1. The WWRP-THORPEX IPY Cluster Coordinator: Thor-Erik Nordeng Norwegian Meteorological Institute (met.no), Oslo, Norway (Fronted by David Burridge)

2. Meteorologisk Institutt met.no The IPY-THORPEX Cluster 10 individual projects (see WMO Bulletin Oct. 2007) The objectives of the IPY-THORPEX Cluster are: –Achieve a better understanding of small scale weather phenomena –To improve the understanding of physical/dynamical processes in polar regions –Explore use of satellite data and optimised observations to improve high impact weather forecasts –Utilise improved forecasts to the benefit of society, the economy and the environment

3. Meteorologisk Institutt met.no The WWRP- THORPEX IPY cluster WWRP-THORPEX IPY Cluster (T.E. Nordeng, coordinator) GFDex Greenland Flow Distortion experiment (I. Renfrew, U. East Anglia) STAR Storm Studies of the Arctic (J. Hanesiak, U Manitoba) Concordiasi Use of IASI data (F. Rabier, Meteo-France) Norwegian IPY-THORPEX (J.E. Kristjansson, U Oslo) Greenland Jets (A. Dombrack, DLR) GREENEX (H. Olafsson, Iceland & DLR) ARCMIP Arctic Regional Climate Model Intercomparison Project (K. Detholf, Alfred-Wegener Institute) Impacts of surfaces fluxes on severe Arctic storms, climate change and coastal orographic processes (W. Perrie, BIO Canada)) T-PARC THORPEX Pacific Asian Regional Campaign (D. Parsons, NCAR) TAWEPI Thorpex Arctic Weather and Environmental and Environmental Prediction Initiative (Ayrton Zadra, Environment Canada)

4. Meteorologisk Institutt met.no Polar lows

5. Meteorologisk Institutt met.no Topogographically induced jets ( light grey is strong wind)

6. Meteorologisk Institutt met.no Lee waves under capping inversion ( strong downslope wind and turbulence)

7. Meteorologisk Institutt met.no Channeling (Sandvik and Furevik, 2002)

8. Meteorologisk Institutt met.no Challenges – initial conditions Model improvements Use of satellites difficult Few traditional observations RMS error of mslp forecasts with the Norwegian limited area model system (HIRLAM) over a two year period; the Barents Sea in red and the North Sea in blue.

9. Meteorologisk Institutt met.no How to improve NWP (in Polar regions) Better understanding of physical processes  improve the models Use more observations probability forecasts

10. Meteorologisk Institutt met.no

11. Targeting Strategy: compute sensitivity area before the actual forecast starts go there (by plane) drop sondes

12. Meteorologisk Institutt met.no Examples from the WWRP-THORPEX IPY cluster Norwegian IPY-THORPEX (J.E. Kristjansson, U of Oslo) TAWEPI Thorpex Arctic Weather and Environmental and Environmental Prediction Initiative (Ayrton Zadra, Environment Canada) Concordiasi (F. Rabier, Meteo-France) GFDex Greenland Flow Distortion experiment (I. Renfrew, U. East Anglia)

13. Meteorologisk Institutt met.no 13 The targeted sondes improve the forecast of the polar low at landfall CONTROL forecastTARGETED forecast Verification: ECMWF analysis

14. Meteorologisk Institutt met.no 5 cases: –24 February –26 February –01 March –03 March (NULL) –10 March 5 -11 targeted dropsondes per flight Data transmitted to GTS in real-time and assimilated into Met Office operational forecast (The flight on 1 st March is the green track on the diagram.) Targeting During GFDex (Emma Irvine, Suzanne Gray and John Methven (University of Reading) + David Walters (Met Office))

15. Meteorologisk Institutt met.no Four targeted observing flights were conducted during GFDex, around southern Greenland and Iceland Targeted sonde data was used by the data assimilation system to modify the background state and influence the forecast via analysis increments The forecast improvement is small compared to the forecast error for the same period; targeted observations have both improved and degraded the forecast The 1 st March case showed that modification of the upper-level PV anomaly by the inclusion of targeted sonde data led to forecast improvement propagating into the Scandinavian verification region with a developing polar low

16. Meteorologisk Institutt met.no Planned targeting experiment (Concordiasi) 1)Determination of sensitive area 1) Depending on the track and/or swath of IASI and AIRS sensors 2) Also depending on the predicted sensitive area at 18hUTC. (Ex with VORCORE data) 2) Targeting of sondes in these area Predicted sensitive area valid on the 2007/10/07 at 18Z, initialized at 00Z and optimized for the 2007/10/09 at 00Z. Balloon trajectories start on the 2007/10/07 at 00Z and reach sensitive areas at 18Z. The blue shading shows mean wind speed at 50 hPa on that period (ECMWF operational forecast). The navy dashed curve shows the limits of sea ice as in ECMWF system. Track of IASI the 7th October 2007. The colour gives the hour of the passage.

