50 citations as recorded by crossref.

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4. Potential of microwave observations for the evaluation of rainfall and convection in a regional climate model in the frame of HyMeX and MED-CORDEX J. Rysman et al. https://doi.org/10.1007/s00382-016-3203-7
5. Warm Core and Deep Convection in Medicanes: A Passive Microwave-Based Investigation G. Panegrossi et al. https://doi.org/10.3390/rs15112838
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8. Global Precipitation Estimates from Cross-Track Passive Microwave Observations Using a Physically Based Retrieval Scheme C. Kidd et al. https://doi.org/10.1175/JHM-D-15-0051.1
9. Integration of shapley additive explanations with random forest model for quantitative precipitation estimation of mesoscale convective systems Z. He et al. https://doi.org/10.3389/fenvs.2022.1057081
10. A Generative Diffusion Model for Probabilistic Ensembles of Precipitation Maps Conditioned on Multisensor Satellite Observations C. Guilloteau et al. https://doi.org/10.1109/TGRS.2025.3548518
11. Analysis of Livorno Heavy Rainfall Event: Examples of Satellite-Based Observation Techniques in Support of Numerical Weather Prediction E. Ricciardelli et al. https://doi.org/10.3390/rs10101549
12. A Machine Learning Snowfall Retrieval Algorithm for ATMS P. Sanò et al. https://doi.org/10.3390/rs14061467
13. Severe convection in the Mediterranean from microwave observations and a convection‐permitting model J. Rysman et al. https://doi.org/10.1002/qj.2611
14. Rainfall Algorithms Using Oceanic Satellite Observations from MWHS-2 R. Chen & R. Bennartz https://doi.org/10.1007/s00376-020-0258-5
15. Assessment of Ground-Reference Data and Validation of the H-SAF Precipitation Products in Brazil L. Martins Costa do Amaral et al. https://doi.org/10.3390/rs10111743
16. Reconstructing Satellite-Based Monthly Precipitation over Northeast China Using Machine Learning Algorithms W. Jing et al. https://doi.org/10.3390/rs9080781
17. A Prototype Precipitation Retrieval Algorithm over Land for ATMS Y. You et al. https://doi.org/10.1175/JHM-D-15-0163.1
18. Precipitation Intensity Trend Detection using Hourly and Daily Observations in Portland, Oregon A. Cooley & H. Chang https://doi.org/10.3390/cli5010010
19. Near-surface wind field characterization of medicanes using satellite observations S. Sebastianelli et al. https://doi.org/10.1016/j.atmosres.2025.108734
20. Three Dimensional Radiative Effects in Passive Millimeter/Sub-Millimeter All-sky Observations V. Barlakas & P. Eriksson https://doi.org/10.3390/rs12030531
21. Use of the GPM Constellation for Monitoring Heavy Precipitation Events Over the Mediterranean Region G. Panegrossi et al. https://doi.org/10.1109/JSTARS.2016.2520660
22. Neural Network Algorithm for Precipitation Estimation from ATMS Radiometer Data A. Filei & A. Andreev https://doi.org/10.1134/S0001433825700112
23. 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
24. The Passive Microwave Neural Network Precipitation Retrieval (PNPR) Algorithm for the CONICAL Scanning Global Microwave Imager (GMI) Radiometer P. Sanò et al. https://doi.org/10.3390/rs10071122
25. Spatio-temporal variations of precipitation considering the orographic effects on Jeju Island M. Um et al. https://doi.org/10.1016/j.atmosres.2016.07.007
26. The Use of TRMM 3B42 Product for Drought Monitoring in Mexico A. De Jesús et al. https://doi.org/10.3390/w8080325
27. 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
28. The High lAtitude sNowfall Detection and Estimation aLgorithm for ATMS (HANDEL-ATMS): a new algorithm for snowfall retrieval at high latitudes A. Camplani et al. https://doi.org/10.5194/amt-17-2195-2024
