# Presentation: 714587

## Identifying Current and Future Shortfalls in
 Data Assimilation Education

-  Data Assimilation Education

- Data Assimilation Education ForumPart I: Overview of Data Assimilation

*January 21, 2008*
*presented by**Michele Rienecker**Head**NASA/GSFC/Global Modeling and Assimilation Office*

## Thank you to.....

- Eugenia Kalnay, UMD
- Robert Miller, OSU
- Carl Wunsch, MIT
- Keith Haines, U. Reading
- Nancy Nichols, U. Reading

## The Problem

- Lack of qualified personnel in data assimilation (state estimation, inverse methods) for large systems
- Lack of qualified personnel with interest and experience in radiative transfer
-  Lack of programming and computing skills for high end computers

## University Experience - 1

- UMD: a very successful data assimilation educational program -        	Chaos-Weather Project
- Interdisciplinary: Mathematicians, Physicists, Atmospheric and Oceanic scientists
- Started in 2001, 12 completed PhD’s, about 10 underway.
- 2 graduate courses on data assimilation taught on a regular basis
- Introductory course attracts some of the best students in Atmospheric Sciences and Applied Math (~ 8-10 students/ year)
- ~1/2 take the follow-on, advanced course
- It has been essential to develop a collection of simple models and methods for data assimilation that the students can learn from and work with.
- Developing the infrastructure (including computational resources) for development and testing of methods is essential.
- Need access to an operational system
- 2007 JCSDA summer workshop - ~ 60 applicants - lecturers from UMD, JCSDA, NCEP, GMAO, NRL, and other universities.

## University Experience - 2

- OSU: Andrew Bennett’s summer school is now taught as a regular course, but # of eligible students is pretty small
- Problem: understanding DA requires a level of mathematical sophistication that most students simply do not have
- Solution: incorporate more mathematical concepts into graduate courses from the beginning, including problems of high complexity
- Problem:  students are averse to this!

## University Experience - 3

- MIT: “Inference from Data and Models” - ~50% of students not meteorology or oceanography
- Much of what is now done in NWP centers and in large university projects involves the _application_ of these ideas to real systems to get real results --- a numerical engineering problem
- A growing interest in the methods by the Engineering Schools
- Headed toward a situation in which data assimilation-like methods will become part of the standard engineering curriculum
- The students involved have to learn enough about _how_ we do things to make it work, but none of them seems interested in extending the methods
*A growing tendency to run big numerical models as black boxes, and to download vast data sets from the web that they then regard as 'truth'. **We need to produce a new generation that is adept in both using models and data, has a realistic sense of what both are good for, and retains a healthy skepticism about what was assumed and done.*

## U. Reading Experience

- Data assimilation program promoted through Mathematics and Meteorology Depts
- Strong offering of PhD projects - PhD students are supported by grants from the UK Research Councils.
- NERC and EPSRC PhD CASE awards - in co-operation with industry and scientific research agencies. The project is agreed between the university and the industry and must be scientifically competitive to win the award. The project has both industrial and academic supervisors and the student is funded to spend some part of the time getting work experience in the industry. This is attractive to good students and is an excellent way to gain interest in the subject!
- At least 16 students funded by this arrangement working on topics in data assimilation with support from the Met Office.

- NERC: Natural Environment Research Council
- EPSRC: Engineering and Physical Sciences Research Council
- CASE: Co-operative Awards in Science and Engineering [Cooperating organization provides at least 1/3 of required funding]

## U. Reading Experience (ctd)

- NERC funded Centre of Excellence in Data Assimilation (DARC) - a distributed research centre specifically in Data Assimilation with the Directorate centred at Reading:
- ・University of Reading
- ・University of Oxford
- ・University of Cambridge
- ・Rutherford Appleton Laboratory
- ・University of Leeds, and
- ・Edinburgh University.
- DARC Post-Doctoral Fellows at Reading help to supervise PhD students, and also collaborate with the MetOffice and ECMWF on projects on data assimilation.
- This creates a critical mass doing research in the area - provides a strong research environment for students.

## U. Reading Experience (ctd)

- One of the DARC’s main objectives is to provide training in data assimilation - lecturing and tutorial teaching in summer schools, NATO ASI meetings, and and similar training courses, by giving seminars at other institutions, .....
- A web-page dedicated to providing tutorial examples and computer programs that can be used freely by other groups for training. This is a popular website and is used internationally. We get around 100 hits per month on this site.
- _[http:](http://darc.nerc.ac.uk/)__[//darc](http://darc.nerc.ac.uk/)__[.](http://darc.nerc.ac.uk/)__[nerc](http://darc.nerc.ac.uk/)__[.ac.](http://darc.nerc.ac.uk/)__[uk](http://darc.nerc.ac.uk/)_

## What we need

- Ph.D. level scientists with good grounding in data assimilation methods and experience with large models and/or expertise relevant to satellite data (radiative transfer)
- Scientists who can advance our science, not just apply existing systems as a black box to a science problem
- Scientists who have some experience with (or exposure to) large, complex systems and models and don’t require extensive OJT
- Scientists who can program in modern Fortran on high end computing architectures

## Summary

**Challenge:** Need to entice students to be interested in assimilation development, not just in using an existing system
**Challenge:**  Need to excite students to work in this discipline after graduation
- Need to have a partnership with Math depts to ensure that students have a strong background in stats, analysis, and PDEs
- Need to reach out to engineering schools - appropriate curriculum and new graduates
- Universities consortium ? - going it alone is not as effective
- Summer schools not effective by themselves

## Summary (ctd)

- Need to involve “industry” in partnership with the university - don’t just leave it to the university - requires an investment from the operational centers
- Partnership with operational centers is best way to provide experience with systems of relevant complexity and with relevant computational infrastructure