1 / 22

An Introduction to the Near-Real-Time QuikSCAT Data

An Introduction to the Near-Real-Time QuikSCAT Data. Ross N. Hoffman and S. Mark Leidner (2005) Guy Cascella MPO531 Presentation 26 April 2007 . Motivation/Overview. Two main goals: show how well the “high-quality, high-resolution QuikSCAT data depict ocean surface winds”

zalman
Download Presentation

An Introduction to the Near-Real-Time QuikSCAT Data

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. An Introduction to the Near-Real-Time QuikSCAT Data Ross N. Hoffman and S. Mark Leidner (2005) Guy Cascella MPO531 Presentation 26 April 2007

  2. Motivation/Overview • Two main goals: • show how well the “high-quality, high-resolution QuikSCAT data depict ocean surface winds” • provide insight into the data errors; where and why they occur • examine how QuikSCAT works • calculating winds, errors in the winds • where QuikSCAT fails, example from paper • other specific uses of the data • concluding remarks

  3. QuikSCAT Fundamentals • NASA’s Quick Scatterometer (QuikSCAT) satellite contains SeaWinds instrument • active, microwave radar operational at 13.4 GHz • designed to observe ocean surface winds • launched on 19 June 1999 • each orbit is ~100 min, travels at ~7 km/sec at an altitude of 803km above the earth • quick math (Atul? Anyone?)… 15 orbits per day • 24 hours = 90% coverage

  4. SeaWinds

  5. Global QuikSCAT coverage for 1 November 2000; ascending passes are dark blue, descending are light blue, green shows a single total pass

  6. Fundamentals, continued • basic idea: determine wind speed based on ocean roughness (backscatter) • each observation samples a “box” (wind vector cell, WVC) of ocean 25km x 37km • each swath is ~1800km wide • first scatterometer with a rotating antenna • two beams, 40 and 46 degrees • each box may be observed several times during a pass… some more than others…

  7. SeaWinds schematic

  8. Determining the winds • wind vector determined by multiple observations at multiple viewing geometries • uses backscattering • idea: surface gravity waves and capillary waves create a surface roughness • “rougher” the surface, the higher the winds • surface waves tend to be aligned perpendicular to winds… can get wind direction • backscatter parameter, σ = F(α,θ,f,p)

  9. Determining the winds • backscatter parameter is applied to the “wind inversion” algorithm • but have multiple obs at every WVC… use statistical concept of the “maximum likelihood estimator” to get a single value • picks a distribution to fit data, usually N(μo,σ2) • μo usually known or estimated from previous obs • σ2 is usually unknown • here, σ2 is estimated as

  10. Errors in the winds • What factors negatively impact SeaWinds data? • (1) heavy rain (> 2.0 km mm hr-1) • affects (increases) surface roughness > affects backscatter parameter > affects wind vector • result: heavy rain tends to overestimate surface winds, and align wind direction across the swath (heavy rain will yield same backscatter at all angles of observation) • algorithm for “rain flags”, based on degree of consistency of backscatter and retrieved wind

  11. Errors in the winds • (2) low winds • difficult to predict accurately (no backscatter) • as wind → 0, surface roughness → 0 • ocean surface becomes closer to a “pure reflector” • direction near impossible to discern • result: low winds sometimes fail to show up; direction is generally an average of surrounding data points

  12. Errors in the winds • (3) high winds (> 25 m/s) • surface roughness “threshold” • backscatter must have an “upper limit” • result: high winds are generally underestimated • best displayed in a particular example…

  13. Hurricane Isaac, 22Z 18 Sep 2000

  14. Best track info (18Z): MSLP: 943mb max winds: 120 kt highest observed wind is O(70 kt)… only about 60% of actual storm strength

  15. Best track info (18Z): MSLP: 943mb max winds: 120 kt highest observed wind is O(70 kt)… only about 60% of actual storm strength does capture a min in winds in the eye of the hurricane (~45 kt)

  16. Best track info (18Z): MSLP: 943mb max winds: 120 kt highest observed wind is O(70 kt)… only about 60% of actual storm strength does capture a min in winds in the eye of the hurricane (~45 kt) places the center of circulation some 200km to the WSW

  17. Overall diagnosis • SeaWinds places the wind field appropriately around a strong tropical cyclone • accurately identifies rain flag areas in both in main area of circulation and outer rainbands • “recognizes” the eye • severely underestimates wind speed • severe bias in wind direction/center of circulation • all due to threshold in backscatter parameter • understanding air-sea interface under a TC is critical

  18. Tropical Storm Katrina, 8Z 25 Aug 2005

  19. Critical uses of QuikSCAT • precursor to tropical cyclone formation and intensification • upper level low in satellite images… • link to surface circulation? • frontogenesis • retrieved winds can be implemented in numerical weather prediction • track oceanic sea ice fraction (no retrievable winds over ice)

  20. Summary • SeaWinds instrument on QuikSCAT satellite determines surface winds based on backscattering from ocean surface • Has limitations… • (1) obviously only valid over ocean • (2) inaccurate for high rain rates • (3) does not capture weak winds well • (4) underestimates strong winds

  21. Summary • In terms of tropical cyclones: • (1) accurately portrays wind field • (2) displaces center of circulation • seems to be a correlation with strength of storm; stronger the storm, the greater the displacement • (3) accurately places rain flags in appropriate areas of TC • Overall: QuikSCAT is a vital tool in weather forecasting

  22. Thank you.

More Related