29. A New Data Fusion Neural Network Scheme for Rainfall Retrieval Using Passive Microwave and Visible/Infrared Satellite Data M. Sist et al. https://doi.org/10.3390/app11104686
30. Detecting change in precipitation indices using observed (1977–2016) and modeled future climate data in Portland, Oregon, USA A. Cooley & H. Chang https://doi.org/10.2166/wcc.2020.043
31. Precipitation Retrieval from FY-3G/MWRI-RM Based on SMOTE-LGBM Y. Lv et al. https://doi.org/10.3390/atmos15111268
32. Can We Use Atmospheric Targets for Geolocating Spaceborne Millimeter-Wave Ice Cloud Imager (ICI) Acquisitions? D. Casella et al. https://doi.org/10.1109/TGRS.2022.3145638
33. Improving Overland Precipitation Retrieval with Brightness Temperature Temporal Variation Y. You et al. https://doi.org/10.1175/JHM-D-17-0050.1
34. Daily precipitation estimation through different microwave sensors: Verification study over Italy L. Ciabatta et al. https://doi.org/10.1016/j.jhydrol.2016.12.057
35. Passive microwave-based diagnostics of medicanes over the period 2000–2021 V. Di Francesca et al. https://doi.org/10.1016/j.atmosres.2025.107922
36. Stratiform/convective rain delineation for TRMM microwave imager T. Islam et al. https://doi.org/10.1016/j.jastp.2015.07.009
37. Beyond the Pixel: Using Patterns and Multiscale Spatial Information to Improve the Retrieval of Precipitation from Spaceborne Passive Microwave Imagers C. Guilloteau & E. Foufoula-Georgiou https://doi.org/10.1175/JTECH-D-19-0067.1
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39. The Passive Microwave Neural Network Precipitation Retrieval Algorithm for Climate Applications (PNPR-CLIM): Design and Verification L. Bagaglini et al. https://doi.org/10.3390/rs13091701
40. Neural network algorithm for precipitation estimation from atms radiometer data A. Filei & A. Andreev https://doi.org/10.31857/S0205961424040019
41. Precipitation Retrievals from Passive Microwave Cross-Track Sensors: The Precipitation Retrieval and Profiling Scheme C. Kidd et al. https://doi.org/10.3390/rs13050947
42. Leveraging machine learning for quantitative precipitation estimation from Fengyun-4 geostationary observations and ground meteorological measurements X. Li et al. https://doi.org/10.5194/amt-14-7007-2021
43. Can severe rain events over the Mediterranean region be detected through simple numerical indices? B. Ziv et al. https://doi.org/10.1007/s11069-016-2385-y
44. The Cloud Dynamics and Radiation Database Algorithm for AMSR2: Exploitation of the GPM Observational Dataset for Operational Applications D. Casella et al. https://doi.org/10.1109/JSTARS.2017.2713485
45. A Comparison of Different Regression Algorithms for Downscaling Monthly Satellite-Based Precipitation over North China W. Jing et al. https://doi.org/10.3390/rs8100835
46. Multiscale and Multilevel Feature Fusion Network for Quantitative Precipitation Estimation With Passive Microwave Z. Wang et al. https://doi.org/10.1109/TGRS.2024.3396379
47. Mapping Fine Spatial Resolution Precipitation from TRMM Precipitation Datasets Using an Ensemble Learning Method and MODIS Optical Products in China X. Zhao et al. https://doi.org/10.3390/su9101912
48. A novel algorithm for detection of precipitation in tropical regions using PMW radiometers D. Casella et al. https://doi.org/10.5194/amt-8-1217-2015
49. Novel SVM-based technique to improve rainfall estimation over the Mediterranean region (north of Algeria) using the multispectral MSG SEVIRI imagery M. Sehad et al. https://doi.org/10.1016/j.asr.2016.11.042
50. A First Step towards Meteosat Third Generation Day-2 Precipitation Rate Product: Deep Learning for Precipitation Rate Retrieval from Geostationary Infrared Measurements L. D’Adderio et al. https://doi.org/10.3390/rs15245662