1. Stratospheric Network for the Assessment of Predictability (SNAP)
Department of Meteorology
Stratospheric Network for the Assessment of Predictability (SNAP)
Andrew Charlton-Perez and Amy Butler
on behalf of SNAP SSG

2. SNAP AIMS
Understanding how we can make use of coupling between the stratosphere and troposphere to improve prediction capability on sub-seasonal to seasonal timescales.
How well do monthly forecasting systems predict long-lived stratospheric anomalies and their tropospheric impact?
Why do some prediction systems fail to capture the amplitude of stratosphere-troposphere coupling?
Can we develop a test set of experiments and diagnostics to assess the role of different processes in stratosphere-troposphere coupling in models?

3. PAST WOrk
Our previous set of projects focused on coordinated forecasts of SSW events and their analysis
Tripathi et al. (2016) Examining the predictability of the Stratospheric Sudden Warming of January 2013 using multiple NWP systems. Mon. Wea. Rev.
Tripathi et al. (2015) Review: the predictability of the extra-tropical stratosphere on monthly timescales and its impact on the skill of tropospheric forecasts. QJRMS
Tripathi et al. (2015) Enhanced long-range forecast skill in boreal winter following stratospheric strong vortex conditions. Env. Res. Lett.
Our current focus is towards the sub-seasonal timescale, working with S2S model forecasts, and on coupling between the stratosphere and troposphere

4. Steering Group
Name
Institution
Mark Baldwin
Univ. Exeter, UK
Daniela Domeisen
ETH, Switzerland
Chaim Garfinkel
Herbrew Univ., Israel
Peter Hitchcock
NCAR, USA
Erik Kolstad
Bjerknes Centre, Norway
Jeff Knight
Met Office, UK
Craig Long
NOAA CPC, USA
Andrea Lopez Lang
Univ. at Albany, USA
Andrew Marshall
BoM, Australia
Isla Simpson
Aditi Sheshadri
Columbia Univ., USA
Seok-Woo Son
Seoul National Univ., Korea
Lantao Sun
NOAA ESRL, USA
Masakazu Taguchi
Aichi Univ. of Education, Japan
Om Tripathi
LA Valley College, USA
Amy Butler has been working as co-chair of SNAP since December 2016
We also refreshed the membership of the steering group to reflect the shift in emphasis towards the sub-seasonal timescale
Ad-hoc committee meeting at AMS Middle Atmosphere in Portland

5. Book Chapter
We were approached to contribute a chapter to a forthcoming book on sub-seasonal predictability led by the S2S community (Book Editors: Frederic Vitart and Andrew Robertson).
We used this opportunity as a way to discuss and review current knowledge of stratosphere-troposphere coupling and predictability.
Chapter authors: Amy Butler, Andrew Charlton-Perez, Daniela Domeisen, Chaim Garfinkel, Edwin Gerber, Peter Hitchcock, Alexey Karpechko, Amanda Maycock, Michael Sigmond, Isla Simpson and Seok-Woo Son

6. Material Covered
Stratosphere-Troposphere coupling in the Extra-Tropics
Predictability related to stratosphere-troposphere coupling
Stratosphere-troposphere coupling in the Tropics
Future Outlook

7. Topics from Outlook
What determines how well a model represents stratosphere-troposphere coupling?
Model lid height and vertical resolution
Stratospheric and Tropospheric state and biases
Different drivers and stratosphere-troposphere coupling efficiency
How can we use sub-seasonal prediction data in new ways to study stratospheric dynamics and stratosphere-troposphere coupling?
Growth of model errors in the troposphere and stratosphere
Separating competing drivers of stratospheric variability and coupling
Probabilistic understanding of stratospheric variability

8. SNAP paper analysing S2S
Lead: Daniela Domeisen (discussed at AMS meeting in Portland)
Currently soliciting sub-projects and constructing working teams (deadline Oct 15th)
Three broad areas of interest covering upward and downward coupling
Next slides show teams and areas of interest (and afterwards) some preliminary results
Lots of interesting ideas – need to work hard to turn this into coordinated understanding and analysis

9. Team
Topic
Seok-Woo Son & Hera Kim
MJO/QBO coupling
Jeff Knight
Signal to noise problem as forecasts progress (e.g. Eade, 2014)
Etienne Dunn-Sigouin
Predictability of wave reflection events
Chaim Garfinkel & Chen Schwartz
MJO tropospheric and stratospheric pathway / SSW impact on NH 
Masakazu Taguchi & Jason Furtado
Prediction of SSW events
Andrea Lang
Early winter tendencies for weak/strong vortex events
Alexey Karpechko & Andrew Charlton-Perez
Basic analysis of coupling
Peter Hitchcock & Isla Simpson
Surface impacts of PJO vs. SSW events
Mark Baldwin & Blanca Ayarzagüena
Mechanistic understanding of coupling
Amy Butler
Surface impacts of final warming events
Eun-Pa Lim & Andrew Marshall
Surface impacts on SAM
Erik Kolstad
Surface impacts on polar lows

10. WEAK Vortex composites
All ten models in the database which have been studied are able to capture coupling
Composite of ensemble mean forecasts from ECMWF model for weak vortex events and corresponding ERA-Interim composite and difference
ACP, Alexexy Karpechko, Isla Simpson & Peter Hitchcock

11. Forecast Skill
Can assess the skill of all model hindcasts for sub-sets where the vortex was weak, strong or in a neutral state (c.f. Tripathi et al., 2015)
Example shown for CMA model
Skill is Anomaly Correlation Coefficient of NAM Index

12. Comparing Models
All models show enhancement of skill for weak and strong stratospheric states
Differences both in lower stratosphere and troposphere in absolute skill and gain relative to neutral cases

13. Forecasting SSWs DEC 1998 DEC 2001 JAN 2001 JAN 2013 SEP 2002
LT= -15 d
LT= -10 d (including spread)
LT= -5 d
BoM
CMA
ECMWF
NCEP
MEAN
DEC
1998
DEC
2001
JAN
2001
JAN
2013
SEP
2002
Masakazu Taguchi

14. Forecasting SSWs II
How does model configuration effect early season weak vortex events (example in red)
Large ensemble spread and inter-model differences
Andrea Lopez Lang

15. Summary
Two major achievements in last year (refresh of steering group and mission and review chapter)
Focus for next year on analysis of S2S data with new energy from Daniela – leading to at least one paper
Already discussed this week:
Firming up links with S2S
Thinking beyond analysis of S2S data – are there experiments we can propose to target improving coupling in models
Model initialisation in the stratosphere – links to S-RIP/DAWGß?

16. REAL-Time Diagnostics
At previous meetings I promised to produce diagnostic tools which could be used to provide information from S2S model real-time forecasts
Example figure shown here
Technical challenge is too large (particularly due to infrastructure issues)
Will work with colleagues at ECMWF to implement on ECMWF servers during my time on sabbatical