A Variational U-Net for Weather Forecasting
Not only can discovering patterns and insights from atmospheric data enable more accurate weather predictions, but it may also provide valuable information to help tackle climate change. Weather4cast is an open competition that aims to evaluate machine learning algorithms' capability to predict future atmospheric states. Here, we describe our third-place solution to Weather4cast. We present a novel Variational U-Net that combines a Variational Autoencoder's ability to consider the probabilistic nature of data with a U-Net's ability to recover fine-grained details. This solution is an evolution from our fourth-place solution to Traffic4cast 2020 with many commonalities, suggesting its applicability to vastly different domains, such as weather and traffic.
Code (1)
Tasks
Weather ForecastingMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
FengWu-4DVar: Coupling the Data-driven Weather Forecasting Model with 4D Variational Assimilation
Weather forecasting is a crucial yet highly challenging task. With the maturity of Artificial Intelligence (AI), the emergence of data-driven weather forecasting models has opened up a new paradigm for the development of…
Weather ForecastingXiChen: A global weather observation-to-forecast machine learning system via four-dimensional variational gradient-guided flexible assimilation
Machine Learning (ML) has shown great promise in revolutionizing weather forecasting, yet most ML systems still rely on initial conditions generated by Numerical Weather Prediction (NWP) systems. End-to-end ML models aim…
Weather ForecastingSwinVRNN: A Data-Driven Ensemble Forecasting Model via Learned Distribution Perturbation
Data-driven approaches for medium-range weather forecasting are recently shown extraordinarily promising for ensemble forecasting for their fast inference speed compared to traditional numerical weather prediction (NWP) …
Weather ForecastingData Assimilation with Machine Learning Surrogate Models: A Case Study with FourCastNet
Modern data-driven surrogate models for weather forecasting provide accurate short-term predictions but inaccurate and nonphysical long-term forecasts. This paper investigates online weather prediction using machine lear…
Weather ForecastingTimeCatcher: A Variational Framework for Volatility-Aware Forecasting of Non-Stationary Time Series
Recent lightweight MLP-based models have achieved strong performance in time series forecasting by capturing stable trends and seasonal patterns. However, their effectiveness hinges on an implicit assumption of local sta…
Time Series Forecasting