37 citations as recorded by crossref.

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3. The value of assimilating different ground-based profiling networks on the forecasts of bore-generating nocturnal convection H. Chipilski et al. https://doi.org/10.1175/MWR-D-21-0193.1
4. Passive ground-based remote sensing of radiation fog H. Guy et al. https://doi.org/10.5194/amt-15-5095-2022
5. Retrieving atmospheric thermodynamic and hydrometeor profiles using a thermodynamic-constrained Kalman filter 1D-Var framework based on ground-based microwave radiometer Q. Zhang et al. https://doi.org/10.5194/gmd-19-505-2026
6. Impact of Assimilating Ground-Based Microwave Radiometer Data on the Precipitation Bifurcation Forecast: A Case Study in Beijing Y. Qi et al. https://doi.org/10.3390/atmos12050551
7. Assimilation of Ground-Based Microwave Radiometer on Heavy Rainfall Forecast in Beijing Y. Qi et al. https://doi.org/10.3390/atmos13010074
8. A dataset of temperature, humidity, and liquid water path retrievals from a network of ground-based microwave radiometers dedicated to fog investigation P. Martinet et al. https://doi.org/10.1007/s42865-022-00049-w
9. Differential Tropospheric Tomography Using Spaceborne Simultaneous Multiangle D-InSAR: Method, Optimization, and Performance Analysis Y. Li et al. https://doi.org/10.1109/TGRS.2024.3482859
10. Optimal Estimation Retrievals and Their Uncertainties: What Every Atmospheric Scientist Should Know M. Maahn et al. https://doi.org/10.1175/BAMS-D-19-0027.1
11. Comparison of temperature and wind profiles between ground-based remote sensing observations and numerical weather prediction model in complex Alpine topography: the Meiringen campaign A. Bugnard et al. https://doi.org/10.5194/amt-18-1039-2025
12. W-band radar observations for fog forecast improvement: an analysis of model and forward operator errors A. Bell et al. https://doi.org/10.5194/amt-14-4929-2021
13. TP-PROFILE: Monitoring the Thermodynamic Structure of the Troposphere over the Third Pole X. Chen et al. https://doi.org/10.1007/s00376-023-3199-y
14. Retrieval of Atmospheric Profiles in the New York State Mesonet Using One‐Dimensional Variational Algorithm J. Yang & Q. Min https://doi.org/10.1029/2018JD028272
15. 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
16. Synchronous Remote Sensing Boundary Layer Temperature and Humidity Profiles From Ground-Based Infrared Hyperspectral Radiometer Based on Radial Basis Function Neural Networks Y. Xiao et al. https://doi.org/10.1109/TGRS.2026.3684746
17. Long-Term Observations of Microwave Brightness Temperatures over a Metropolitan Area: Comparison of Radiometric Data and Spectra Simulated with the Use of Radiosonde Measurements M. Belikovich et al. https://doi.org/10.3390/rs13112061
18. Toward instrument combination for boundary layer classification T. Rieutord et al. https://doi.org/10.1002/asl.1144
19. EUMETNET opens to microwave radiometers for operational thermodynamical profiling in Europe R. Rüfenacht et al. https://doi.org/10.1007/s42865-021-00033-w
20. Uncertainty of atmospheric microwave absorption model: impact on ground-based radiometer simulations and retrievals D. Cimini et al. https://doi.org/10.5194/acp-18-15231-2018
21. Synergy of Satellite- and Ground-Based Observations for Continuous Monitoring of Atmospheric Stability, Liquid Water Path, and Integrated Water Vapor: Theoretical Evaluations Using Reanalysis and Neural Networks M. Toporov & U. Löhnert https://doi.org/10.1175/JAMC-D-19-0169.1
22. An optimal estimation algorithm for the retrieval of fog and low cloud thermodynamic and micro-physical properties A. Bell et al. https://doi.org/10.5194/amt-15-5415-2022
23. How Can Existing Ground-Based Profiling Instruments Improve European Weather Forecasts? A. Illingworth et al. https://doi.org/10.1175/BAMS-D-17-0231.1
24. Improvement of numerical weather prediction model analysis during fog conditions through the assimilation of ground-based microwave radiometer observations: a 1D-Var study P. Martinet et al. https://doi.org/10.5194/amt-13-6593-2020
25. Assimilation of Ground‐Based Microwave Radiometer Temperature Observations Into a Convective‐Scale NWP Model for Fog Forecast Improvement G. Thomas et al. https://doi.org/10.1002/qj.4893
26. A Method for Retrieving Thermodynamic Atmospheric Profiles Using Microwave Radiometers of Meteorological Observation Networks L. Zhu et al. https://doi.org/10.1109/TGRS.2022.3208939
27. Effects of Assimilating Ground-Based Microwave Radiometer and FY-3D MWTS-2/MWHS-2 Data in Precipitation Forecasting B. Wang et al. https://doi.org/10.3390/rs16142682
28. Improving the representation of the atmospheric boundary layer by direct assimilation of ground‐based microwave radiometer observations J. Vural et al. https://doi.org/10.1002/qj.4634
29. Atmospheric stability from numerical weather prediction models and microwave radiometer observations for onshore and offshore wind energy applications D. Cimini et al. https://doi.org/10.5194/amt-18-2041-2025
30. PyRTlib: an educational Python-based library for non-scattering atmospheric microwave radiative transfer computations S. Larosa et al. https://doi.org/10.5194/gmd-17-2053-2024
31. Characterising the mountain boundary layer using a passive microwave radiometer H. Scheiber et al. https://doi.org/10.1007/s42865-026-00131-7
32. Temperature and Humidity Profiles Retrieval in a Plain Area from Fengyun-3D/HIRAS Sensor Using a 1D-VAR Assimilation Scheme L. Zhu et al. https://doi.org/10.3390/rs12030435
33. Numerical correction of atmospheric temperature profiles in clear and cloudy days Y. Zhao et al. https://doi.org/10.1016/j.atmosres.2018.10.024
34. Towards the profiling of the atmospheric boundary layer at European scale—introducing the COST Action PROBE D. Cimini et al. https://doi.org/10.1007/s42865-020-00003-8
35. One-Dimensional Variational Retrieval of Temperature and Humidity Profiles from the FY4A GIIRS Q. Xue et al. https://doi.org/10.1007/s00376-021-1032-z
36. Application of Ground-Based Microwave Radiometers to Optimize the Estimation Method of Cloud Liquid Water on the Tibetan Plateau Y. Liu et al. https://doi.org/10.1007/s00376-025-4416-7
37. Precipitation Phase Classification with X-Band Polarimetric Radar and Machine Learning Using Micro Rain Radar and Disdrometer Data in Grenoble (French Alps) F. Polls et al. https://doi.org/10.3390/rs18030433