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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

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
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
quikscat fundamentals
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
slide5

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

fundamentals continued
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…
determining the winds
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)
determining the winds9
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
errors in the winds
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
errors in the winds11
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
errors in the winds12
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…
slide14

Best track info (18Z):

MSLP: 943mb

max winds: 120 kt

highest observed wind is O(70 kt)… only about 60% of actual storm strength

slide15

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)

slide16

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

overall diagnosis
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
critical uses of quikscat
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)
summary
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
summary21
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