Słownik AI
Kompletny słownik sztucznej inteligencji
3D Convolutional Neural Network (3D-CNN)
Deep learning architecture specialized in the analysis of volumetric spatio-temporal data, applied to process climate data grids and capture complex atmospheric dynamics.
Physics-AI Hybrid Model
Approach combining traditional fluid dynamics equations with machine learning components to correct biases and parameterize sub-grid processes in climate simulations.
Climate Generative Adversarial Network (ClimateGAN)
Generative AI system trained on historical climate data to produce realistic and plausible weather scenarios, used for data augmentation and uncertainty analysis.
Bayesian Inference for Climate Parameters
Advanced statistical method using AI to estimate the probability distributions of uncertain parameters in climate models, thereby quantifying systematic uncertainties.
Spatio-Temporal Variational Autoencoder
Unsupervised AI model that learns the dominant modes of variability in climate data while generating coherent new atmospheric states for forecast ensembles.
Graph Neural Networks for Climate Meshes
AI architecture that treats climate data as graphs where nodes represent grid points and edges represent physical relationships, better preserving the topological structure of simulations.
Meta-Learning for Model Calibration
AI technique that enables a model to quickly learn to calibrate new climate model configurations by transferring knowledge acquired from previous calibrations.
AI-Augmented Ensemble Forecasting
Method that uses artificial intelligence to generate and optimize members of weather forecast ensembles, improving the coverage of the space of possibilities and probabilistic reliability.
Physics-Informed Neural Network (PINN)
Architecture that integrates the laws of conservation of physics (mass, energy, momentum) as constraints in the loss function, ensuring that predictions respect fundamental principles.
Deep Learning Modal Decomposition
AI technique that automatically extracts modes of variability (like ENSO or NAO) from raw climate data without a priori assumptions, outperforming classical methods like EOF/PCA.
Transformers for Climate Time Series
Attention-based AI model applied to multivariate meteorological data to capture long-term dependencies and complex interactions between different atmospheric variables.
Reinforcement Learning for Data Assimilation
Approach where an AI agent learns to strategically optimize the assimilation of observations into climate models, balancing accuracy and computational cost in real-time.
Diffusion Model for Extreme Event Forecasting
AI generator that produces scenarios of extreme weather events by progressively learning to denoise and reconstruct the probability distributions of rare phenomena.
Deep Learning Uncertainty Quantification
Set of AI techniques that estimate not only the mean value of forecasts but also their complete probability distribution, essential for risk-based climate decision-making.
4D U-Net for Convective Forecasting
Specialized architecture that processes 4D radar data (3 spatial + 1 temporal) for storm nowcasting, capturing the rapid evolution of high-resolution convective systems.
AI-Coupled Ocean-Atmosphere Model
Artificial intelligence system that learns the non-linear interactions between oceans and atmosphere, improving the simulation of coupled phenomena like El Niño or monsoons.