19 citations as recorded by crossref.

1. Rainfall-Rate Assignment Using MSG SEVIRI Data—A Promising Approach to Spaceborne Rainfall-Rate Retrieval for Midlatitudes M. Kühnlein et al. https://doi.org/10.1175/2010JAMC2284.1
2. Retrieval of warm cloud optical properties using simple approximations T. Nauss & A. Kokhanovsky https://doi.org/10.1016/j.rse.2011.01.010
3. Weather type dependent quality assessment of a satellite-based rainfall detection scheme for the mid-latitudes B. Thies et al. https://doi.org/10.1007/s00703-010-0076-x
4. 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
5. Precipitation estimation by a multi-threshold method using cloud optical and microphysical properties from MSG / SEVIRI data M. Lazri et al. https://doi.org/10.1051/e3sconf/202017002002
6. Using the Gradient Boosting Decision Tree to Improve the Delineation of Hourly Rain Areas during the Summer from Advanced Himawari Imager Data L. Ma et al. https://doi.org/10.1175/JHM-D-17-0109.1
7. Novel WkNN-based technique to improve instantaneous rainfall estimation over the north of Algeria using the multispectral MSG SEVIRI imagery N. Bensafi et al. https://doi.org/10.1016/j.jastp.2018.12.004
8. Precipitation process and rainfall intensity differentiation using Meteosat Second Generation Spinning Enhanced Visible and Infrared Imager data B. Thies et al. https://doi.org/10.1029/2008JD010464
9. Discriminating raining from non-raining clouds at mid-latitudes using meteosat second generation daytime data B. Thies et al. https://doi.org/10.5194/acp-8-2341-2008
10. Discriminating raining from non‐raining cloud areas at mid‐latitudes using meteosat second generation SEVIRI night‐time data B. Thies et al. https://doi.org/10.1002/met.56
11. Improving the accuracy of rainfall rates from optical satellite sensors with machine learning — A random forests-based approach applied to MSG SEVIRI M. Kühnlein et al. https://doi.org/10.1016/j.rse.2013.10.026
12. Precipitation Clouds Delineation Scheme in Tropical Cyclones and Its Validation Using Precipitation and Cloud Parameter Datasets from TRMM F. Chen et al. https://doi.org/10.1175/JAMC-D-17-0157.1
13. The Berchtesgaden National Park (Bavaria, Germany): a platform for interdisciplinary catchment research T. Marke et al. https://doi.org/10.1007/s12665-013-2317-z
14. Precipitation Estimates from MSG SEVIRI Daytime, Nighttime, and Twilight Data with Random Forests M. Kühnlein et al. https://doi.org/10.1175/JAMC-D-14-0082.1
15. 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
16. Satellite based remote sensing of weather and climate: recent achievements and future perspectives B. Thies & J. Bendix https://doi.org/10.1002/met.288
17. SEVIRI rainfall retrieval and validation using weather radar observations R. Roebeling & I. Holleman https://doi.org/10.1029/2009JD012102
18. A Deep Learning-Based Algorithm for Identifying Precipitation Clouds Using Fengyun-4A Satellite Observation Data G. Ma et al. https://doi.org/10.3390/s23156832
19. Identifying precipitating clouds in Greece using multispectral infrared Meteosat Second Generation satellite data H. Feidas & A. Giannakos https://doi.org/10.1007/s00704-010-0316-5