Taking advantage of quasi-periodic signals for S2S operational forecast from a perspective of deep learning
Y Zhou, Q Zhao - Scientific Reports, 2023 - nature.com
The quasi-periodic signals in the earth system could be the predictability source for sub-
seasonal to seasonal (S2S) climate prediction because of the connections among the lead …
seasonal to seasonal (S2S) climate prediction because of the connections among the lead …
Soft Periodic Convolutional Recurrent Network for Spatiotemporal Climate Forecast
Many machine-learning applications and methods are emerging to solve problems
associated with spatiotemporal climate forecasting; however, a prediction algorithm that …
associated with spatiotemporal climate forecasting; however, a prediction algorithm that …
Data-driven Global Subseasonal Forecast Model (GSFM v1. 0) for intraseasonal oscillation components
C Lu, D Huang, Y Shen, F Xin - Geoscientific Model …, 2022 - gmd.copernicus.org
As a challenge in the construction of a “seamless forecast” system, improving the prediction
skills of subseasonal forecasts is a key issue for meteorologists. In view of the evolution …
skills of subseasonal forecasts is a key issue for meteorologists. In view of the evolution …
Data-Driven Global Subseasonal Forecast for Intraseasonal Oscillation Components
Y Shen, C Lu, Y Wang, D Huang, F Xin - Atmosphere, 2023 - mdpi.com
As a challenge in the construction of a “seamless forecast” system, improving the prediction
skills of subseasonal forecasts is a key issue for meteorologists. In view of the evolution …
skills of subseasonal forecasts is a key issue for meteorologists. In view of the evolution …
FuXi-S2S: An accurate machine learning model for global subseasonal forecasts
Skillful subseasonal forecasts beyond 2 weeks are crucial for a wide range of applications
across various sectors of society. Recently, state-of-the-art machine learning based weather …
across various sectors of society. Recently, state-of-the-art machine learning based weather …
Exploring dominant processes for multi-month predictability of western Pacific precipitation using deep learning
Over the past half-century, there has been an increasing trend in the magnitude and
duration of the Madden-Julian Oscillation (MJO) attributable to the significant warming trend …
duration of the Madden-Julian Oscillation (MJO) attributable to the significant warming trend …
Forecasting ENSO using convolutional LSTM network with improved attention mechanism and models recombined by genetic algorithm in CMIP5/6
Abstract El Niño-Southern Oscillation (ENSO) has a profound impact on global climate, and
the ability to forecast it effectively over the long term is essential. In recent years, deep …
the ability to forecast it effectively over the long term is essential. In recent years, deep …
ENSO-ASC 1.0. 0: ENSO deep learning forecast model with a multivariate air–sea coupler
B Mu, B Qin, S Yuan - Geoscientific Model Development, 2021 - gmd.copernicus.org
The El Niño–Southern Oscillation (ENSO) is an extremely complicated ocean–atmosphere
coupling event, the development and decay of which are usually modulated by the energy …
coupling event, the development and decay of which are usually modulated by the energy …
[HTML][HTML] Improving the accuracy of subseasonal forecasting of China precipitation with a machine learning approach
C Wang, Z Jia, Z Yin, F Liu, G Lu, J Zheng - Frontiers in Earth Science, 2021 - frontiersin.org
Precipitation change, which is closely related to drought and flood disasters in China, affects
billions of people every year, and the demand for subseasonal forecasting of precipitation is …
billions of people every year, and the demand for subseasonal forecasting of precipitation is …
Deep learning for bias correction of MJO prediction
Producing accurate weather prediction beyond two weeks is an urgent challenge due to its
ever-increasing socioeconomic value. The Madden-Julian Oscillation (MJO), a planetary …
ever-increasing socioeconomic value. The Madden-Julian Oscillation (MJO), a planetary …