluation of a snow accumulation countermeasure for ground-based microwave radiometers for atmospheric and cloud observations in heavy snowfall areas M. MINOWA et al. https://doi.org/10.5331/bgr.25R04