Site-Specific Deterministic Temperature and Humidity Forecasts with Explainable and Reliable Machine Learning
This study investigates the feasibility of optimizing site-specific temperature and dew point forecasts by adopting the gradient boosting decision tree model XGBoost, supported by insights from Shapley Additive Explanations (SHAP) to increase the reliability of the machine learning-based forecasts.
Aardvark Weather: An End-to-End Data-Driven System for Accurate and Efficient Global Weather Forecasting
Aardvark Weather is the first end-to-end data-driven weather forecasting system that takes raw observational data as input and provides skillful global and local weather forecasts without relying on traditional numerical weather prediction models.
Tropical Cyclone Forecasting with Cascaded Diffusion Models at ICLR 2024 Workshop
AI-based diffusion models provide accurate and efficient tropical cyclone forecasts, crucial for vulnerable regions.
HEAL-ViT: Vision Transformers on a Spherical Mesh for Medium-Range Weather Forecasting
HEAL-ViT introduces a novel architecture that combines the benefits of graph-based models and transformers to improve medium-range weather forecasting.
ExtremeCast: Enhancing Extreme Value Prediction for Global Weather Forecast
MSE-based models struggle with extreme weather prediction, but Exloss and ExEnsemble improve accuracy.
ExtremeCast: Enhancing Extreme Value Prediction for Global Weather Forecast
Using Exloss and ExEnsemble improves extreme weather prediction accuracy.
Ensemble of Data-Driven Weather Prediction Models for Sub-Seasonal Forecasting
Data-driven ensemble models improve sub-seasonal forecasting accuracy.
Deep Learning-Based Weather Forecasting Method in Itoshima, Japan
Accurate weather forecasting using a multilayer perceptron model tailored for Itoshima, Japan.
Online Model Error Correction with Neural Networks: Enhancing Forecasting Accuracy
The author proposes a hybrid modeling approach to address the limitations of data-driven weather prediction models by integrating physics-based and statistical components, such as neural networks, to improve forecasting accuracy.
Conformer: Embedding Continuous Attention in Vision Transformer for Weather Forecasting
Conformer introduces continuous attention to capture spatio-temporal features in weather forecasting, outperforming existing models.
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