1 / 37

Applications of Gridded Ocean Vector Wind Products

Applications of Gridded Ocean Vector Wind Products. Mark A. Bourassa, Ryan Maue, Steve Morey, and Jim O’Brien Center for Ocean - Atmospheric Prediction Studies The Florida State University. SeaWinds Daily (22 hour) Coverage. Ascending Node. Descending Node.

hisano
Télécharger la présentation

Applications of Gridded Ocean Vector Wind Products

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. Applications of Gridded Ocean Vector Wind Products Mark A. Bourassa, Ryan Maue, Steve Morey, and Jim O’Brien Center for Ocean-Atmospheric Prediction Studies The Florida State University

  2. SeaWinds Daily (22 hour) Coverage Ascending Node Descending Node From Paul Chang (NOAA/NESDIS): http://manati.wwb.noaa.gov/quikscat/ The Florida State University

  3. Gridded Products Show Large Scale Features The Florida State University

  4. Outline • Very quick description of publicly available gridded products • Brief description of input to gridding techniques • Examples of strengths and weaknesses • Are two satellites better than one? • Examines of applications • Mostly scientific applications • Some operational applications • Several views on ideal solutions. The Florida State University

  5. Publicly Available Gridded Wind Products • There are regularly gridded data products that are freely available and widely used. • Tang and Liu Twice daily global wind fields • Tang and Liu • QSCAT/NCEP Blended Ocean Winds from Colorado Research Associates (version 4.0) • Morzel, Milliff, and Chin • COAPS/FSU Objectively Analyzed Winds • Bourassa and O’Brien • The last two products are more similar with each other than the first product. The Florida State University

  6. Tang and Liu Twice daily global wind fields • Spatial/Temporal grid: • Temporal spacing: 12 hourly • Spatial grid spacing: 0.5° x 0.5° over water. • Data source: • NOAA Near Real Time winds. • Rain-contaminated data are not removed. • Produced by successive corrections using scatterometer winds, with QuikSCAT monthly averaged wind data as the initial fields. • http://airsea-www.jpl.nasa.gov/seaflux. • Tang, W. and W. T. Liu, 1996: Objective interpolation of scatterometer winds. JPL publication 96-19. 16pp. The Florida State University

  7. COAPS/FSU Objectively Analyzed Winds • Spatial/Temporal Grid: • Temporal spacing: 6 hourly • Spatial grid spacing: • 0.5° x 0.5° regional or monthly global over water • 1° x 1° global over water • Data source: • RSS winds, and for regional products only the Eta29 NWP winds. • Rain-contaminated scatterometer measurements are excluded. • Where to get the data: http://www.coaps.fsu.edu/cgi-bin/qscat/gcv_glob_L2B • Pegion, P. J., M. A. Bourassa, D. M. Legler, and J. J. O'Brien, 2000: Objectively-derived daily "winds" from satellite scatterometer data. Mon. Wea. Rev., 128, 3150-3168. • Morey, S. L., M. A. Bourassa, X. Davis, J. J. O’Brien, and J. Zavala-Hidalgo, 2005: Remotely sensed winds for forcing ocean models. J. Geophys. Res., accepted. The Florida State University

  8. QSCAT/NCEP Blended Ocean Winds from Colorado Research Associates (version 4.0) • Spatial/Temporal Grid: • Temporal spacing: 6 hourly • Spatial grid spacing: 0.5° x 0.5°, global from 88S to 88N • Data source: • JPL’s DIRTH winds and NCEP reanalysis. • Rain-contaminated scatterometer measurements are excluded for winds <15ms-1. • Where to get the data: http://dss.ucar.edu/datasets/ds744.4/ • Milliff et al. 2004 , Wind Stress Curl and Wind Stress Divergence Biases from Rain Effects on QSCAT Surface Wind Retrievals.J. Atmospheric and Oceanic Tech., Vol 21, pp 1216–1231 The Florida State University

  9. Key Differences • Gridding technique • Successive relaxation, • Different constraints in the COAPS and Colorado Research Associates products. • Gap filling related to either a vorticity constraint (COAPS) or a kinetic energy constraint (CRA). • Input data • NOAA’s NRT vs. RSS science quality product. • Blending with NWP or not. • Only important in data gaps. • Is rain contaminated data included? • Jan Morzel will speak about this issue later. The Florida State University

  10. Examples of Strengths and Weaknesses • And when the event is captured within a swath. • Problems occurs for • Rapidly translating features • Often find gaps in coverage, and • Combining data from two large a time span accounts for much of the error in the CRA and COAPS products. • Data loss and data errors associated with rain • Each of these techniques/products is very effective when conditions are not rapidly evolving. The Florida State University

