IWAL

An Interactive Weather Analysis Laboratory

Michael Sprenger, Sebastian Limbach, Elmar Schömer, Heini Wernli

Institute for Atmospheric and Climate Science, ETH, Zurich, Switzerland

Institute for Computer Science, Johannes-Gutenberg University, Mainz, Germany

Working with weather data!

Print out weather maps and let the students arrange them according to time

Use weather data resources on the internet

Prepare simple Matlab/Python scripts to produce weather charts

Let the students learn how to use a professional tool, e.g. Ninjo at MeteoSwiss

Create an intuitive and simple tool that allows to work with ‘real’ meteo data

Data Sources

- ERA-Interim 1979-2014 in 6-h steps (about 7 TB)

- Selected case studies (e.g., storms Xynthia, Lothar, Sandy, Kyrill, …) with

extended lists of meteorological fields; additional events can be included..

- ECMWF operational analysis and deterministic forecast

- global IR and WV satellite imagery

WebGUI – Interface to IWAL Viewport 1: horizontal cross section

Viewport 2: vertical cross section

list of all image

layers

details of

a image

layer

decid

e w

hat

to d

o!

new

im

ag

e layer?

save s

tate

? s

ave i

mag

e?

Features I

Dynamic zoom

- similar to Google Earth

- Resolution of the image is auto-

matically adapted to plotting domain

Arbitrary number of image layers

- additional fields (e.g. relative humidity)

can be overlaid with a free degree of

transparency)

- Easy switching on/off of layers

Features II Arbitrary vertical cross sections

- end points easily set in horizontal viewport;

cross section redrawn if end points moved

- position in vertical cross section shown

in horizontal cross section

- several layers allowed (degree of transparency

adjustable)

x

x PV [PVU] and θ [K]

Features III

X

- Pseudosounding at mouse-selected position (T, dew-point T, wind profile)

- Sounding is automatically adjusted if position is changing

P and T @ LCL

CAPE

Showalter index

Features IV

- Forward and backward trajectories from

arbitrary starting positions

- Additional fields traced along trajectories and

shown in table (e.g., temperature T)

starting positions

Student Case Study – Task for the students

Student Case Study – Anna Gevecke

Student Case Study – Sylvia Gassner & Lars van Galen

Characteristics

- storm Jeannette

- 27-28 October 2002 in Northwestern Europe

- strongest storm in regard to area and intensity

- two deepening phases

- sting jet formed during passage over Britain

storm passes over

northern Europe

Genesis

Student Case Study

Students found

- storm Jeannette formed south of Newfoundland

- no closed isobars, but baroclinic zone with a little 'kink' in it (indicates unstable zone)

SLP and theta-e @ 850 hPa

1st deepening phase

SLP and wind speed @ 200 hPa

Student Case Study

Students found

- storm has developed first closed isobars

- first deepening phase takes place near left-exit of an upper-level jet streak

2nd deepening phase

SLP and wind speed @ 200 hPa

Student Case Study

Students found

- storm has reached 980 hPa in ERA-Interim data set

- at 200 hPa a remarkable straight jet streak is discernible

- second deepening takes place in exit of this jet streak

Maturity and sting jet

27-06 06Z

SLP and theta-e @ 850 hPa

Student Case Study

Students found

- storm shows distinct frontal structures

(warm and cold fronts)

- they relate it to the conceptual Shapiro-Keyser model

- they recognize that ERA-Interim cannot capture sting jet

How we used IWAL so far / experience!

Since it was developed in 2014, it was used in the Bachelor lecture ‚Wettersysteme‘

First tasks: Characterize the weather at your birthday!

Then: Choose an interesting weather event, and tell a story about it with IWAL

Now: We specifiy three cases, and students can choose one of these cases.

IWAL is not perfect – we now about bugs and deficiencies!

It is not very easy to implement further features...

The deficiencies are not severe; IWAL is useful even with them...

We do *not* use it in Bachelor and Master theses

Needed flexibility and functionality is not sufficient in IWAL

Bachelor and Master students use script languages (Python/Matlab/NCL,...)

Students are ‚nearer‘ to the data, and get full control by ‚programming‘ their figures...

