Data Driven Nowcasting

Data-driven nowcasting focuses on generating short-term predictions of various dynamic systems using real-time data, aiming to improve timeliness and accuracy compared to traditional forecasting methods. Current research emphasizes the application of deep learning architectures, such as convolutional neural networks (CNNs), recurrent neural networks (RNNs, including LSTMs and GRUs), transformers, and generative adversarial networks (GANs), often incorporating physics-based constraints for enhanced performance and interpretability. These techniques are being applied across diverse domains, including economics (GDP growth), meteorology (precipitation, temperature, fog, and severe weather), seismology (earthquakes), and environmental monitoring (greenhouse gas emissions, wildfire spread). The improved accuracy and speed of nowcasting offer significant benefits for decision-making in various sectors, from financial markets and disaster response to energy management and sustainable development.