The Post QuikSCAT ERA
Paul Chang and Zorana Jelenak
NOAA/NESDIS
August 10, 2010
Corp Symposium

Outline

QuikSCAT impacts

QuikSCAT decade-long OSVW record

Post QuikSCAT mitigation efforts

Outlook

Benefits Gained from QuikSCAT at National Hurricane Center
 Increased surface wind data for all three major analysis parameters

Intensity (Maximum sustained 1-min 10-m wind)

Center Fixing/Identification (short term motion and model initialization, initiation and discontinuation of advisories)

Structure (34-, 50-, and 64-kt wind radii)

Arctic Support Services
Support to the USCG Hamilton on Arctic Mission Sep 2008
• Ocean surface vector winds and sea ice
• extent and characterization
• In support of maritime commerce
• and resource exploitation

NOAA Ocean Prediction Center –
Impact on Operations
QuikSCAT
Forecasters used every pass to: make warning and short-term forecast decisions and to estimate
- cyclone location, intensity, extent of wind field
- strength and extent of orographically enhanced jets
- wind field near strong SST gradients
Resulted in improved warning and forecast services over otherwise data sparse oceans

Extratropical Cyclones with Hurricane Force Winds
Detected using QuikSCAT

70
60

WARNING

CATEGORIES

Pre- QSCAT

1. GALE 34-47 kt

2. STORM >48

QSCAT ERA

1. GALE 34-47 kt

2. STORM 48 -63 kt


3. HURCN FORCE
> 64 kt l o
C y c n e s
50
40
30
20
10
•
Hurricane Force Warning Initiated Dec 2000
•
Detection increased with
-
Forecaster familiarity
-
Data availability

Improved wind
 algorithm and
 rain flag Oct 06
-
Improved resolution
-
Improved algorithm

12.5 km QuikSCAT
 available May 04

25 km QuikSCAT

Available in N-AWIPS

Oct 01
37
33 31
34
64
51
43
48

Hurricane Force

Wind Warning

Initiated Dec 00
23
24 23
22

QuikSCAT

Launch

Jun 99
11
9
14
15
0
1997-

98
1998-
99
1999-
00
2000-
01
2001-
02
2002-
03
2003-
04
2004-
05
2005-
06
2006-
07
2007-
08
Atlantic
Pacific

Extreme winter ocean storms occur over major shipping routes: North Atlantic example

Major Shipping Routes

North Atlantic

4,000/yr container transits

1,000/yr bulkers

The geographic distribution of all cyclone centers that contained hurricane force conditions for the
North Atlantic basin 2001 - 2008

Geographic distribution of cyclones with winds of HF intensity

7

Sep-May 2000-2007

Cumulative number of extratropical cyclones observed (contoured) with hurricane force winds for the years 2000 - 2007
Hurricane Force Winds Occur Across the Sea Lanes

~ 6,000 Containership transits/year

Operating costs - $50,000 per day

~1,500 Dry bulk transits/year

Operating costs - $20,000 per day

Major Shipping Routes

North Pacific


Present level of warning/forecast services to 48 hours w/QuikSCAT
 $135 million per year savings in reduced damage / cargo loss for container and bulk commerce (Kite-
Powell, 2008)

Kite-Powell, Hauke, 2008: Benefits to Maritime Commerce from Ocean Surface Vector Wind Observations and Forecasts, Maritime Economics and Logistics (in review), pp. 15. Available online at: http://manati.orbit.nesdis.noaa.gov/SVW_nextgen/QuikSCAT_maritime_report_final.pdf

Millersville University, Nov 12, 2009

QuikSCAT Hurricane Force Extratropical
Cyclones Observations - Summary

QuikSCAT – Hurricane Force Winds in extratropical cyclones more frequent than thought



Predominantly cold season phenomena
Onset, rapidly deepening phase
Ocean phenomena but…
 landfall AK, PAC NW, Europe, even East Coast

Conditions impact coastline without landfall

Climiatology of Hurricane Force
Extratropical Cyclones

Utilize decade long QuikSCAT OSVW record to study frequency and characteristics of these extreme events

HF Cyclone Database

Storm ID

Date

Lat

Lon
.

