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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”

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An Introduction to the Near-Real-Time QuikSCAT Data

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  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.

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