http://coaps.fsu.edu/~bourassa/[email protected]
Applications of Gridded Ocean Vector Wind ProductsMark A. Bourassa, Ryan Maue, Steve
Morey, and Jim O’BrienCenter for OceanAtmospheric Prediction
StudiesThe Florida State University
The Florida State UniversityOcean Vector Winds Gridded Products 2
http://coaps.fsu.edu/~bourassa/[email protected]
SeaWinds Daily (22 hour) Coverage
Ascending Node Descending Node
From Paul Chang (NOAA/NESDIS): http://manati.wwb.noaa.gov/quikscat/
The Florida State UniversityOcean Vector Winds Gridded Products 3
http://coaps.fsu.edu/~bourassa/[email protected]
Gridded Products Show Large Scale Features
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Outline
Very quick description of publicly available gridded products Brief description of input to gridding techniques Examples of strengths and weaknesses
Are two satellites better than one? Examines of applications
Mostly scientific applications Some operational applications
Several views on ideal solutions.
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Publicly Available Gridded Wind Products
There are regularly gridded data products that are freely available and widely used. Tang and Liu Twice daily global wind fields
Tang and Liu QSCAT/NCEP Blended Ocean Winds from Colorado
Research Associates (version 4.0) Morzel, Milliff, and Chin
COAPS/FSU Objectively Analyzed Winds Bourassa and O’Brien
The last two products are more similar with each other than the first product.
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http://coaps.fsu.edu/~bourassa/[email protected]
Tang and Liu Twice daily global wind fields
Spatial/Temporal grid: Temporal spacing: 12 hourly Spatial grid spacing: 0.5° x 0.5° over water.
Data source: NOAA Near Real Time winds. Rain-contaminated data are not removed.
Produced by successive corrections using scatterometer winds, with QuikSCAT monthly averaged wind data as the initial fields.
http://airsea-www.jpl.nasa.gov/seaflux. Tang, W. and W. T. Liu, 1996: Objective interpolation of
scatterometer winds. JPL publication 96-19. 16pp.
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COAPS/FSU Objectively Analyzed Winds
Spatial/Temporal Grid: Temporal spacing: 6 hourly Spatial grid spacing:
0.5° x 0.5° regional or monthly global over water 1° x 1° global over water
Data source: RSS winds, and for regional products only the Eta29 NWP winds. Rain-contaminated scatterometer measurements are excluded.
Where to get the data: http://www.coaps.fsu.edu/cgi-bin/qscat/gcv_glob_L2B
Pegion, P. J., M. A. Bourassa, D. M. Legler, and J. J. O'Brien, 2000: Objectively-derived daily "winds" from satellite scatterometer data. Mon. Wea. Rev., 128, 31503168.
Morey, S. L., M. A. Bourassa, X. Davis, J. J. O’Brien, and J. Zavala-Hidalgo, 2005: Remotely sensed winds for forcing ocean models. J. Geophys. Res., accepted.
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http://coaps.fsu.edu/~bourassa/[email protected]
QSCAT/NCEP Blended Ocean Winds from Colorado Research Associates
(version 4.0) Spatial/Temporal Grid:
Temporal spacing: 6 hourly Spatial grid spacing: 0.5° x 0.5°, global from 88S to 88N
Data source: JPL’s DIRTH winds and NCEP reanalysis. Rain-contaminated scatterometer measurements are
excluded for winds <15ms-1. Where to get the data: http://dss.ucar.edu/datasets/ds744.4/ Milliff et al. 2004 ,
Wind Stress Curl and Wind Stress Divergence Biases from Rain Effects on QSCAT Surface Wind Retrievals. J. Atmospheric and Oceanic Tech., Vol 21, pp 1216–1231
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Key Differences
Gridding technique Successive relaxation, Different constraints in the COAPS and Colorado Research
Associates products. Gap filling related to either a vorticity constraint
(COAPS) or a kinetic energy constraint (CRA).
Input data NOAA’s NRT vs. RSS science quality product. Blending with NWP or not.
Only important in data gaps. Is rain contaminated data included?
Jan Morzel will speak about this issue later.
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Examples of Strengths and Weaknesses
And when the event is captured within a swath.
Problems occurs for Rapidly translating features
Often find gaps in coverage, and Combining data from two large a time span accounts for
much of the error in the CRA and COAPS products. Data loss and data errors associated with rain
Each of these techniques/products is very effective when conditions are not rapidly evolving.
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Gridded Products 11 http://coaps.fsu.edu/~bourassa/
Good Example: Extratropical Irene (1999) 10/20 08z
8:42Z
7:02Z
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Gridded Products 12 http://coaps.fsu.edu/~bourassa/
Pre-TS Irene (Oct. 10, 1999)
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Gridded Products 13 http://coaps.fsu.edu/~bourassa/
Pre-TS Irene (Oct. 11, 1999)
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TS Irene (Oct. 14, 1999)
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Examples of Applications
ENSO-related changes in surface currents (Lagerloef et al., 2003 GRL). Ocean (Gulf of Mexico) forcing (Curry et al., 2004 BAMS; Morey et al.).