  11. Good Example: Extratropical Irene (1999) 10/20 08z 8:42Z 7:02Z The Florida State University

  12. Pre-TS Irene (Oct. 10, 1999) The Florida State University

  13. Pre-TS Irene (Oct. 11, 1999) The Florida State University

  14. TS Irene (Oct. 14, 1999) The Florida State University

  15. Examples of Applications • ENSO-related changes in surface currents (Lagerloef et al., 2003 GRL). • Ocean (Gulf of Mexico) forcing (Curry et al., 2004 BAMS; Morey et al.). • Identification of MJOin gridded surface winds (Arguez et al., in review) • Heat transport in the Southern Ocean. • Dependency of modeled sea surface temperatures in the Black Sea on various forcing products (Kara et al., in review). • Surface fluxes & stability associated with tropical instability waves (several publications). • Gap flow (Chelton et al., 1999 MWR; Zamudio et al., 1999 MWR) • Influence of winds on the flight pattern and feeding habits of albatrosses. • Extratropical Transition (Maue, 2004, Masters thesis). • Validation of monthly wind product based on situ observations (Bourassa et al., accepted, JCLIM) The Florida State University

  16. MJO Example in Gridded Winds • Examples of the signal in zonal wind, zonal component of divergence, and zonal psuedostress. The Florida State University

  17. Coverage by Two SeaWinds Scatterometers • SeaWinds on QSCAT • SeaWinds on Midori2 The Florida State University

  18. Are Two Scatterometers Better Than One? • Example of Hurricane Fabian (2003) • Single scatterometer uses observations from a 24 hours, and a 72 hours for background • Duo scatterometers use observations from a 10 hour period and background from 30 hours SeaWinds on QuikSCAT and Midori SeaWinds on QuikSCAT The Florida State University

  19. Problems with Sampling: Tropics • The QSCAT-only fields (left) show a great deal of sampling-related variability in rapidly evolving features, such as hurricane Fabian. • The combined scatterometer fields (right) also suffer from this problem. The Florida State University

  20. Problems with Sampling: Tropics • The QSCAT-only fields (left) show a great deal of sampling-related variability in rapidly evolving features, such as hurricane Fabian. • The combined scatterometer fields (right) also suffer from this problem. The Florida State University

  21. Severe Storm Northern England, Scotland January 11, 2005 1904z Max winds > 60 m/s 0658Z Individual swaths are 12 hours apart for this rapid translation and a rapidly developing system. It is relatively small as well. So gridding is not as useful for this example. The Florida State University

  22. Rain Less of An Issue in Some Cases Temperature retrieval and Visible image. Even where the cloud cover and convection is not abundant, the strongest winds exist. The Florida State University

  23. Observational Cyclone Paradigms Norwegian (Bjerknes and Solberg 1922) & Shapiro-Keyser (1990 Figure from Schultz et al. (1998) Low zonal index Cold front dominant, stubby warm front Diffluent flow (jet exit region) Narrowing of warm sector Hydrostatic cold core occlusion High zonal index Warm front dominant Confluent jet streak entrance Encircling bent-back warm front Warm core seclusion The Florida State University

  24. <10 mph Strongest winds >100 mph 10/20/99 1100z NOAA-15 Irene Warm core seclusion 948 hPa The Florida State University

  25. 10/19 08z Irene QuikSCAT 0.5° Wind Speed 10/19 21z 10/20 08z The Florida State University

  26. Scatterometer-Derived Gridded PressuresTS Keith Statistics Best Track:20.8N 94.9W988 mb70 mph QSCAT:20.75N 94.75W989.9 mb Development of this technique was inspired by Patoux and Brown (2001) The Florida State University

  27. Example of a successful operational application: Ocean Surface Current Analysis – Real time The Florida State University

  28. Jason-1 Altimeter + QuikSCAT = Near real-time monitoring of total surface current in the tropical Pacific The Florida State University

  29. 5-DAY INTERVAL MAPS & ANOMALIES ZOOM IN SUB-AREAS The Florida State University

  30. Case Study: T.S. Harvey Sep. 19 – 22, 1999 The Florida State University

  31. NCOM vs. COMPS ADCP 4m Velocity T.S. Harvey Eta QuikSCAT QuikSCAT/Eta The Florida State University

  32. September 19, 0:00Z The Florida State University

  33. September 20, 0:00Z The Florida State University

  34. September 21, 0:00Z The Florida State University

  35. Sep. 18 Pre- T.S. Harvey Wind Stress+Heat Flux Wind Stress Only Heat Flux Only The Florida State University

  36. Sep. 23 Post- T.S. Harvey Wind Stress+Heat Flux Wind Stress Only Heat Flux Only The Florida State University

  37. What We Would Like in Future Observing Systems • Global Products: • No more than 6 hours between samples. • Severe Weather: • 2 to 3 hours (or shorter) sampling intervals. • 10km spatial resolution. • Coastal Work: • Sampling intervals of 2 hours or finer (diurnal and inertial). • Very fine spatial resolution. • Other Issues: • Correction, where possible for rain contamination. • An indication of the confidence (accuracy) of the correction. The Florida State University

More Related