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- Improving Subseasonal‐To‐Seasonal Prediction of Summer Extreme . . .
In this study, a deep learning method based on U-Net with additional atmospheric factor forecasts included is proposed to improve S2S quantitative forecasts of summer precipitation over Southern China
- Quantifying sources of subseasonal prediction skill in CESM2
Here we use a set of subseasonal reforecast experiments with CESM2 to quantify the respective roles of atmosphere, land, and ocean initial conditions on subseasonal prediction skill over
- Current progress in subseasonal-to-decadal prediction based on machine . . .
The application of machine learning (ML) techniques to climate science has received significant attention, particularly in the field of climate predictions, ranging from sub-seasonal to decadal time scales
- NeuralOM: Neural Ocean Model for Subseasonal-to-Seasonal Simulation
We validate NeuralOM on the challenging task of global Subseasonal-to-Seasonal (S2S) ocean simulation Extensive experiments demonstrate that NeuralOM not only surpasses state-of-the-art models in forecast accuracy and long-term stability, but also excels in simulating extreme events
- Deep learning-based subseasonal to seasonal precipitation prediction in . . .
This study compares three deep learning algorithms, namely, the long short-term memory recurrent (LSTM), gated recurrent unit (GRU), and recurrent neural network (RNN), and selects the optimal algorithm to establish an S2S precipitation prediction model
- Subseasonal to Seasonal (S2S) Prediction Algorithms Using Hybrid . . .
This study aims to investigate which features are useful for the subseasonal to seasonal prediction with a time scale of 2 weeks to 3 months based on hybrid machine learning techniques
- Nature Communications | 李昊 陆波 漆远团队揭秘“伏羲”次 . . .
复旦大学人工智能创新与产业研究院、上海科学智能研究院李昊研究员与漆远教授团队携手中国气象局气候研究开放实验室陆波研究员团队在国际权威综合性期刊《Nature Communications》杂志发表题为“A machine learning model that outperforms conventional global subseasonal forecast
- Improving Subseasonal‐To‐Seasonal Prediction of Summer Extreme . . .
The reliable Subseasonal‐to‐Seasonal (S2S) forecast of precipitation, particularly extreme precipitation, is critical for disaster prevention and mitigation, which however remains a great
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