The primary objective of the project is to educate and to familiarize graduate students (MSc and PhD students) with the basic fundamental concepts, as well as in-depth topics, of the data assimilation paradigm and its applications.

The secondary objective is to setup the arena for discussions and debates around the big data challenge present in almost all the practical applications of these methodologies: hydrology, atmospheric sciences, oceanography and geosciences.

The mixture between the fundamental concepts/theoretical background presented in the first week of the event and their implementation in different fields of applications in the second week, is a unique opportunity to grasp the full understanding of the data assimilation world and its challenges.

The objective in data assimilation is generally to find the state of some system.

To find the state one can use a model, but the modelled estimate is subject to uncertainties from simplifications and weak assumptions. The model input uncertainties e.g. imperfect forcing data and uncertain model parameters are also an important source of uncertainties.

One could also just observe the state using either ground-based observations or remote-sensing. Remote-sensing offers many advantages.

Data assimilation is a way to combine models and observations in an optimal way to obtain an estimate of the state that is better than that from models or observations alone. The optimal estimate should be closer to the truth than either the observations or the model.

The huge dimension of the numerical models of the climate system, the vast amount of Earth observational data at our disposal, and the pressure to deliver timely accurate forecasts, have motivated an extraordinary research activity that has led to enormous advances which have subsequently spread out to other domains of science.

At the same time, geophysical DA is an exemplar of a Big Data problem: models have O(10⁹) and the observational datasets O(10⁸). Computationally efficient state estimation and uncertainty quantification must be carried out using massive datasets and huge dynamical models.

7 th Summer School on Data Assimilation and its applications Oceanography, Atmospheric Sciences, Risk & Safety and Reservoir Engineering

Our purpose is to get together experts in the field of data assimilation from different schools (statistics, decision analysis, system and control, pure mathematics, engineering, etc.) and to make use of their knowledge by:

• educating graduate students, young and senior researchers;
• transferring knowledge from the best lecturers to the students;
• exposing the Romanian students, academics and researchers to the most recent theoretical/algorithmic approaches and their applications;
• having extensive discussions and exchanging ideas;
• working hands-on with academic and commercial dedicated software.

This summer school targets primarily students and researchers at an early stage of their career with/without previous experience in data assimilation.

Location: The University of Agricultural Sciences and Veterinary Medicine Cluj-Napoca, Calea Mănăștur 3-5, Cluj Napoca, Romania

Period: July 16th – July 28th 2023