1 / 24

Polar Low Case Study

Polar Low Case Study. Paula Doubrawa Moreira Xiangdong Zhang Jeremy Krieger Jing Zhang. Outline. What, when, where? Intro to research and methods Preliminary results Experiment 1 Experiment 2 Experiment 3 Experiment 4 Conclusion. What?. I ntense maritime mesoscale cyclones

saxton
Download Presentation

Polar Low Case Study

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Polar Low Case Study Paula DoubrawaMoreira Xiangdong Zhang Jeremy Krieger Jing Zhang

  2. Outline • What, when, where? • Intro to research and methods • Preliminary results • Experiment 1 • Experiment 2 • Experiment 3 • Experiment 4 • Conclusion

  3. What? • Intensemaritimemesoscalecyclones • Formpolewardof a major barocliniczone • Mostlydue to strongcoldinflowsofdry/stableArcticair over warmerwatersurfaces • Scalesup to 1000km (621mi) • Near-surfacewindsexceeding 15m/s (33mph) Definitions by: - Kolstad, E. W. (2007), Extreme winds in the Nordic Seas: polar lows and Arctic fronts in a changing climate, University of Bergen, Bergen. - European Geophysical Society’s Polar Low Working Group, Rasmussen, E. A. T., J. (2003), Polar Lows: Mesoscale Weather Systems in the Polar Regions, 624 pp., Cambridge University Press, Cambridge, Cambridge.

  4. When? October 9th 2009 Formed in themorning ~ 1100 AKDT Reachedmaturity in afternoon ~ 1600 AKDT Starteddissipatingatnight ~ 2100 AKDT • Scale of the system ≈ 9° lat x 20° lon ≈ 1000km x 800km = 621mi x 501mi Oct 10th 2009 01:33 UTC (Oct 9th 1700 AKDT) Oct 9th 2009, 1633 UTC (0800 AKDT) Satelliteimageon IR channelfromthe DMS (DefenseMeteorologicalSatelliteProgram) OLS (OperationalLinescan System) sensor.

  5. Where? • ChukchiSea, western Beaufort Sea • Centered to the NW ofBarrow, AK • Regionwithconsiderableminimumofcylclonefrequencyandintensitywhencompared to otherregionsoftheArctic[Lynch, 2003] • Only published record of a polar-like disturbance in this region is by Twitchell [1989] about a system in October 1985 Lynch, A. H., E. N. Cassano, J. J. Cassano, and L. R. Lestak (2003), Case Studies of High Wind Events in Barrow, Alaska: Climatological Context and Development Processes, Monthly Weather Review, 131(4), 719-732. Twitchell, P. F. (1989), Polar and Arctic lows / edited by Paul F. Twitchell, Erik A. Rasmussen, and Kenneth L. Davidson, A. Deepak Pub, Hampton, Va., USA :.

  6. Research goals • Use modelingtechniques to understandphysicallyanddynamicallywhatledto developmentofthis polar low • Determine themostsuitablemodel setup for forecastingthiskindof system in thisarea

  7. Some model configurations • Weather Research and Forecasting model [Michalakes et al., 2001] • 2-way nesting : 2 domains with different resolutions are run simultaneously and communicatively • Resolutions 18km, 6km • Interpolation to 40 vertical levels • Initialization October 8th 2009 at 1200UTC • Run for60h Michalakes, J., S. Chen, J. Dudhia, L. Hart, J. Klemp, J. Middlecoff, and W. Skamarock (2001): Development of a Next Generation Regional Weather Research and Forecast Model. Developments in Teracomputing: Proceedings of the Ninth ECMWF Workshop on the Use of High Performance Computing in Meteorology. Eds. Walter Zwieflhofer and Norbert Kreitz. World Scientific, Singapore. pp. 269-276.

  8. Past/Present Research • Accomplished : modeling experiments Purpose: obtain solid simulation for further analysis assess model sensitivies/forecasting capabilities • Current work : Statistical evaluation of the obtained simulations • Experiment 1: Sensitivity to initialconditions: 4 simulationsusingdifferentreanalysisdatasets • Experiment 2: Sensitivity to physicalparameterizations: 10 ensemble membersandan ensemble meanvaryingmicrophysics, radiation, surfacelayer, boyndarylayerandconvectionschemes • Experiment 3: Sensitivity to initialization time: 5 simulations starting atdifferent times

  9. Data for model verification • Network of surface data being used for model verification

  10. Statistical parameters for model verification • Bias = • RMSE = • URMSE = Systematic errors resulting from model parameters, deficiencies, approximations Evaluates overall performance; sensitive to systematic and large errors Like the former but getting rid of the bias.

  11. Experiment 1: Sensitivity to IC • Same simulation setup • 4 different reanalysis datasets as input for initial and boundary conditions • NCEP Reanalysis 1 (NNRP1) • NCEP Reanalysis 2 (NNRP2) • European Interim Reanalysis (ERA) • Japanese Reanalysis (JRA)

  12. Just to give a broad “spatial” view of error magnitudes… Speed: from x<2 m/s to x>15 m/s Direction: from x<45° to x>315

  13. Domain-averaged statistics • RMSE and URMSE averaged over all times and all 205 stations • Loss of spatial variability evens out errors among different simulations

  14. Domain-averaged statistics • Bias for each simulation time averaged over all 205 stations • Same problem can be seen spatial averages are not ideal for evaluating differences among the simulations.

  15. Solution : look separately at each station • Selected sub-domain • Will show data for 5 stations very close to storm area Shell Beaufort Buoy Milne Point Barrow Deadhorse Reindeer Island

  16. Obs/Modeled+ σ surface wind speeds • 4 simulations, 1 point • Shell Beaufort Buoy (70.372N, 146.062W) • ERA captures reality better

  17. Obs/Modeled + σ surface wind speeds • 4 points, 1 simulation • ERA Simulation

  18. Errors for same 4 points and 4 simulations ERA : smaller RMSE for all 5 points

  19. Experiment 2: Sensitivity to physical parameterizations • Only ERA as IC • 10 different combination of physics • Not yet evaluated using station data • Preliminary assessment using reanalysis product indicates that • Ensemble Member 1 presents significantly smaller errors and biasesfor various atmospheric parameters • (wind speed and direction, sea-level pressure, relative humidity, geopotentialheight, air temperature)

  20. Experiment 3: Sensitivity to initialization time • Used setup of Ensemble Member 1 from previous Experiment • 5 starting times, 6 hours apart • Preliminary assessment indicates solid simulation – errors do not vary with initialization time Ensemble Member 1 from Experiment 2 ERA simulation from Experiment 1 5 simulations starting at different times Average of these 5 Temp. at 500hPa SLP RH at 850hPa

  21. Experiment 3: Sensitivity to initialization time • Model output for the best-performing simulation • Temperature at 2m • Wind at 10m • SLP Strong E, SE winds Beaufort High Polar Low Aleutian Low

  22. Experiment 4: Sensitivity to initialization time • Same as Experiment 3 but for model setup of ERA Simulation from Experiment 1 • Preliminary assessment shows that simulation was sensitive to initialization time – not stable (unlike Experiment 3) Temp. at 500hPa SLP RH at 850hPa

  23. Conclusions • According to preliminary analysis: • ERA seems to be the best performing reanalysis dataset for WRF input • A solid simulation can be achieved using ERA-Interim IC/BC and specific physical parameterizations combinations (that used for member 1 of Experiment 2)

  24. Thank you. pmoreira@alaska.edu

More Related