Can Machines Learn to Predict Weather? Using Deep Learning to Predict Gridded 500‐hPa Geopotential Height From Historical Weather Data

By Jonathan A. Weyn, Dale R. Durran, R. Caruana. Journal of Advances in Modeling Earth Systems, 2019

315 citations recorded by the source corpus.

We develop elementary weather prediction models using deep convolutional neural networks (CNNs) trained on past weather data to forecast one or two fundamental meteorological fields on a Northern Hemisphere grid with no explicit knowledge about physical processes. At forecast lead times up to 3 days, CNNs trained to predict only 500‐hPa geopotential height easily outperform persistence, climatology, and the dynamics‐based barotropic vorticity model, but do not beat an operational full‐physics weather prediction model. These CNNs are capable of forecasting significant changes in the intensity of weather systems, which is notable because this is beyond the capability of the fundamental dynamical equation that relies solely on 500‐hPa data, the barotropic vorticity equation. Modest improvements to the CNN forecasts can be made by adding 700‐ to 300‐hPa thickness to the input data. Our best performing CNN does a good job of capturing the climatology and annual variability of 500‐hPa heights and is capable of forecasting realistic atmospheric states at lead times of 14 days. Although our simple models do not perform better than an operational weather model, machine learning warrants further exploration as a weather forecasting tool; in particular, the potential efficiency of CNNs might make them attractive for ensemble forecasting.

Subfields: Atmospheric Science, Environmental Engineering, Global and Planetary Change

View Paper: https://www.semanticscholar.org/paper/0bdd9c714582904c2dd31c2175f221a6a36ba5d0

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Citations in the corpus

WeatherBench 2: A Benchmark for the Next Generation of Data‐Driven Global Weather Models (2023, background): “A number of initial attempts at building data-driven medium-range NWP models (Dueben and Bauer, 2018; Scher, 2018; Weyn et al., 2019) led to the creation of WeatherBench (Rasp et al.”
WeatherBench: A Benchmark Data Set for Data‐Driven Weather Forecasting (2020, methodology): “ than chaining together short-term forecasts. However, the poor skill of the iterative forecast could easily be a result of using an overly simplistic network architecture. The iterative forecasts of Weyn et al. (2019), who employ a more complex network structure, show stable long term performance u”
Backpropagation algorithms and Reservoir Computing in Recurrent Neural Networks for the forecasting of complex spatiotemporal dynamics (2019, background): “…far-reaching implications for societal and scientific challenges in fields ranging from weather and climate prediction (Gneiting & Raftery, 2005; Weyn, Durran, & Caruana, 2019), medicine analytics (Esteva et al., 2017; Kurth et al., 2018), to modeling ocean dynamics and turbulent flows…”
FourCastNet: A Global Data-driven High-resolution Weather Model using Adaptive Fourier Neural Operators (2022, background): “…on climate model outputs, general circulation models (GCM) [Scher and Messori, 2018, 2019, Chattopadhyay et al., 2020a], reanalysis products [Weyn et al., 2019, 2020, 2021, Rasp et al., 2020, Rasp and Thuerey, 2021a, 2020, Chattopadhyay et al., 2021, Arcomano et al., 2020, Chantry et al.,…”