Hierarchical multimodel ensemble probabilistic forecasts for precipitation over East Asia
Abstract Bayesian model averaging (BMA) and ensemble model output statistics (EMOS), as two state‐of‐the‐art approaches, were applied to improve the prediction skills of 24‐h accumulated precipitation over East Asia with lead days of 1–7 days. The multimodel ensemble precipitation probabilistic fore...
Saved in:
| Main Authors: | Luying Ji, Xiefei Zhi, Qixiang Luo, Yan Ji |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Wiley
2025-03-01
|
| Series: | Meteorological Applications |
| Subjects: | |
| Online Access: | https://doi.org/10.1002/met.70035 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Heavy Precipitation Forecasts Based on Multi-model Ensemble Members
by: Zhi Xiefei, et al.
Published: (2020-05-01) -
DR potential probabilistic forecasting model of load aggregators based on ensemble learning
by: YEERSEN Sailike, et al.
Published: (2025-04-01) -
Forecasting Magnitude and Frequency of Seasonal Streamflow Extremes Using a Bayesian Hierarchical Framework
by: Álvaro Ossandón, et al.
Published: (2023-07-01) -
Adapting Ensemble‐Calibration Techniques to Probabilistic Solar‐Wind Forecasting
by: N. O. Edward‐Inatimi, et al.
Published: (2024-12-01) -
Long-Term Multi-Resolution Probabilistic Load Forecasting Using Temporal Hierarchies
by: Shafie Bahman, et al.
Published: (2025-06-01)