AM-IA brings together news, research papers and applications relating to artificial intelligence published around the world into a single feed — and makes them available in your European language at the click of a button.
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Oracle’s AI campus in New Mexico is set to see its permitted annual greenhouse gas emissions reduced from 8.8 to 5.7 million tonnes; opponents are demanding to see the calculation models.
The RemoTAP-Hybrid algorithm, applied to one year’s worth of data from the SPEXone polarimeter, produces the first global seasonal maps of aerosol composition as observed by this instrument.
Researchers from Niklas Boers’ team reduce both the spatial and temporal scales of temperature and precipitation without having to retrain the model for each task.
Out of 2,099 catchment areas tested between 2021 and 2023, this open probabilistic model provides more accurate predictions of floods with return periods of 1.5 and 2 years than the AIFL method on which it is based.
A study covering all the counties in the contiguous United States quantifies the losses incurred by planning three to five days in advance, and shows that early and comprehensive deployment remains more effective than late reinforcement.
Compared with IFS-ENS, GenCast, FourCastNet 3 and FGN are more accurate at locating cyclones; FGN performs better for 90 per cent of the storms in 2025. Intensity and dispersion remain their weak points.
Chinese researchers have shown that an emulator trained on a 0.1° grid preserves the energy and fronts of ocean eddies, where downscaling a model to 1° fails.
Each branch of the PANN network attributes a portion of the error to a distinct physical process, thereby revealing biases that cancel each other out and remain undetectable using conventional diagnostic methods.
By combining seasonal forecasts from the CEPMMT, vegetation models and machine-learning-based predictions of burnt areas, researchers are able to identify areas at risk before the event occurs.
Trained on a global model that simulates thunderstorms, the parameterisation has been running for over ten years without drifting and reduces biases in both light and extreme rainfall.
Published in *Nature*, the method developed by MIT, Microsoft and their partners corrects systematic errors in the models and, for the first time, outperforms climatological averages.
Trained on six major climate simulation datasets, a convolutional neural network identifies states of the Arctic Ocean that are conducive to a sudden loss of sea ice within a few years.
Rather than perturbing a single forecast, OceanFlow directly learns the distribution of future ocean states around a stable mean trajectory.
Trained on 24 years of satellite data, a ConvLSTM model outperforms simple persistence by up to four years and predicts that soil degradation will continue between now and 2030.
AI models learn from past observations: where no measurements are taken, they make educated guesses. Down To Earth advocates funding instruments for poorer countries rather than providing them with free forecasts.
The polynomial approximation of the UTCI index is inaccurate under extreme conditions; its neural network version reduces the error by 87 per cent and increases the number of days of very high heat stress from 15 to 35.
Trained on the Italian Civil Protection’s radar composite, this probabilistic system for short-term precipitation forecasting outperforms the two industry benchmarks on the CRPS score across all time horizons.
Met Éireann, University College Dublin and the AIMSIR centre will combine Google Earth AI, satellite imagery and a network of solar stations installed in schools across the 26 counties.
The AGOP method outperforms conventional explanatory techniques on three out of four criteria and can even trigger or halt a simulated El Niño ten months later.
Researchers have shown that the way the problem is framed matters just as much as the model: predicting root moisture in physical units, using residual learning, makes all the difference.