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”
 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
 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
SeaWinds
Global QuikSCAT coverage for 1 November 2000; ascending passes are dark
blue, descending are light blue, green shows a single total pass
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…
SeaWinds schematic
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 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
 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 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 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…
Hurricane Isaac, 22Z 18 Sep 2000
Best track info (18Z):
MSLP: 943mb
max winds: 120 kt
highest observed wind
is O(70 kt)… only about
60% of actual storm
strength
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)
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
 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
Tropical Storm Katrina, 8Z 25 Aug 2005
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
 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
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
Thank you.