[2010.14610] Improving seasonal forecast using probabilistic deep learningopen searchopen navigation menucontact arXivsubscribe to arXiv mailings

The path toward realizing the potential of seasonal forecasting and its socioeconomic benefits depends heavily on improving general circulation model based dynamical forecasting systems. To improve dynamical seasonal forecast, it is crucial to set up forecast benchmarks, and clarify forecast limitations posed by model initialization errors, formulation deficiencies, and internal climate variability. With huge cost in generating large forecast ensembles, and limited observations for forecast verification, the seasonal forecast benchmarking and diagnosing task proves challenging. In this study, we develop a probabilistic deep neural network model, drawing on a wealth of existing climate simulations to enhance seasonal forecast capability and forecast diagnosis. By leveraging complex physical relationships encoded in climate simulations, our probabilistic forecast model demonstrates favorable deterministic and probabilistic skill compared to state-of-the-art dynamical forecast systems in

4 mentions: @AkiraTOSEI@AkiraTOSEI@Provectus_inc
Keywords: deep learning
Date: 2020/11/18 14:21

Referring Tweets

@AkiraTOSEI t.co/ETjI9lkb3B 世界規模の気候予測を、気候シミュレーションモデルに基づいた深層学習モデル(VAE)で設計した研究。エルニーニョ現象がどう地球規模に影響していくかなどを予測できる。 t.co/GUVDxLp0t2
@AkiraTOSEI t.co/ETjI9lBLV9 A study of global climate predictions designed by deep learning models (VAE) based on climate simulation models. It can predict how the El Nino phenomenon will affect the global scale. t.co/OjJMW8c5Ve
@Provectus_inc 📈Kicking off the week with some #reseach: "Improving seasonal forecast using probabilistic #deeplearning" by Baoxiang Pan, Gemma J. Anderson et al. #artificial_intelligence #MachineLearning #Forecasting t.co/lu2nmV6axJ

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