Dashboard, Communication and Training activities

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In Situ Thematic Assembly Centre for

Marine Service

C. Troupin, P. Rotllan, I. Serra, B. Frontera

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One-word summary. . .

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Conclusions

I More regular contacts with Mercator-Oc´ean communication

I _{Better identification of use cases and applications} I More presence in international conferences

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Conclusions

I Better identification of use cases and applications

I More presence in international conferences

I Use all the modern weapons in term of communication

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Conclusions

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Conclusions

M D ¬ Ö 

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Conclusions

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INSTAC

Dashboard

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The plan

1. Make in situ data visible and discoverable

2. Avoid duplication of work with regional portals and EMODnet

3. Build solution using open software and make the code open

4. Work on the index files

5. Include time series of KPIs

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The plan

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The plan

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The plan

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The plan

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The plan

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Feedback from RUTW – NWS

I Format I _Content I Suggestions I Acknowledgements: Susanne (BSH) 4 × 30-min. block Total 15 attendees (!)

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Feedback from RUTW – NWS

I Format

I Content

I Suggestions

I Acknowledgements: Susanne (BSH)

1. Intro INSTAC portal

2. Northwest Shelf part

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Feedback from RUTW – NWS

I Format

I Content

I Suggestions

I Acknowledgements: Susanne (BSH)

1. Users from companies: not familiar with netCDF → Need for a brief/common introduction

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Feedback from RUTW – NWS

I Format

I Content

I Suggestions

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Feedback from RUTW – Global

I Overall positive feedback

I _{Contact with SME} I Usefulness of in situ data

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Feedback from RUTW – Global

I Contact with SME

I Usefulness of in situ data

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Feedback from RUTW – Global

I Contact with SME

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Feedback from RUTW – Global

I Contact with SME

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T-S diagram & water masses

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T-S diagram & water masses

Goal: show how T-S diagrams can be related to water masses Motivation: T-S diagrams not often considered in CMEMS

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T-S diagram & water masses

Bonus: the Python code to re-do the plots

m https://github.com/ctroupin/CMEMS INSTAC Training/blob/master/ PythonNotebooks/CommunicationMaterial/plot TS diagram all.py

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Mixed layer depth: seasonal cycle

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Mixed layer depth: seasonal cycle

Goal: show a simple but relevant diagnostic computed from profiles Motivation: implication of MLD in primary production and climate change

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Mixed layer depth: seasonal cycle

Bonus: the Python code to re-do the plots

m https://github.com/ctroupin/CMEMS INSTAC Training/blob/master/ PythonNotebooks/CommunicationMaterial/plot MLD profilers.ipynb

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Mixed layer depth: seasonal cycle

Acknowledgements: Sylvie, Tanguy & J´erˆome (Ifremer)

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Use case

Goals: present application from downstream user demonstrate the added-value of CMEMS

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Use case

Proposed use case: ”Monitoring the oxygen conditions in the deep basins of the Baltic Sea” using data products from SMHI

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Use case

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Use case

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Use case

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Use case

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Oxygen conditions in the Baltic

I Downstream users: HELCOM, EEA

I Mercator response: happy of the users but . . . not clear what is the CMEMS added values. . .

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Oxygen conditions in the Baltic

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Oxygen conditions in the Baltic

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IMDIS 2016 conference

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IMDIS 2016 conference

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What’s next?

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What’s next?

I _{Better identification of use cases and applications}

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What’s next?

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What’s next?

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