17. Meteorologisk Institutt met.no Ensemble prediction Estimate the forecasted pdf (probability density function) rather than single deterministic approach Assumption: # of perturbed forecasts large enough to cover the whole ”true” pdf. method run a number of integrations from a number of (optimally) perturbed initial states combine results from a number of models  Use spread as a measure of uncertainty

18. Meteorologisk Institutt met.no Downscaling LAMEPS with high resolution model (UM – 4 km) Flight 3: 4 March 10.15-13.30 UTC (Silje Sørsdal (Master thesis, UiO, Norwegian IPY-THORPEX)

19. Meteorologisk Institutt met.no Comparing with observation data from flight 3(flight time 10.15-13.30). Black contours are std.dev of MSLP. LAMEPS UM-EPS Probability of wind at 925hPa>25m/s T+42h (12UTC 04.03)

20. Meteorologisk Institutt met.no Collaboration: Status of extended regional model at CMC* Polar extension of CMC’s regional NWP model global, rotated, variable-resolution lat-lon grid core: 15-km resolution 58 hybrid vertical levels, top 10 hPa timestep: 7.5 min Implementation plans 4 runs (48-h forecasts) per day to replace current operational regional model probable implementation in the winter of 2009/2010 _____________________________________________________________________________ * Project partly funded by IPY-LIEP. Grid parameters kindly provided by A. Patoine (CMC). Fig.: Grid of CMC’s next regional model (Note: Only every 5 grid-point is shown)

21. Meteorologisk Institutt met.no TAWEPI subproject 1: Coupling snow and ice Y.-C. Chung, S. Bélair, J. Mailhot Goal To investigate snow and sea ice evolution in the Arctic Ocean by a coupled snow/sea ice system Methods Sequentially couple models: 1-D, multi-layer, offline sea ice model in Meteorological Service of Canada (MSC) operational forecasting system 1-D, multi-layer snow model SNTHERM (Jordan, 1991) 1-D, blowing snow model, PIEKTUK (Déry, 2001) Surface Heat Budget of the Arctic Ocean (SHEBA) Datasets Multi-year ice floe, drifted more than 1400 km in the Beaufort and Chukchi Seas Measurements for one year from October 31, 1997 SHEBA Coupling flowchart

22. Meteorologisk Institutt met.no - Sensitivity analysis of snow depth  Wind effect and error related to new snow density should be considered in winter  During ablation period, uncertainties in albedo affect stored energy & grain size, retarding or accelerating spring snow melt - Temporal evolution  The model predicts snow depth well after considering erosion due to blowing snow  The model system captures accurately the start of snow melt (5/29) and intensive snow melts until snow depletion (6/24 ~7/12)  The model predicts the ice thickness very well before snow depletion. The underestimation after snow depletion is caused mainly by the error of the ice model - Vertical structure  Temperature, grain, density, thermal conductivity, etc. temporal evolution of snow depth temporal evolution of ice thickness vertical structure of snow temperature

23. Meteorologisk Institutt met.no TAWEPI (Canada) Subproject 1: Coupling snow and ice Subproject 2: Polar-GEM clouds Subproject 3: Sea-ice modelling Subproject 4: Sensitivity studies in the Arctic using singular vectors Subproject 5: Hyperspectral IR assimilation in the Arctic Subproject 6: GEM IPY Analyses

24. Meteorologisk Institutt met.no Outcome of the THORPEX IPY cluster Data for improving physical parameterization in NWP models, -clouds, microphysics, surf fluxes Improved assimilation techniques for high latitudes with emphasis on satellites data Increased understanding on the effect of the use of ensemble simulations for high latitudes Increased understanding on the effect of targeting in high latitudes Increased understanding of dynamics of high latitude, particularly high impact weather phenomena Demonstration of the effect of new instruments Demonstration of the effect of increased Arctic and Antarctic observations for local and extratropical NWP forecasting.

25. Meteorologisk Institutt met.no IPY legacy We call for an immediate, high-level and sustained focus on polar prediction services, stimulated, led and coordinated by WMO, as the best way to integrate and synthesize the IPY observational efforts and to communication and maximise the impact of IPY science (David Carlson, IPY IPO, July 2009 - in preparation)

26. Meteorologisk Institutt met.no Thank you for your attention with special thanks to scientists of the THORPEX IPY cluster projects and others who contributed to this summary