27 citations as recorded by crossref.

1. Determination of CERES TOA Fluxes Using Machine Learning Algorithms. Part I: Classification and Retrieval of CERES Cloudy and Clear Scenes B. Thampi et al. 10.1175/JTECH-D-16-0183.1
2. A quantitative precipitation forecast model using convective-cloud tracking in satellite thermal infrared images and adaptive regression: a case study along East Coast of India B. Goswami et al. 10.1007/s40808-020-00968-7
3. Application of Deep Learning Architectures for Satellite Image Time Series Prediction: A Review W. Moskolaï et al. 10.3390/rs13234822
4. A random forest algorithm for nowcasting of intense precipitation events S. Das et al. 10.1016/j.asr.2017.03.026
5. NowCasting-Nets: Representation Learning to Mitigate Latency Gap of Satellite Precipitation Products Using Convolutional and Recurrent Neural Networks M. Ehsani et al. 10.1109/TGRS.2022.3158888
6. Precipitation Estimation Using FY-4B/AGRI Satellite Data Based on Random Forest Y. Huang et al. 10.3390/rs16071267
7. MCSIP Net: Multichannel Satellite Image Prediction via Deep Neural Network J. Lee et al. 10.1109/TGRS.2019.2955538
8. Rainfall Nowcasting From Multisatellite Passive-Sensor Images Using a Recurrent Neural Network F. Marzano et al. 10.1109/TGRS.2007.903685
9. A sequence-to-sequence based multi-scale deep learning model for satellite cloud image prediction J. Lian & R. Chen 10.1007/s12145-023-00945-5
10. Learning ensembles of deep neural networks for extreme rainfall event detection G. Folino et al. 10.1007/s00521-023-08238-0
11. Flood forecast in complex orography coupling distributed hydro-meteorological models and in-situ and remote sensing data M. Verdecchia et al. 10.1007/s00703-007-0278-z
12. Neural Network Approach to Forecast Hourly Intense Rainfall Using GNSS Precipitable Water Vapor and Meteorological Sensors P. Benevides et al. 10.3390/rs11080966
13. Comparison of four machine learning algorithms for their applicability in satellite-based optical rainfall retrievals H. Meyer et al. 10.1016/j.atmosres.2015.09.021
14. Extrapolation of Sequence of Geostationary Satellite Images for Weather Nowcasting B. Shukla et al. 10.1109/LGRS.2010.2060311
15. Convcast: An embedded convolutional LSTM based architecture for precipitation nowcasting using satellite data A. Kumar et al. 10.1371/journal.pone.0230114
16. Multiple data fusion for rainfall estimation using a NARX-based recurrent neural network – the development of the REIINN model M. Ang et al. 10.1088/1755-1315/17/1/012019
17. Some novel hybrid forecast methods based on picture fuzzy clustering for weather nowcasting from satellite image sequences L. Son & P. Thong 10.1007/s10489-016-0811-1
18. Cyclone track forecasting based on satellite images using artificial neural networks R. Kovordányi & C. Roy 10.1016/j.isprsjprs.2009.03.002
19. Implementation of intuitionistic fuzzy logic to assess the predictability of severe thunderstorms S. Chaudhuri 10.1007/s13143-011-0032-9
20. Precipitation Estimates from MSG SEVIRI Daytime, Nighttime, and Twilight Data with Random Forests M. Kühnlein et al. 10.1175/JAMC-D-14-0082.1
21. 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. 10.1016/j.rse.2013.10.026
22. Revealing the potential of spectral and textural predictor variables in a neural network-based rainfall retrieval technique H. Meyer et al. 10.1080/2150704X.2017.1312026
23. The Added Value of Surface Data to Radar-Derived Rainfall-Rate Estimation Using an Artificial Neural Network B. Root et al. 10.1175/2010JTECHA1361.1
24. Estimating High Spatio-Temporal Resolution Rainfall from MSG1 and GPM IMERG Based on Machine Learning: Case Study of Iran N. Turini et al. 10.3390/rs11192307
25. Prediction of Satellite Image Sequence for Weather Nowcasting Using Cluster-Based Spatiotemporal Regression B. Shukla et al. 10.1109/TGRS.2013.2280094
26. Monthly adjustment of Global Satellite Mapping of Precipitation (GSMaP) data over the VuGia^|^ndash;ThuBon River Basin in Central Vietnam using an artificial neural network T. Ngo-Duc et al. 10.3178/hrl.7.85
27. Assessing Nowcast Models in the Central Mexico Region Using Radar and GOES-16 Satellite Data D. Islas-Flores & A. Magaldi 10.3390/atmos15020152