paper
2018
Observations & remote sensing
New model or method
OtherNASACalifornia Institute of Technology (Caltech)Tsinghua University (NowcastNet)University of California, Los Angeles (UCLA)University of Oklahoma (AI2ES) Journal
2018-10-01
047
Oct 2018
Guoqiang Tang, Di Long, Ali Behrangi et al.
Water Resources Research
NASA, California Institute of Technology (Caltech), Tsinghua University (NowcastNet), University of California, Los Angeles (UCLA), University of Oklahoma (AI2ES)
A deep neural network retrieves high-latitude rain and snow from GMI and MODIS data, largely outperforming GPROF on snowfall and reducing rain underestimation.
Exploring Deep Neural Networks to Retrieve Rain and Snow in High Latitudes Using Multisensor and Reanalysis Data
In one sentence. A deep neural network retrieves high-latitude rain and snow from GMI and MODIS data, largely outperforming GPROF on snowfall and reducing rain underestimation.
Labels
- Topic
- Observations & remote sensing
- Type
- New model or method
- Method
- Other
- Domain
- Regional
- Data
- Satellite, ERA5 / reanalysis
- Status
- Research
- Identifiers
- DOI:10.1029/2018wr023830 OpenAlex:W2897265228
- Added
- 2026-10-22
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