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(μ o ,σ 2 ) μ o usually known or estimated from previous obs σ 2 is usually unknown  here, σ 2 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.