Research graph
References from Cross-Regional Transfer Learning for Radar Echo Extrapolation in Northwestern Xinjiang, Western China. Local targets link to admitted publications; unresolved targets remain external evidence.
Unresolved reference
External reference
Towards nowcasting in Europe in 2030
10.1002/met.2124 · 2023 · External reference
10.3390/atmos13050815
10.3390/atmos13050815 · External reference
Skilful precipitation nowcasting using deep generative models of radar
10.1038/s41586-021-03854-z · 2021 · External reference
Understanding the mechanisms of summer extreme precipitation events in Xinjiang of arid Northwest China
10.1029/2020jd034111 · 2021 · External reference
Analysis of convective and stratiform precipitation characteristics in the summers of 2014–2019 over Northwest China based on GPM observations
10.1016/j.atmosres.2021.105762 · 2021 · External reference
Advancing radar nowcasting through deep transfer learning
2022 · External reference
Unresolved reference
External reference
Unresolved reference
External reference
Unresolved reference
External reference
10.1109/cvpr.2019.00937
10.1109/cvpr.2019.00937 · External reference
10.1109/cvpr52688.2022.00317
10.1109/cvpr52688.2022.00317 · External reference
SimVPv2: Towards simple yet powerful spatiotemporal predictive learning
10.1109/tmm.2025.3543051 · 2025 · External reference
10.1109/cvpr52729.2023.01800
10.1109/cvpr52729.2023.01800 · External reference
10.52202/068431-1841
10.52202/068431-1841 · External reference
TempEE: Temporal-spatial parallel transformer for radar echo extrapolation beyond autoregression
2023 · External reference
SFTformer: A spatial-frequency-temporal correlation-decoupling transformer for radar echo extrapolation
2024 · External reference
Learning from precipitation events in the wider domain to improve the performance of a deep learning-based precipitation nowcasting model
10.1175/waf-d-21-0078.1 · 2022 · External reference
Enhancing nowcasting with multi-resolution inputs using deep learning: Exploring model decision mechanisms
10.1029/2024gl113699 · 2025 · External reference
Evaluation of spatial-temporal distribution of precipitation in mainland China by statistic and clustering methods
10.1016/j.atmosres.2021.105772 · 2021 · External reference
Coverage of China new generation weather radar network
10.1155/2019/5789358 · 2019 · External reference
Application of the Doppler weather radar in real-time quality control of hourly gauge precipitation in eastern China
10.1016/j.atmosres.2015.12.016 · 2016 · External reference
Unresolved reference
External reference
Pysteps: An open-source Python library for probabilistic precipitation nowcasting (v1.0)
10.5194/gmd-12-4185-2019 · 2019 · External reference
Unresolved reference
External reference
Analysis of Convective and Stratiform Precipitation Characteristics in Xinjiang, China Based on GPM Dual-Frequency Precipitation Radar
10.1155/2024/8043060 · 2024 · External reference
Scale-Selective Verification of Rainfall Accumulations from High-Resolution Forecasts of Convective Events
10.1175/2007mwr2123.1 · 2008 · External reference
Assessing the spatial and temporal variation in the skill of precipitation forecasts from an NWP model
10.1002/met.57 · 2008 · External reference
Similarity of neural network representations revisited
2019 · External reference
Towards nowcasting in Europe in 2030
10.1002/met.2124 · ExternalCitation · doi-reference
Assessing the spatial and temporal variation in the skill of precipitation forecasts from an NWP model
10.1002/met.57 · ExternalCitation · doi-reference
Application of the Doppler weather radar in real-time quality control of hourly gauge precipitation in eastern China
10.1016/j.atmosres.2015.12.016 · ExternalCitation · doi-reference
Analysis of convective and stratiform precipitation characteristics in the summers of 2014–2019 over Northwest China based on GPM observations
10.1016/j.atmosres.2021.105762 · ExternalCitation · doi-reference
Evaluation of spatial-temporal distribution of precipitation in mainland China by statistic and clustering methods
10.1016/j.atmosres.2021.105772 · ExternalCitation · doi-reference
Understanding the mechanisms of summer extreme precipitation events in Xinjiang of arid Northwest China
10.1029/2020jd034111 · ExternalCitation · doi-reference
Enhancing nowcasting with multi-resolution inputs using deep learning: Exploring model decision mechanisms
10.1029/2024gl113699 · ExternalCitation · doi-reference
Skilful precipitation nowcasting using deep generative models of radar
10.1038/s41586-021-03854-z · ExternalCitation · doi-reference
10.1109/cvpr.2019.00937
10.1109/cvpr.2019.00937 · ExternalCitation · doi-reference
10.1109/cvpr52688.2022.00317
10.1109/cvpr52688.2022.00317 · ExternalCitation · doi-reference
10.1109/cvpr52729.2023.01800
10.1109/cvpr52729.2023.01800 · ExternalCitation · doi-reference
SimVPv2: Towards simple yet powerful spatiotemporal predictive learning
10.1109/tmm.2025.3543051 · ExternalCitation · doi-reference
Coverage of China new generation weather radar network
10.1155/2019/5789358 · ExternalCitation · doi-reference
Analysis of Convective and Stratiform Precipitation Characteristics in Xinjiang, China Based on GPM Dual-Frequency Precipitation Radar
10.1155/2024/8043060 · ExternalCitation · doi-reference
Scale-Selective Verification of Rainfall Accumulations from High-Resolution Forecasts of Convective Events
10.1175/2007mwr2123.1 · ExternalCitation · doi-reference
Learning from precipitation events in the wider domain to improve the performance of a deep learning-based precipitation nowcasting model
10.1175/waf-d-21-0078.1 · ExternalCitation · doi-reference
10.3390/atmos13050815
10.3390/atmos13050815 · ExternalCitation · doi-reference
Pysteps: An open-source Python library for probabilistic precipitation nowcasting (v1.0)
10.5194/gmd-12-4185-2019 · ExternalCitation · doi-reference
10.52202/068431-1841
10.52202/068431-1841 · ExternalCitation · doi-reference
