Attend a comprehensive two-week workshop exploring the mathematical foundations and practical applications of data assimilation in weather and climate modeling. Master fundamental concepts including Bayesian theory, probability, and linear algebra before progressing to advanced data assimilation methods such as Optimal Interpolation, 3D-Var, 4D-Var, Kalman filters, and Ensemble Kalman Filter techniques. Engage in hands-on sessions applying particle filters, satellite radiance assimilation, and machine learning approaches to real-world atmospheric and oceanic models. Learn from leading experts in the field as they demonstrate uncertainty estimation techniques, ensemble data assimilation methods, and the integration of satellite and remote sensing observations into operational weather forecasting systems. Explore diverse applications across land surface studies, oceanography, atmospheric composition analysis, and reanalysis efforts while gaining practical experience with numerical weather prediction methods. Discover how data assimilation serves as a crucial tool for estimating accurate atmospheric states by combining observations with short-range forecasts, and understand its role in climate monitoring and variability assessment through historical data reanalysis. Participate in specialized sessions covering rainfall extremes modeling, flood prediction, operational forecast systems used by organizations like NCMRWF and NARL, and the latest developments in dynamical systems applications to Earth sciences.

Syllabus

Data Assimilation and Dynamical Systems in Earth Sciences (Lecture 3) & Hands on by Amit Apte
Application of Particle Filters on Data Assimilation by Prashant Kumar
Modelling Rainfall Extremes and Floods Over Karnataka....... by Rakesh V and Rekha Bharali Gogoi
Data Assimilation experiments for Operational Weather Forecast in NARL by Amit Kesarkar
Bayesian Theory, Cost Function, Minimization, Incremental Optimization, Observati... by Vinu Valsala
Hands on Data Assimilation on Real World Model by Jyoti Bhate
Ocean Data Assimilation (Lecture 2) by Arya Paul
Ocean Data Assimilation by Arya Paul
Hands on Satellite Radiance Assimilation by Indira Rani
Overview of Data Assimilation in Operational Forecast of NCMRWF by Indira Rani
Satellite Data Assimilation by Fiona Smith
Data Assimilation and Dynamical Systems in Earth Sciences (Lecture 2) by Amit Apte
Data assimilation and Dynamical systems in Earth sciences by Amit Apte
Data Assimilation – Variational methods (lecture 8) by Varahan
Data Assimilation – Kalman Filter (Lecture 7) by Varahan
Data Assimilation – High Dimensional Systems by Varahan
Data Assimilation – Basic Theory by Varahan
Finite Dimentional Vectore Spaces: An Overview by S. Lakshmivarahan
Bayesian Inference (Lecture 2) by Vineeth BS
Introduction to Probability and Bayesian Inference (Lecture 1) by Vineeth BS
Modelling Rainfall Extremes and Floods Over Karnataka by Rakesh V
Application of Particle Filters on Data Assimilation by Prashant Kumar
Machine Learning Approach for Data Assimilation by Deepak Subramani
Data Assimilation For Reanalysis by Indira Rani
Numerical Weather Prediction – Methods by A. Chandrasekar
Data assimilation and Dynamical systems in Earth sciences (Lecture 2) by Amit Apte
Data Assimilation – Basic Theory (lecture 2) by Varahan
Data Assimilation – Basic Theory (lecture 1) by Varahan
Linear Algebra - II by Varahan
Linear Algebra - II by Varahan
Linear Algebra - I by Varahan
Bayesian Models by Vineeth BS
Probability Theory by Vineeth BS