paper
2025
Observations & remote sensing
Regional & km-scale models Evaluation or intercomparison of existing models
Classical ML (trees, GP, linear, kernels)6 h–3 daysChina Meteorological Administration (CMA)Fudan University (FuXi)Nanjing University of Information Science & Technology (NUIST) Journal
2025-11-20
047
Nov 2025
You Wu, Hitoshi Irie, Wenwen Li et al.
Weather and Forecasting
China Meteorological Administration (CMA), Fudan University (FuXi), Nanjing University of Information Science & Technology (NUIST)
Couples the Dayu radiative transfer model with WRF to simulate Himawari brightness temperatures for Typhoon Hato, finding WRF underestimates low clouds, with an ML cloud classifier used for evaluation.
Evaluation of WRF Cloud Properties of Typhoon Hato Based on Forward Radiative Transfer Simulation and Machine Learning
In one sentence. Couples the Dayu radiative transfer model with WRF to simulate Himawari brightness temperatures for Typhoon Hato, finding WRF underestimates low clouds, with an ML cloud classifier used for evaluation.
Labels
- Topic
- Observations & remote sensing, Regional & km-scale models
- Type
- Evaluation or intercomparison of existing models
- Method
- Classical ML (trees, GP, linear, kernels)
- Time scale
- 6 h–3 days
- Domain
- Regional
- Data
- NWP analysis or forecasts (IFS, GFS, ...), Satellite
- Status
- Research
- Identifiers
- DOI:10.1175/waf-d-24-0205.1 OpenAlex:W4416436387
- Added
- 2026-10-22
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