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The WWRP-THORPEX IPY Cluster Coordinator: Thor-Erik Nordeng

The WWRP-THORPEX IPY Cluster Coordinator: Thor-Erik Nordeng Norwegian Meteorological Institute (met.no), Oslo, Norway ( Fronted by David Burridge ). The IPY-THORPEX Cluster 10 individual projects (see WMO Bulletin Oct. 2007). The objectives of the IPY-THORPEX Cluster are:

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The WWRP-THORPEX IPY Cluster Coordinator: Thor-Erik Nordeng

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

  2. The IPY-THORPEX Cluster10 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. The WWRP-THORPEX IPY cluster WWRP-THORPEX IPY Cluster (T.E. Nordeng, coordinator) ARCMIP Arctic Regional Climate Model Intercomparison Project (K. Detholf, Alfred-Wegener Institute) STAR Storm Studies of the Arctic (J. Hanesiak, U Manitoba) TAWEPI Thorpex Arctic Weather and Environmental Prediction Initiative (AyrtonZadra, Environment Canada) Norwegian IPY-THORPEX (J.E. Kristjansson, U Oslo) GFDex Greenland Flow Distortion experiment (I. Renfrew, U. East Anglia) GREENEX (H. Olafsson, Iceland & DLR) 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) Concordiasi Use of IASI data (F. Rabier, Meteo-France) Greenland Jets (A. Dombrack, DLR)

  4. Polar lows

  5. Topogographically induced jets(light grey is strong wind)

  6. Lee waves under capping inversion(strong downslope wind and turbulence)

  7. Channeling (Sandvik and Furevik, 2002)

  8. Challenges – initial conditions Model improvementsUse of satellites difficultFew 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. How to improve NWP (in Polar regions) • Better understanding of physical processes  improve the models • Use more observations • probability forecasts

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

  11. Norwegian IPY-THORPEX (J.E. Kristjansson, U of Oslo) GFDex Greenland Flow Distortion experiment (I. Renfrew, U. East Anglia) Examples from the WWRP-THORPEX IPY cluster TAWEPI Thorpex Arctic Weather and Environmental Prediction Initiative (Ayrton Zadra, Environment Canada) Concordiasi (F. Rabier, Meteo-France)

  12. The targeted sondes improve the forecast of the polar low at landfall CONTROL forecast TARGETED forecast Verification: ECMWF analysis 13

  13. Targeting During GFDex(Emma Irvine, Suzanne Gray and John Methven (University of Reading) + David Walters (Met Office)) • 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 1st March is the green track on the diagram.)

  14. 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 1st 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

  15. Planned targeting experiment (Concordiasi) • Determination of sensitive area • Depending on the track and/or swath of IASI and AIRS sensors • Also depending on the predicted sensitive area at 18hUTC. (Ex with VORCORE data) • Targeting of sondes in these area Track of IASI the 7th October 2007. The colour gives the hour of the passage. 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.

  16. 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

  17. 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)

  18. Probability of wind at 925hPa>25m/s T+42h (12UTC 04.03) LAMEPS UM-EPS • Comparing with observation data from flight 3(flight time 10.15-13.30). • Black contours are std.dev of MSLP.

  19. 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 Fig.: Grid of CMC’s next regional model (Note: Only every 5 grid-point is shown) _____________________________________________________________________________ * Project partly funded by IPY-LIEP. Grid parameters kindly provided by A. Patoine (CMC).

  20. TAWEPI subproject 1: Coupling snow and iceY.-C. Chung, S. Bélair, J. Mailhot Coupling flowchart • 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

  21. temporal evolution of snow depth - Sensitivity analysis of snow depth • Wind effect and error related to new snowdensity 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 ice thickness vertical structure of snow temperature

  22. 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

  23. 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.

  24. 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)

  25. Thank you for your attention with special thanks to scientists of the THORPEX IPY cluster projects and others who contributed to this summary

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