climate data

Towards an Early Warning System for Climate-Sensitive Infectious Diseases: Spatio-Temporal Modeling and Deep Learning for Dengue Forecasting in Brazil

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B5 R5209

This dissertation develops integrated spatio-temporal forecasting approaches that combine deep learning, climate data, spatial dependencies, and human mobility to improve dengue prediction and support early-warning systems in Brazil.

KAUST climate research named finalist for ADIA Lab Climate Data Sciences Award

3 min read ·
KAUST team develops breakthrough method for wind data generation, earning finalist spot for ADIA Lab’s climate science award.

Statistical models for long-range dependent climate data

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B9 L2 R2322

In this talk I will discuss statistical models which incorporate temperature response to the radiative forcing components. The models can be used to estimate important climate sensitivity measures and give temperature forecasts. Bayesian inference is obtained using the methodology of integrated nested Laplace approximation and Monte Carlo simulations. The resulting approach will be demonstrated in analyzing instrumental data and Earth system model ensembles.