paper
2022
Extremes & hazards
Sub-seasonal to seasonal New model or method
Classical ML (trees, GP, linear, kernels)2 weeks–2 monthsNanjing University of Information Science & Technology (NUIST) Journal
2022-02-28
022
Feb 2022
Md. Jalal Uddin, Yubin Li, Md. Yahya Tamim et al.
Journal of Applied Meteorology and Climatology
Nanjing University of Information Science & Technology (NUIST)
Random forest forecasts monthly extreme rainfall indices over Bangladesh 1–3 months ahead using CAPE, humidity, and ENSO/IOD/NAO teleconnections.
Extreme Rainfall Indices Prediction with Atmospheric Parameters and Ocean–Atmospheric Teleconnections Using a Random Forest Model
In one sentence. Random forest forecasts monthly extreme rainfall indices over Bangladesh 1–3 months ahead using CAPE, humidity, and ENSO/IOD/NAO teleconnections.
Labels
- Topic
- Extremes & hazards, Sub-seasonal to seasonal
- Type
- New model or method
- Method
- Classical ML (trees, GP, linear, kernels)
- Time scale
- 2 weeks–2 months
- Domain
- Regional
- Data
- ERA5 / reanalysis, Other
- Status
- Research
- Identifiers
- DOI:10.1175/jamc-d-21-0170.1 OpenAlex:W4214604383
- Added
- 2026-10-22
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