Weather Forecasting
102 papers with code • 2 benchmarks • 13 datasets
Weather Forecasting is the prediction of future weather conditions such as precipitation, temperature, pressure and wind.
Source: MetNet: A Neural Weather Model for Precipitation Forecasting
Libraries
Use these libraries to find Weather Forecasting models and implementationsDatasets
nuScenes
A2D2
Shifts
SEVIR
Oxford Radar RobotCar Dataset
IowaRain
Hurricane
Latest papers with no code
Advancing Data-driven Weather Forecasting: Time-Sliding Data Augmentation of ERA5
In this new paradigm, our research introduces a novel strategy that deviates from the common dependence on high-resolution data, which is often constrained by computational resources, and instead utilizes low-resolution data (2. 5 degrees) for global weather prediction and climate data analysis.
Denoising Diffusion Probabilistic Models in Six Simple Steps
Denoising Diffusion Probabilistic Models (DDPMs) are a very popular class of deep generative model that have been successfully applied to a diverse range of problems including image and video generation, protein and material synthesis, weather forecasting, and neural surrogates of partial differential equations.
Weather Prediction with Diffusion Guided by Realistic Forecast Processes
Weather forecasting remains a crucial yet challenging domain, where recently developed models based on deep learning (DL) have approached the performance of traditional numerical weather prediction (NWP) models.
Hyper-Diffusion: Estimating Epistemic and Aleatoric Uncertainty with a Single Model
In this work we introduce a new approach to ensembling, hyper-diffusion, which allows one to accurately estimate epistemic and aleatoric uncertainty with a single model.
Comparative Evaluation of Weather Forecasting using Machine Learning Models
Gaining a deeper understanding of weather and being able to predict its future conduct have always been considered important endeavors for the growth of our society.
Efficient Subseasonal Weather Forecast using Teleconnection-informed Transformers
Subseasonal forecasting, which is pivotal for agriculture, water resource management, and early warning of disasters, faces challenges due to the chaotic nature of the atmosphere.
Improving Global Weather and Ocean Wave Forecast with Large Artificial Intelligence Models
The rapid advancement of artificial intelligence technologies, particularly in recent years, has led to the emergence of several large parameter artificial intelligence weather forecast models.
FengWu-GHR: Learning the Kilometer-scale Medium-range Global Weather Forecasting
Therefore, building a kilometer-scale global forecast model is a persistent pursuit in the meteorology domain.
A Practical Probabilistic Benchmark for AI Weather Models
We also reveal how multiple time-step loss functions, which many data-driven weather models have employed, are counter-productive: they improve deterministic metrics at the cost of increased dissipation, deteriorating probabilistic skill.
VN-Net: Vision-Numerical Fusion Graph Convolutional Network for Sparse Spatio-Temporal Meteorological Forecasting
Sparse meteorological forecasting is indispensable for fine-grained weather forecasting and deserves extensive attention.
