Applications of Gridded Ocean Vector Wind Products

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


SeaWinds Daily (22 hour) Coverage

Ascending Node Descending Node

From Paul Chang (NOAA/NESDIS): http://manati.wwb.noaa.gov/quikscat/



Gridded Products Show Large Scale Features



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.



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.



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.



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.



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



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.

The Florida State UniversityOcean Vector Winds

Gridded Products 10 http://coaps.fsu.edu/~bourassa/

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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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Good Example: Extratropical Irene (1999) 10/20 08z

8:42Z

7:02Z



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Pre-TS Irene (Oct. 10, 1999)



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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.



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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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10/20 08z

Irene QuikSCAT 0.5° Wind Speed

10/19 08z

10/19 21z



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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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September 21, 0:00Z



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Win

d S

tres

s+H

eat F

lux

Hea

t Flu

x O

nly

Win

d S

tres

s O

nly

Sep. 18Pre- T.S. Harvey



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Win

d S

tres

s+H

eat F

lux

Hea

t Flu

x O

nly

Win

d S

tres

s O

nly

Sep. 23Post- T.S. Harvey



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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.

Documents

Applications of Gridded Ocean Vector Wind Products