Neural general circulation models optimized to predict satellite-based precipitation observations

TL;DR

A hybrid model that is trained directly on satellite-based precipitation observations, which demonstrates significant improvements over existing general circulation models, the ERA5 reanalysis, and a global cloud-resolving model in simulating precipitation.

Abstract

Climate models struggle to accurately simulate precipitation, particularly extremes and the diurnal cycle. Here, we present a hybrid model that is trained directly on satellite-based precipitation observations. Our model runs at 2.8$^\circ$ resolution and is built on the differentiable NeuralGCM framework. The model demonstrates significant improvements over existing general circulation models, the ERA5 reanalysis, and a global cloud-resolving model in simulating precipitation. Our approach yields reduced biases, a more realistic precipitation distribution, improved representation of extremes, and a more accurate diurnal cycle. Furthermore, it outperforms the mid-range precipitation forecast of the ECMWF ensemble. This advance paves the way for more reliable simulations of current climate and demonstrates how training on observations can be used to directly improve GCMs.

Figures (6)

• Figure 1: Overall model structure. Inputs are encoded into the model state $x_{\rm{t}}$.This state is fed into the dynamical core and the learned precipitation module. Along with forcings and noise, the state is also used as input to the learned physics module. The dynamical core and learned physics module produce tendencies (rates of change) for an implicit-explicit ordinary differential equation (ODE) solver, which advances the state in time to $x_{\rm{t+1}}$. The precipitation module predicts the precipitation rate and, by enforcing water column conservation (Eq. \ref{['eq:p_minus_e']}), diagnoses the evaporation rate. The new model state can then be used for the next time step or decoded to produce outputs.
• Figure 2: Precipitation forecasting accuracy scores for 24-hour accumulated precipitation, evaluated against IMERG. Area-weighted mean, calculated over all longitudes and latitudes between $-60^\circ$ to $60^\circ$ for: (a) Continuous Ranked Probability Score (CRPS). (e) Ensemble mean root-mean-square error (RMSE). (i) Spread-skill ratio. (m) Root-mean-square bias (RMSB). (q) Brier score (0.95 quantile). Comparisons are shown for NeuralGCM, the ECMWF ensemble, and probabilistic climatology (see Methods). Spatial distributions of (b, c, d) CRPS, (f, g, h) RMSE, (j, k, l) spread-skill ratio, (n, o, p) RMSB, and (r, s, t) Brier score (0.95 quantile) for NeuralGCM, the ECMWF ensemble, and probabilistic climatology on the second day of forecasting.
• Figure 3: Hovmoller tropical precipitation diagram for different models. Precipitation is averaged between latitudes $-5^{\circ}$ and $5^{\circ}$. IMERG, NeuralGCM, X-SHiELD, and ERA5 data are shown for 91 days starting on April 20, 2020. CMIP model are shown for historical runs for 91 days starting on April 20, 2013. NeuralGCM run shown was initialized on December 27 2001. All models were coarse-grained to 2.8$^{\circ}$ before plotting.
• Figure 4: Bias in mean precipitation averaged over 2002–2014. (a, b) Box plots showing the mean absolute error (MAE) relative to IMERG for 37 NeuralGCM runs (initialized during 2001), 37 CMIP6 AMIP experiments (model details in Methods), ERA5, and GPCPhuffman2023new over (a) land and (b) ocean. In the box plots, the red line indicates the median; the box delineates the interquartile range (IQR); whiskers extend to 1.5 × IQR; and outliers are shown as dots. (c) IMERG mean precipitation averaged over 2002–2014. (d–i) Bias in mean precipitation from NeuralGCM, ERA5, GPCP, and three CMIP6 AMIP experiments. Global MAE (in mm/day) is shown for land and ocean regions.
• Figure 5: Tropical precipitation rate distribution and annual maximum daily precipitation (Rx1day) averaged over 2002–2014. (a) Frequency distributions of 24-hourly precipitation rate for IMERGhuffman2020integrated, NeuralGCM, ERA5, and IPSL-CM6A-LR (historical run) in the tropics (latitudes -20$^\circ$ to 20$^\circ$). (b) Relative distribution normalized by the IMERG value. (c) IMERG Rx1day calculated over 2002-2014. (d–i) Bias in Rx1day for NeuralGCM, ERA5, GPCPhuffman2023new, and various CMIP6 historical simulations, relative to IMERG. Global mean absolute error (MAE) relative to IMERG is shown for land and ocean regions (in mm/day). The NeuralGCM simulation was initialized on December 27, 2001. All models were coarsened to a $2.8^{\circ}$ resolution.