Summer school Data Assimilation and its applications – big data challenge
17 – 28 July 2017

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(10^9) and the observational datasets O(10^8). Computationally efficient state estimation and uncertainty quantification must be carried out using massive datasets and huge dynamical models.

5 th Summer School on Data Assimilation and its applications Oceanography, Atmospheric Sciences, Risk & Safety and Reservoir Engineering
The goal is to get together experts in the field of data assimilation from different schools (statistics, system and control, pure mathematics, engineering, … ) and to make use of their knowledge by:
• Educating graduate students, young and senior researchers
• Knowledge transfer from the best lecturers to the students
• Exposing these new theoretical/algortihmic approaches and their applications to the Romanian academia and researchers
• Having extensive discussions and exchanging ideas
• Working hands-on with academia and commercial dedicated softwares.

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

 

Organising team:

Costin Mihnea-Ionut mihneacostin37@yahoo.com

Dan Sava

Remus Hanea rhane@statoil.com

 

1. Location and period
• Location: Lucian Blaga University, Faculty of Mathematics and Computer Science, Sibiu, Romania
• Period: 17th – 28th July 2017

2. Speakers and topics
The two weeks will cover the theory and applications

Prof. Dr. Arnold Heemink (TU Delft, The Netherlands) – An introduction in Inverse Modelling and Data Assimilation – Basic notions

Dr. Pavel Sakov (Meteorological Bureau Melbourne, Australia) – Ensemble Kalman Filter – From basics to advanced technologies and improvements

Dr. Joanna Pelc – Variational methods for Data assimilation (3D, 4D VAR, hybrids)

Dr. Andreas Stordal (IRIS, Norway) – Particle filter and the hybrid filter – Adaptive Gaussian Mixture – basics to advance techniques

Prof. Dr. Martin Verlaan (TU Delft and Deltares, The Netherlands) & Nils van Velzen (Vortech, The Netherlands) – The Open DA paradigm – theory and the toolbox (V&V)

Dr. Anca Hanea and Prof. Dr. Mark Burgman (CEBRA, Melbourne, Australia) – Risk quantification, risk management, Expert Judgment and Safety issues (H&B)

Dr. Alberto Carrassi and Dr. Laurent Bertino (NANSEN center) – Ocean and Climate applications (C&L)

Kees Lemmens (TU Delft, The Netherlands) – Scientific Programming using C and Python; Parallel Programming using MPI; and GPU Programming using Cuda.

Dr. Adriana Coman (LISA, Paris) – Atmospheric Data Assimilation – Air pollution

Dr. Chitu Alin (TNO, The Netherlands) – Ensemble based Robust Optimization and its flavors (theory and applications)

Prof. Dr. Remus Hanea (UiS and Statoil, Norway) and Torbjørn Ek (Statoil, Norway) – Ensemble based Assisted History Matching in Modern Reservoir Engineering – Introduction and in-depth approaches

Joao Encarnacao (UT Austin) – Satellite Gravimetric Data

Prof. Dr. Ben Ale (Benale Risk Management Advice) – Risk and Safety Issues

3. Cultural program
In the weekend (22-23 July 2017): trip to Sighisoara, with an over-night stay in Paltinis.

4. Participants
• graduate students
• researchers
• Academia

5. Accommodation
• Students
– Student accommodation in the University Campus
– Hotels in Sibiu
• Lecturers – The University Hotel accommodation

6. Registration fee
• Students 500 Euros*
• Researchers and Academia 850 Euros

* Romanian students can apply for scholarships.

7. Contact
If you are interested in participating to this event please send an email to Remus Hanea, rhane@statoil.com, and please attached a short description of your research and interest in this event.