paper
2018
Nowcasting
Observations & remote sensing New model or method
RNN / LSTM / ConvLSTM0–6 hUniversity of Oklahoma (AI2ES) Journal
2018-10-29
032
Oct 2018
Ata Akbari Asanjan, Tiantian Yang, Kuolin Hsu et al.
Journal of Geophysical Research Atmospheres
University of Oklahoma (AI2ES)
An LSTM extrapolates cloud-top brightness temperature, then PERSIANN retrieves 0–6 h rainfall, beating RNN, optical flow and RAP over three US regions.
Short‐Term Precipitation Forecast Based on the PERSIANN System and LSTM Recurrent Neural Networks
In one sentence. An LSTM extrapolates cloud-top brightness temperature, then PERSIANN retrieves 0–6 h rainfall, beating RNN, optical flow and RAP over three US regions.
Labels
- Topic
- Nowcasting, Observations & remote sensing
- Type
- New model or method
- Method
- RNN / LSTM / ConvLSTM
- Time scale
- 0–6 h
- Domain
- Regional
- Data
- Satellite
- Status
- Research
- Identifiers
- DOI:10.1029/2018jd028375 OpenAlex:W2898796705
- Added
- 2026-10-22
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