32 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. https://doi.org/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. https://doi.org/10.1007/s40808-020-00968-7
3. Application of Artificial Neural Networks for Accuracy Enhancements of Real-Time Flood Forecasting in the Imjin Basin A. Jabbari & D. Bae https://doi.org/10.3390/w10111626
4. Application of Deep Learning Architectures for Satellite Image Time Series Prediction: A Review W. Moskolaï et al. https://doi.org/10.3390/rs13234822
5. A random forest algorithm for nowcasting of intense precipitation events S. Das et al. https://doi.org/10.1016/j.asr.2017.03.026
6. NowCasting-Nets: Representation Learning to Mitigate Latency Gap of Satellite Precipitation Products Using Convolutional and Recurrent Neural Networks M. Ehsani et al. https://doi.org/10.1109/TGRS.2022.3158888
7. Precipitation Estimation Using FY-4B/AGRI Satellite Data Based on Random Forest Y. Huang et al. https://doi.org/10.3390/rs16071267
8. MCSIP Net: Multichannel Satellite Image Prediction via Deep Neural Network J. Lee et al. https://doi.org/10.1109/TGRS.2019.2955538
9. Comparative study of cloud evolution for rainfall nowcasting using AI-based deep learning algorithms X. Jiang et al. https://doi.org/10.1016/j.jhydrol.2024.131593
10. Rainfall Nowcasting From Multisatellite Passive-Sensor Images Using a Recurrent Neural Network F. Marzano et al. https://doi.org/10.1109/TGRS.2007.903685
11. A sequence-to-sequence based multi-scale deep learning model for satellite cloud image prediction J. Lian & R. Chen https://doi.org/10.1007/s12145-023-00945-5
12. Learning ensembles of deep neural networks for extreme rainfall event detection G. Folino et al. https://doi.org/10.1007/s00521-023-08238-0
13. Radiometric Estimation of Tropospheric Attenuation: A Mixed Physically Based/Machine Learning Approach T. Tunçkol et al. https://doi.org/10.1109/TGRS.2024.3393506
14. Flood forecast in complex orography coupling distributed hydro-meteorological models and in-situ and remote sensing data M. Verdecchia et al. https://doi.org/10.1007/s00703-007-0278-z
15. Neural Network Approach to Forecast Hourly Intense Rainfall Using GNSS Precipitable Water Vapor and Meteorological Sensors P. Benevides et al. https://doi.org/10.3390/rs11080966
16. Comparison of four machine learning algorithms for their applicability in satellite-based optical rainfall retrievals H. Meyer et al. https://doi.org/10.1016/j.atmosres.2015.09.021
17. Artificial Intelligence and Its Application in Numerical Weather Prediction S. Soldatenko https://doi.org/10.3103/S1068373924040010
18. Extrapolation of Sequence of Geostationary Satellite Images for Weather Nowcasting B. Shukla et al. https://doi.org/10.1109/LGRS.2010.2060311
19. Convcast: An embedded convolutional LSTM based architecture for precipitation nowcasting using satellite data A. Kumar et al. https://doi.org/10.1371/journal.pone.0230114
20. Machine Learning Methods for Weather Forecasting: A Survey H. Zhang et al. https://doi.org/10.3390/atmos16010082
21. Multiple data fusion for rainfall estimation using a NARX-based recurrent neural network – the development of the REIINN model M. Ang et al. https://doi.org/10.1088/1755-1315/17/1/012019
22. Some novel hybrid forecast methods based on picture fuzzy clustering for weather nowcasting from satellite image sequences L. Son & P. Thong https://doi.org/10.1007/s10489-016-0811-1
23. Cyclone track forecasting based on satellite images using artificial neural networks R. Kovordányi & C. Roy https://doi.org/10.1016/j.isprsjprs.2009.03.002
24. Implementation of intuitionistic fuzzy logic to assess the predictability of severe thunderstorms S. Chaudhuri https://doi.org/10.1007/s13143-011-0032-9
25. 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
26. 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
27. Revealing the potential of spectral and textural predictor variables in a neural network-based rainfall retrieval technique H. Meyer et al. https://doi.org/10.1080/2150704X.2017.1312026
28. The Added Value of Surface Data to Radar-Derived Rainfall-Rate Estimation Using an Artificial Neural Network B. Root et al. https://doi.org/10.1175/2010JTECHA1361.1
29. Estimating High Spatio-Temporal Resolution Rainfall from MSG1 and GPM IMERG Based on Machine Learning: Case Study of Iran N. Turini et al. https://doi.org/10.3390/rs11192307
30. Prediction of Satellite Image Sequence for Weather Nowcasting Using Cluster-Based Spatiotemporal Regression B. Shukla et al. https://doi.org/10.1109/TGRS.2013.2280094
31. 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. https://doi.org/10.3178/hrl.7.85
32. Assessing Nowcast Models in the Central Mexico Region Using Radar and GOES-16 Satellite Data D. Islas-Flores & A. Magaldi https://doi.org/10.3390/atmos15020152