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Nowcast-to-Forecast: Token-Based Multiple Remote Sensing Data Fusion for Precipitation Forecast

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What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

inferredcites →https://doi.org/10.1002/met.296openalex-snapshot

A long‐term assessment of precipitation forecast skill using the Fractions Skill Score

inferredcites →https://doi.org/10.1007/s13143-018-0028-9openalex-snapshot

The Korean Integrated Model (KIM) System for Global Weather Forecasting

inferredcites →https://doi.org/10.1016/j.sciaf.2022.e01246openalex-snapshot

Machine learning models for prediction of rainfall over Nigeria

inferredcites →https://doi.org/10.1029/2002jd003359openalex-snapshot

A study of the direct and indirect effects of aerosols using global satellite data sets of aerosol and cloud parameters

inferredcites →https://doi.org/10.1038/s41467-022-32483-xopenalex-snapshot

Deep learning for twelve hour precipitation forecasts

inferredcites →https://doi.org/10.1038/s41586-021-03854-zopenalex-snapshot

Skilful precipitation nowcasting using deep generative models of radar

inferredcites →https://doi.org/10.1038/s41597-022-01304-7openalex-snapshot

Temporal disaggregation of hourly precipitation under changing climate over the Southeast United States

inferredcites →https://doi.org/10.1109/tgrs.2019.2942280openalex-snapshot

A Machine Learning System for Precipitation Estimation Using Satellite and Ground Radar Network Observations

inferredcites →https://doi.org/10.3390/atmos10030123openalex-snapshot

An Alternative Bilinear Interpolation Method Between Spherical Grids

inferredcites →https://doi.org/10.3390/atmos10050244openalex-snapshot

Computer Vision in Precipitation Nowcasting: Applying Image Quality Assessment Metrics for Training Deep Neural Networks

inferredcites →https://doi.org/10.3390/atmos12020261openalex-snapshot

Enhancing the Encoding-Forecasting Model for Precipitation Nowcasting by Putting High Emphasis on the Latest Data of the Time Step

inferredcites →https://doi.org/10.48550/arxiv.2006.10739openalex-snapshot

Fourier Features Let Networks Learn High Frequency Functions in Low Dimensional Domains

inferredcites →https://doi.org/10.48550/arxiv.2207.05833openalex-snapshot

Earthformer: Exploring Space-Time Transformers for Earth System Forecasting

inferredcites →https://doi.org/10.5194/egusphere-egu23-9531openalex-snapshot

Latent diffusion models for generative nowcasting and uncertainty quantification of precipitation fields

inferredcites →https://doi.org/10.5194/gmd-12-4185-2019openalex-snapshot

Pysteps: an open-source Python library for probabilistic precipitation nowcasting (v1.0)

What points at this work

Incoming claims: where this work appears in other papers’ reference lists and arguments.

inferred← citeshttps://doi.org/10.2139/ssrn.4888852openalex-extracted

Deep Learning for Precipitation Nowcasting: A Survey from the Perspective of Time Series Forecasting

inferred← citeshttps://doi.org/10.1007/978-981-95-6733-1_2openalex-snapshot

SDAUnet: A Satellite and Radar-Based Feature Fusion Approach for Short-Term Precipitation Forecasting

inferred← citeshttps://doi.org/10.1016/j.eswa.2024.126301openalex-snapshot

Deep learning for precipitation nowcasting: A survey from the perspective of time series forecasting

inferred← citeshttps://doi.org/10.1088/2632-2153/ad8981openalex-snapshot

Observation impact explanation in atmospheric state estimation using hierarchical message-passing graph neural networks*

inferred← citeshttps://doi.org/10.1109/tgrs.2025.3587883openalex-snapshot

RSG-GAN: A GAN-Based Precipitation Nowcasting Model Integrating Radar QPE, GOES-16 SWD, and GNSS ZTDs

inferred← citeshttps://doi.org/10.3390/rs16162871openalex-snapshot

Oceanic Precipitation Nowcasting Using a UNet-Based Residual and Attention Network and Real-Time Himawari-8 Images

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