Improving the MJO Forecast of S2S Operation Models by Correcting Their Biases in Linear Dynamics-Reference-Cited by-同舟云学术

Improving the MJO Forecast of S2S Operation Models by Correcting Their Biases in Linear Dynamics

Published:2021-03-17 Issue:6 Volume:48 Page:
ISSN:0094-8276
Container-title:Geophysical Research Letters
language:en
Short-container-title:Geophys Res Lett

Author:

Wu Jie¹^ORCID,Jin Fei‐Fei²^ORCID

Affiliation:

1. Laboratory for Climate Studies & China Meteorological Administration‐Nanjing University Joint Laboratory for Climate Prediction Studies National Climate Center China Meteorological Administration Beijing China

2. Department of Atmospheric Sciences SOEST University of Hawaii at Mānoa Honolulu HI USA

Publisher

American Geophysical Union (AGU)

Subject

General Earth and Planetary Sciences,Geophysics

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1029/2020GL091930

Reference59 articles.

1. Three‐dimensional structure and evolution of the MJO and its relation to the mean flow;Adames A. F.;Journal of the Atmospheric Sciences,2014

2. Three-Dimensional Structure and Evolution of the Moisture Field in the MJO

3. Seasonality of the Structure and Propagation Characteristics of the MJO

4. Intraseasonal interaction between the Madden–Julian Oscillation and the North Atlantic Oscillation

5. The skill of atmospheric linear inverse models in hindcasting the Madden–Julian Oscillation

Cited by 14 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. A machine learning model that outperforms conventional global subseasonal forecast models;Nature Communications;2024-07-30

2. The Effect of Moisture in Different Altitude Layers on the Eastward Propagation of MJO;Journal of Meteorological Research;2024-06

3. Improved subseasonal prediction of South Asian monsoon rainfall using data-driven forecasts of oscillatory modes;Proceedings of the National Academy of Sciences;2024-04

4. Evaluation and correction of sub-seasonal dynamic model forecast of precipitation in eastern China;Climate Dynamics;2023-05-19

5. Taking advantage of quasi-periodic signals for S2S operational forecast from a perspective of deep learning;Scientific Reports;2023-03-13