arXiv subjects

Diego G. Miralles

Publications and source records attributed to Diego G. Miralles.

3 recordsLinked to original sources

arXiv2026

Weather Emulators at the Frontier of Heat Extremes Predictability

Atmospheric predictability declines rapidly beyond the next ten days, such that forecasts at longer lead times primarily convey large-scale trends rather than specific states. Yet in a warming world, improving early warnings of extreme heat is an increasingly critical challenge. Here we evaluate six state-of-the-art deep learning weather emulators - Pangu-Weather, FuXi, ArchesWeather, AIFS, GraphCast and Aurora - alongside leading dynamical systems and statistical baselines in forecasting global near-surface temperature and extreme heat at lead times of 10-15 days. We find that several emulators rival or even surpass physics-based forecasts in deterministic temperature skill, but do so at the cost of reduced spectral fidelity, in a process widely known as blurring. While all models show some degree of predictive skill for extreme heat, most emulators under-represent peak intensities, and IFS recall is greater than that of any of the emulators. These results highlight both the emerging potential of AI to enhance extended range temperature prediction, and the remaining challenges in delivering reliable, actionable early warnings in a changing climate.

physics.ao-ph↗

arXiv2026

MSWEP V3: Machine Learning-Powered Global Precipitation Estimates at 0.1$^\circ$ Hourly Resolution (1979-Present)

We introduce Version 3 (V3) of the gridded near real-time Multi-Source Weighted-Ensemble Precipitation (MSWEP) product -- the first fully global, historical machine learning powered precipitation (P) dataset, developed to meet the growing demand for timely and accurate P estimates amid escalating climate challenges. MSWEP V3 provides hourly data at 0.1$^\circ$ resolution from 1979 to the present, continuously updated with a latency of approximately two hours. Development follows a two-stage process. First, baseline P fields are generated using machine learning model stacks that integrate satellite- and (re)analysis-based P and air-temperature products, along with static variables. The models are trained using hourly and daily observations from 15,959 P gauges worldwide. Second, these baseline P fields are corrected using daily and monthly gauge observations from 57,666 and 86,000 stations globally. To assess MSWEP V3's baseline performance, we evaluated 19 (quasi-) global gridded P products -- including both uncorrected and gauge-based products -- using observations from an independent set of 15,958 gauges excluded from the first training stage. The MSWEP V3 baseline achieved a median daily Kling-Gupta Efficiency (KGE) of 0.69, outperforming all evaluated products. Other uncorrected products achieved median daily KGE values of 0.61 (ERA5), 0.46 (IMERG-L V7), 0.38 (GSMaP V8), and 0.31 (CHIRP). Using leave-one-out cross-validation, the daily gauge correction was found to improve the median daily correlation by 0.09, constrained by the already strong baseline performance. We anticipate that MSWEP V3 -- accessible at www.gloh2o.org/mswep -- will enable more reliable monitoring, forecasting, and management of water-related risks in a variable and changing climate.

physics.ao-ph↗

arXiv2024

Vegetation-climate feedbacks across scales

Vegetation often understood merely as the result of long-term climate conditions. However, vegetation itself plays a fundamental role in shaping Earth's climate by regulating the energy, water, and biogeochemical cycles across terrestrial landscapes. It exerts influence by altering surface roughness, consuming significant water resources through transpiration and interception, lowering atmospheric CO2 concentration, and controlling net radiation and its partitioning into sensible and latent heat fluxes. This influence propagates through the atmosphere, from microclimate scales to the entire atmospheric boundary layer, subsequently impacting large-scale circulation and the global transport of heat and moisture. Understanding the feedbacks between vegetation and atmosphere across multiple scales is crucial for predicting the influence of land use and cover changes and for accurately representing these processes in climate models. This short review aims to discuss the mechanisms through which vegetation modulates climate across spatial and temporal scales. Particularly, we evaluate the influence of vegetation on circulation patterns, precipitation and temperature, both in terms of trends and extreme events, such as droughts and heatwaves. The main goal is to highlight the state of science and review recent studies that may help advance our collective understanding of vegetation feedbacks and the role they play in climate.

physics.ao-ph↗