Artificial intelligence for climate prediction of extremes: State of the art, challenges, and future perspectives
Extreme events such as heat waves and cold spells, droughts, heavy rain, and storms are
particularly challenging to predict accurately due to their rarity and chaotic nature, and …
particularly challenging to predict accurately due to their rarity and chaotic nature, and …
ClimaX: A foundation model for weather and climate
Most state-of-the-art approaches for weather and climate modeling are based on physics-
informed numerical models of the atmosphere. These approaches aim to model the non …
informed numerical models of the atmosphere. These approaches aim to model the non …
A machine learning model that outperforms conventional global subseasonal forecast models
Skillful subseasonal forecasts are crucial for various sectors of society but pose a grand
scientific challenge. Recently, machine learning-based weather forecasting models …
scientific challenge. Recently, machine learning-based weather forecasting models …
Artificial Intelligence for Prediction of Climate Extremes: State of the art, challenges and future perspectives
Scientific and technological advances in numerical modelling have improved the quality of
climate predictions over recent decades, but predictive skill remains limited in many aspects …
climate predictions over recent decades, but predictive skill remains limited in many aspects …
Towards an end-to-end artificial intelligence driven global weather forecasting system
The weather forecasting system is important for science and society, and significant
achievements have been made in applying artificial intelligence (AI) to medium-range …
achievements have been made in applying artificial intelligence (AI) to medium-range …
In-season maize yield prediction in Northeast China: The phase-dependent benefits of assimilating climate forecast and satellite observations
C Lu, G Leng, X Liao, H Tu, J Qiu, J Li, S Huang… - Agricultural and Forest …, 2024 - Elsevier
Various yield forecasting methods have been reported in literature, but the benefits of
assimilating seasonal climate forecasts and satellite observations for in-season yield …
assimilating seasonal climate forecasts and satellite observations for in-season yield …
Deepphysinet: Bridging deep learning and atmospheric physics for accurate and continuous weather modeling
Accurate weather forecasting holds significant importance to human activities. Currently,
there are two paradigms for weather forecasting: Numerical Weather Prediction (NWP) and …
there are two paradigms for weather forecasting: Numerical Weather Prediction (NWP) and …
A deconfounding approach to climate model bias correction
Global Climate Models (GCMs) are crucial for predicting future climate changes by
simulating the Earth systems. However, GCM outputs exhibit systematic biases due to model …
simulating the Earth systems. However, GCM outputs exhibit systematic biases due to model …
Deep learning improves GFS sea surface wind field forecast accuracy in the Northwest Pacific Ocean
S Fu, W Huang, J Luo, D Liu, D Sun… - Journal of …, 2024 - Wiley Online Library
Sea surface winds influence shipping, fisheries, and coastal projects. However, the current
sea surface wind forecast exhibits noticeable biases. This study introduces a deep learning …
sea surface wind forecast exhibits noticeable biases. This study introduces a deep learning …
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 …