43 citations as recorded by crossref.

1. A systematic review of the NASA Land Information System (LIS): Two decades of advancements in hydrological modeling, data assimilation, and operational earth system applications S. Marshall et al.
2. MAUNet: a max-average neural network architecture for precipitation downscaling S. Mishra Sharma & A. Mitra
3. Improved Near-Real-Time Precipitation Estimation From Himawari-8 Data and Gauge Observations in the Xiangjiang River Basin Using a Three-Stage Machine Learning Framework S. Yan et al.
4. Sub-Seasonal Precipitation Bias-Correction in Thailand Using Attention U-Net With Seasonal and Meteorological Effects T. Faijaroenmongkol et al.
5. Deep Learning Improves GFS Wintertime Precipitation Forecast Over Southeastern China D. Sun et al.
6. Enhancing IMERG precipitation estimates using machine learning and bias correction across elevation zones in India L. Nongmaithem et al.
7. Toward an improved ensemble of multi-source daily precipitation via joint machine learning classification and regression H. Chen et al.
8. SPATIAL DOWNSCALING OF DAILY PRECIPITATION d4PDF DATA USING ANN ALGORITHM I. KHATEEB et al.
9. Evaluating Bias Correction Methods Using Annual Maximum Series Rainfall Data from Observed and Remotely Sensed Sources in Gauged and Ungauged Catchments in Uganda M. Okirya & J. Du Plessis
10. A scalable deep learning framework for daily precipitation downscaling: architecture, accuracy, and adaptability X. Liu et al.
11. Multivariate bias correction and downscaling of climate models with trend-preserving deep learning F. Wang & D. Tian
12. Impact of deep learning-driven precipitation corrected data using near real-time satellite-based observations and model forecast in an integrated hydrological model K. Patakchi Yousefi et al.
13. Evaluating Geostatistical and Statistical Merging Methods for Radar–Gauge Rainfall Integration: A Multi-Method Comparative Study X. Le et al.
14. Assessing the effectiveness of ANN model in spatial downscaling of d4PDF hourly precipitation data: a case study in Japan I. Khateeb et al.
15. Machine Learning-Based Bias Correction of Precipitation Measurements at High Altitude H. Li et al.
16. Kernel-based dynamic ensemble approach for classifying imbalanced data with overlapping classes S. Abokadr et al.
17. Testing machine learning algorithms as post-processing tools for hydro-meteorological modelling over a small river basin C. Xu et al.
18. Limitation of super-resolution machine learning approach to precipitation downscaling P. Reddy et al.
19. Intercomparison of Deep Learning Architectures for the Prediction of Precipitation Fields With a Focus on Extremes N. Otero & P. Horton
20. Advancing isotope‐enabled hydrological modelling for ungauged calibration of data‐scarce humid tropical catchments A. Watson et al.
21. Improved modelling of mountain snowpacks with spatially distributed precipitation bias correction derived from historical reanalysis M. von Kaenel & S. Margulis
22. Explainable sequence-aware deep learning based decadal shoreline modelling in the Southern Baltic until 2050 K. Tanwari et al.
23. Successful Precipitation Downscaling Through an Innovative Transformer-Based Model F. Yang et al.
24. Accurate and efficient AI-assisted paradigm for adding granularity to ERA5 precipitation reanalysis M. Cavaiola et al.
25. Deep learning based bias correction of TRMM precipitation estimates using IMD-gridded precipitation as ground observation S. Mishra Sharma & A. Mitra
26. Deep Learning-Based Multi-Source Precipitation Fusion and Its Utility for Hydrological Simulation Z. Huang et al.
27. A precipitation downscaling method using a super-resolution deconvolution neural network with step orography P. Reddy et al.
28. Spatiotemporal Super-Resolution of Satellite Sea Surface Salinity Based on a Progressive Transfer Learning-Enhanced Transformer Z. Liang et al.
29. Bridging Global Climate Solutions and Local Realities: Evaluating Neural Networks for High-Resolution Downscaling D. Taniushkina et al.
30. Fusing Multi-Scale Numerical Model Forecasts to Improve Short-Term Intense Rainfall Forecast with a Deep Learning Rain Network Q. Zhong et al.
31. Performance of Kilometer-Scale CARAS Precipitation Product Against Ground-based Observations During 2008–2021 over Hubei, China C. ZHU et al.
32. Projecting future climate extremes in the glacier-fed upper indus basin using machine learning based downscaling of CMIP6 GCMs M. Saleem et al.
33. Current progress in subseasonal-to-decadal prediction based on machine learning Z. Shen et al.
34. Statistical spatial downscaling of significant wave height in a regional sea from the global ERA5 dataset B. Yuan et al.
35. Downscaling sea surface height and currents in coastal regions using convolutional neural network B. Yuan et al.
36. Deep Learning for Bias‐Correcting CMIP6‐Class Earth System Models P. Hess et al.
37. A visual analytics approach to exploring regional physical processes reflected in generative climate downscaling models P. Chang et al.
38. Deep learning-based bias correction of ISMR simulated by GCM S. Sharma et al.
39. A tailored deep learning method to improve spatial rainfall downscaling T. Li et al.
40. A deep learning-based bias correction model for Arctic sea ice concentration towards MITgcm S. Yuan et al.
41. Enhancing precipitation intensity estimation using ERA5-land reanalysis with statistical and machine learning approaches A. Abdolmanafi et al.
42. A Data-Driven Approach to Improve Cocoa Crop Establishment in Colombia: Insights and Agricultural Practice Recommendations from an Ensemble Machine Learning Model L. Talero-Sarmiento et al.
43. START: A Hybrid Spatio-Temporal Attention ResNet Transformer for Explainable Multivariable Meteorological Bias-correction D. Singh et al.