Sub-seasonal forecasting with a large ensemble of deep-learning weather prediction models

We present an ensemble prediction system using a Deep Learning Weather Prediction (DLWP) model that recursively predicts key atmospheric variables with six-hour time resolution. This model uses convolutional neural networks (CNNs) on a cubed sphere grid to produce global forecasts. The approach is computationally efficient, requiring just three minutes on a single GPU to produce a 320-member set of six-week forecasts at 1.4{\deg} resolution. Ensemble spread is primarily produced by randomizing the CNN training process to create a set of 32 DLWP models with slightly different learned weights. Although our DLWP model does not forecast precipitation, it does forecast total column water vapor, and it gives a reasonable 4.5-day deterministic forecast of Hurricane Irma. In addition to simulating mid-latitude weather systems, it spontaneously generates tropical cyclones in a one-year free-running simulation. Averaged globally and over a two-year test set, the ensemble mean RMSE retains skill relative to climatology beyond two-weeks, with anomaly correlation coefficients remaining above 0.6 through six days. Our primary application is to subseasonal-to-seasonal (S2S) forecasting at lead times from two to six weeks. Current forecast systems have low skill in predicting one- or 2-week-average weather patterns at S2S time scales. The continuous ranked probability score (CRPS) and the ranked probability skill score (RPSS) show that the DLWP ensemble is only modestly inferior in performance to the European Centre for Medium Range Weather Forecasts (ECMWF) S2S ensemble over land at lead times of 4 and 5-6 weeks. At shorter lead times, the ECMWF ensemble performs better than DLWP.Comment: Submitted to Journal of Advances in Modeling Earth System

Advancing Parsimonious Deep Learning Weather Prediction using the HEALPix Mesh

We present a parsimonious deep learning weather prediction model on the Hierarchical Equal Area isoLatitude Pixelization (HEALPix) to forecast seven atmospheric variables for arbitrarily long lead times on a global approximately 110 km mesh at 3h time resolution. In comparison to state-of-the-art machine learning weather forecast models, such as Pangu-Weather and GraphCast, our DLWP-HPX model uses coarser resolution and far fewer prognostic variables. Yet, at one-week lead times its skill is only about one day behind the state-of-the-art numerical weather prediction model from the European Centre for Medium-Range Weather Forecasts. We report successive forecast improvements resulting from model design and data-related decisions, such as switching from the cubed sphere to the HEALPix mesh, inverting the channel depth of the U-Net, and introducing gated recurrent units (GRU) on each level of the U-Net hierarchy. The consistent east-west orientation of all cells on the HEALPix mesh facilitates the development of location-invariant convolution kernels that are successfully applied to propagate global weather patterns across our planet. Without any loss of spectral power after two days, the model can be unrolled autoregressively for hundreds of steps into the future to generate stable and realistic states of the atmosphere that respect seasonal trends, as showcased in one-year simulations. Our parsimonious DLWP-HPX model is research-friendly and potentially well-suited for sub-seasonal and seasonal forecasting

Rapid 20th century warming reverses 900-year cooling in the Gulf of Maine

The Gulf of Maine, located in the western North Atlantic, has undergone recent, rapid ocean warming but the lack of long-term, instrumental records hampers the ability to put these significant hydrographic changes into context. Here we present multiple 300-year long geochemical records (oxygen, nitrogen, and previously published radiocarbon isotopes) measured in absolutely-dated Arctica islandica shells from the western Gulf of Maine. These records, in combination with climate model simulations, suggest that the Gulf of Maine underwent a long-term cooling over most of the last 1000 years, driven primarily by volcanic forcing and North Atlantic ocean dynamics. This cooling trend was reversed by warming beginning in the late 1800s, likely due to increased atmospheric greenhouse gas concentrations and changes in western North Atlantic circulation. The climate model simulations suggest that the warming over the last century was more rapid than almost any other 100-year period in the last 1000 years in the region

A Sea Surface Model for Coupled Data-Driven S2S Forecasting

Thesis (Master's)--University of Washington, 2023Data-driven modelling of the atmosphere has rapidly become a vibrant area of research. Recent studies have shown these models have the ability to outperform existing state-of-the-artnumerical weather prediction models. Many of these efforts, however, remain targeted at short range forecasts (within 2 weeks). We propose using recent advancements in machine learning to extend the window of predictive skill to the seasonal to subseasonal timescales (2-10 weeks). To do this we believe capturing couplings between Earth system components is necessary. Toward this end we have developed an entirely data-driven sea surface model. Our model predicts global sea surface temperature at daily resolution and can be run iterative like traditional circulation models. We find that even without atmospheric influence, our ocean model can produce skillful forecasts, consistently beating persistence and outperforming a climatological forecast out to 60 days. We also succeed in predicting the extreme El Nino event of 2015 at extended leadtimes. Interestingly, our models can run freely for over a year without producing unstable behavior even though they have no prescribed physical constraints such as conservation of energy. Furthermore we show that adding information about the atmosphere can significantly improve upon model performance suggesting that a these architectures are capable of learning coupled atmosphere-ocean interactions. Our study is an important step toward developing a fully coupled Deep Learning Earth System Model