Student Feedback

- Most students enjoyed working with IWAL. They quickly understood how to handle

the tool and were motivated by the interactive possibilities

- Anonymous questionnaire in spring 2012 with 25 students:

- 19 students rated the statement “I enjoyed working with IWAL” as

“fully or rather true”

- 17 responded that “working with IWAL facilitated an in-depth

meteorological analysis” was “fully or rather true.”

- Compared to the approaches we used before

…. (investigation of a static set of pre-produced figures;

teaching the students how to produce plots with standard graphics software),

…. IWAL allows the user to rapidly focus on meteorology instead of technical challenges,

Student Feedback – Felix Piringer

Technical Aspects – Innovedum Project

Limbach, S., 2013: Software tools and efficient algorithms for the feature detection, feature

tracking, event localization, and visualization of large sets of atmospheric data. PhD thesis,

University of Mainz, 256 pp.

[available online at http://ubm.opus.hbz-nrw.de/volltexte/2013/3503/pdf/doc.pdf.]

- Typical web application with client/server architecture

- server provides components for creation and serving of static and dynamic web

content (including all data and code)

- client (run in web browser) sends request for images and data to server

- advantage: no local installation needed; input data not needed on client

- disadvantage: large workload on server if multiple clients

- Images created with NCL/PyNGL and cached, on demand, for further client requests

- Web framework running on Apache web server: python-based Django

- IWAL provides a administration user interface to

- Add/delete new users and case studies

- Define which meteorological fields can be plotted

Access to IWAL

- IWAL is installed at ETH Zurich, and external users can access a demo version

online at

www.iwal.ethz.ch

Challenges when installing IWAL on other place:

i) install freely-available third party software (e.g., NCL, trajectory calculation);

ii) setup a powerful web server;

iii) link (large) database (satellite imagery, reanalysis data,..) to web server

||Data Literacy

Refresh Teching

Felix Piringer

MSc student Geomatics D-BAUG

12/02/2019 1

Data Literacy at the Example of IWAL: A Student’s View

Felix Piringer

||Data Literacy

Refresh Teching12/02/2019Felix Piringer 2

Geomatics, Geodesy, Geoinformatics, Geospatial Engineering,…

||Data Literacy

Refresh Teching

Practical application of theoretical knowledge

“Search for the weather” in the 3D-atmosphere

Getting “a feel” for the specific data

Reminder of how… …important visualization is when dealing with big amounts of data

…visualization can trick you

12/02/2019Felix Piringer 3

IWAL: Personal Experience

Source: IWAL Source: https://de.wikipedia.org/wiki/Plattkarte#/media/Datei:Plate_Carr%C3%A9e_with_Tissot's_Indicatrices_of_Distortion.svg

||Data Literacy

Refresh Teching

Downsides: Usually less capable => no problem up to a certain point

Lack of practical knowledge and skills (for work after ETH)

Sometimes neglected…

12/02/2019Felix Piringer 4

Rudimentary Teaching/Science Tools vs Commercial Software

||Data Literacy

Refresh Teching

Advantages: Close to theoretical knowledge of students

Little familiarization required

Focus on the actual data

Link to other disciplines

Possibly even better: raw data or own data acquisition (when feasible)

Crucial (my opinion): Fundamentals of statistics AND programming

12/02/2019Felix Piringer 5

Rudimentary Teaching/Science Tools vs Commercial Software

||Data Literacy

Refresh Teching

Data Acquisition: Measurement principle of particular system

Measurement setup

Modelling and corrections of systematic errors

Ability to quantify uncertainties and reliability

Data Representation: Complex data structure, large amounts of data

Geometry (reference frames, ellipsoidal geometry, etc.)

Semantics

Visualization Projections

>> 2 dimensions

Cartographical methods (generalization, colors, lines, symbology, layout,…)

12/02/2019Felix Piringer 6

Use of Geospatial Data (outside Geodesy)

||Data Literacy

Refresh Teching

Acquiring and handling geodata and is a science in itself

Many things to do wrong

Communication and fundamental knowledge Top down (required accuracy, unwanted corrections, systematic errors)

Bottom up (interpretation methods, desired results)

12/02/2019Felix Piringer 7

Use of Geospatial Data (outside Geodesy)