Identification of MJO in gridded surface winds (Arguez et al., in review) Heat transport in the Southern Ocean. Dependency of modeled sea surface temperatures in the Black Sea on
various forcing products (Kara et al., in review). Surface fluxes & stability associated with tropical instability waves (several
publications). Gap flow (Chelton et al., 1999 MWR; Zamudio et al., 1999 MWR) Influence of winds on the flight pattern and feeding habits of albatrosses. Extratropical Transition (Maue, 2004, Masters thesis). Validation of monthly wind product based on situ observations (Bourassa et
al., accepted, JCLIM)
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MJO Example in Gridded Winds
Examples of the signal in zonal wind, zonal component of divergence, and zonal psuedostress.
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Coverage by Two SeaWinds Scatterometers
SeaWinds on QSCAT SeaWinds on Midori2
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Are Two Scatterometers Better Than One?
Example of Hurricane Fabian (2003) Single scatterometer uses observations from a 24 hours, and a 72 hours for
background Duo scatterometers use observations from a 10 hour period and background from
30 hours
SeaWinds on QuikSCAT SeaWinds on QuikSCAT and Midori
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Problems with Sampling: Tropics
The QSCAT-only fields (left) show a great deal of sampling-related variability in rapidly evolving features, such as hurricane Fabian.
The combined scatterometer fields (right) also suffer from this problem.
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Problems with Sampling: Tropics
The QSCAT-only fields (left) show a great deal of sampling-related variability in rapidly evolving features, such as hurricane Fabian.
The combined scatterometer fields (right) also suffer from this problem.
The Florida State UniversityOcean Vector Winds
Gridded Products 21 http://coaps.fsu.edu/~bourassa/
Severe Storm Northern England, ScotlandJanuary 11, 2005
1904z Max winds > 60 m/s0658Z
Individual swaths are 12 hours apart for this rapid translation and a rapidly developing system. It is relatively small as well. So gridding is not as useful for this example.
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Rain Less of An Issue in Some Cases
Temperature retrieval and Visible image. Even where the cloud cover and convection is not abundant, the strongest winds exist.
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Observational Cyclone Paradigms
Norwegian (Bjerknes and Solberg 1922) & Shapiro-Keyser (1990
Figure from Schultz et al. (1998)
Low zonal index Cold front dominant, stubby warm frontDiffluent flow (jet exit region)Narrowing of warm sectorHydrostatic cold core occlusion
High zonal indexWarm front dominantConfluent jet streak entranceEncircling bent-back warm frontWarm core seclusion
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10/20/99 1100z NOAA-15Irene Warm core seclusion 948 hPa
Strongest winds
>100 mph
<10 mph
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Gridded Products 25 http://coaps.fsu.edu/~bourassa/
10/20 08z
Irene QuikSCAT 0.5° Wind Speed
10/19 08z
10/19 21z
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Gridded Products 26 http://coaps.fsu.edu/~bourassa/
Scatterometer-Derived Gridded PressuresTS Keith
Statistics
Best Track:20.8N 94.9W988 mb70 mph
QSCAT:20.75N 94.75W989.9 mb
Development of this technique was inspired by Patoux and Brown (2001)
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Example of a successful operational application:
Ocean Surface Current Analysis – Real time
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Near real-timemonitoring of total surface currentin the tropical Pacific
Jason-1Altimeter
QuikSCAT
+
=
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5-DAY INTERVAL MAPS&
ANOMALIES
ZOOM INSUB-AREAS
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Case Study: T.S. Harvey Sep. 19 – 22, 1999
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4 m ADCP (red) and NCOM ETA (black) Velocity
8/ 1 8/ 8 8/15 8/22 8/29 9/ 5 9/12 9/19 9/260
20
40
60
80
100
Sca
le c
m/s
Complex Correlation: Magnitude = 0.52, Phase Angle = -6.3°, Speed Correlation = 0.32
4 m ADCP (red) and NCOM Qscat/ETA (black) Velocity
8/ 1 8/ 8 8/15 8/22 8/29 9/ 5 9/12 9/19 9/260
20
40
60
80
100
Sca
le c
m/s
Complex Correlation: Magnitude = 0.84, Phase Angle = 5.5°, Speed Correlation = 0.84
4 m ADCP (red) and NCOM Qscat (black) Velocity
8/ 8 8/15 8/22 8/29 9/ 5 9/12 9/19 9/260
20
40
60
80
100
Sca
le c
m/s
Complex Correlation: Magnitude = 0.61, Phase Angle = 1.0°, Speed Correlation = 0.46
8/ 1
NCOM vs. COMPS ADCP 4m Velocity
T.S. Harvey
Eta
QuikSCAT
QuikSCAT/Eta
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September 19, 0:00Z
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September 20, 0:00Z
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Gridded Products 34 http://coaps.fsu.edu/~bourassa/
September 21, 0:00Z
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Win
d S
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s+H
eat F
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Hea
t Flu
x O
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Win
d S
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Sep. 18Pre- T.S. Harvey
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Gridded Products 36 http://coaps.fsu.edu/~bourassa/
Win
d S
tres
s+H
eat F
lux
Hea
t Flu
x O
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Win
d S
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Sep. 23Post- T.S. Harvey
The Florida State UniversityOcean Vector Winds
Gridded Products 37 http://coaps.fsu.edu/~bourassa/
What We Would Like in Future Observing Systems
Global Products: No more than 6 hours between samples.
Severe Weather: 2 to 3 hours (or shorter) sampling intervals. 10km spatial resolution.
Coastal Work: Sampling intervals of 2 hours or finer (diurnal and inertial). Very fine spatial resolution.
Other Issues: Correction, where possible for rain contamination. An indication of the confidence (accuracy) of the correction.