Press

Type
PAC011007 2007100712 43.93 -169.72
1013 L
PAC011007 2007100718 43.23 -164.06
PAC011008 2007100800 42.37 -158.38
PAC011008 2007100806 41.33 -152.07
PAC011008 2007100812 39.87 -143.82
PAC011008 2007100818 40.76 -137.83
PAC011009 2007100900 43.15 -134.94
PAC011009 2007100906 44.46 -134.08
PAC011009 2007100912 46.45 -132.94
PAC011009 2007100918 47.51 -132.81
PAC011010 2007101000 48.90 -132.36
PAC011010 2007101006 50.65
-132.3
PAC011010 2007101012 52.17 -132.36
PAC011010 2007101018 53.67 -131.68
1010 G
1010 G
1007 G
998
974
970 H
964 H
966 H
968 H
967 S
S
S
964 S
975 G
977 G





Estimate cyclone motion speed and direction
Extract all hurricane force 6-h cycles per month
Extract hurricane force events during 6h, 12h 18h, 24h and >24h
Perform statistical analysis of these events
Select QuikSCAT files that correspond to each chosen event

Cyclone Motion Speed and Direction during HF cycles

Mean~
50 °


Fast moving cyclones with average storm speed ~25kts
Storm moving in NE direction (average heading 50 °) during hurricane force wind phases

Number of HF Cyclones per Month
2001-2008 North Pacific
No of HF Cyclones No of 6h HF Cycles
250
200
150
100
50
0
Sep Oct Nov Dec Jan Feb Mar Apr



Extratropical cyclone season from September to April
Peak activity during December and January
September and April have same number of cyclones, but April storms last longer

Duration of HF Events
25
20
15
10
5
0
6h 12h 18h 24h 30h 36h 42h 48h 54h
HF Event Duration




Most HF events last between 6-24h
50% of 12-h events occurred during December
75% of 30-h events occurred during November and December
In February most events last between 18-24h

Mean Cyclone Wind Speed
2001-2008 North Pacific

3000km

2000km

Storm motion direction

1000km

Wind Force Frequencies
Hurricane Force > 64kts Storm Force Wind >48kts
Gale Force >32kts


W

October

N

E

November

S

January

February

December

March

300
250
200
150
100
50
0
0-6h 6-12h 12-18h 18-24h >24h

12-
18h

0-6h

18-
24h

6-
12h

>24h

QuikSCAT decade-long OSVW record

Impact of the changing wind on evaporation
 perspective from the OAFlux 50-year time series
Trend in global E
Effect of Wind (U)
Effect of humidity (  q)
Courtesy L. Yu, WHOI, 2009

Coherent Changes in E and Wind: tropical oceans
Courtesy L. Yu, WHOI, 2009

Ocean Circulation and
Climate Change
 Can satellite winds and an ocean model be used to predict northern hemisphere Tropical cyclone activity a year in advance?
Global sea level rise is due to two components:
– fluxes of water into the oceans, and
– steric sea level rise due to heat induced expansion of the ocean’s volume.
• Wind forcing modifies the ocean’s circulation, modifying the pattern of steric sea level rise.
• The observation of the modulation of large-scale ocean sea level by winds has been enabled by the availability of a decade long scatterometer wind data record.
Graphics from Maue, R. N., 2009: Northern Hemisphere Tropical Cyclone Activity. Geophys. Res. Lett., 36

Gas Fluxes into the Ocean
0.8
0.6
0.4
0.2
0
1.4
1.2
1
A key unknown in predicting global climate change is the rate of uptake by the oceans of greenhouse gases
– Oceans constitute their primary sink (for the moment).
– Winds are the primary uncertainty in the determination of the gas transfer coefficient for CO
2
( Gary Wick , personal communication, 2008)
– Long term changes in wind appear to be correlated to changes in evaporation ( Yu et al . 2007, 2009)
• Water vapor is the key greenhouse gas
• A longer time series based on better inter calibrated data is needed
COARE-only, no bubble
GasEx-98
GasEx-01
SO-GasEx
Sensitivity analysis of errors in
CO2 transfer velocity
Far more sensitive to errors in wind than other input variables
Graphic courtesy Gary Wick
ALL Ws SST Ta Qa Rl Rs

Mitigation Efforts

ASCAT Measurements Geometry

Top view

Can ASCAT high wind speed retrievals be improved?
200
180
160
140
120
100
80
60
40
20
0
QSCAT-Hi

Hurricane Force Observations

Oct’07 ~ May’08

195

168
• Loss of QuikSCAT data could possibly result in 80-
90% reduction in detection of hurricane force winds
• Necessary to investigate improvement in ASCAT wind retrievals to try better support wind warning and forecast products for extreme winter ocean storms
QSCAT-p ASCAT

2
GFS

38
ECMWF

4

15
Surf. Obs.
•
ASCAT underestimate high wind speeds in comparison to
QuikSCAT
•
Hurricane force wind detection from
ASCAT is very rare

Measured and Model Sigma0 dependence on wind vector at 30 deg incidence

ASCAT/QuikSCAT match-ups @ 30 ° incidence

5m/s

9m/s

12m/s

CMOD5n B
0

15m/s

17m/s

Relative wind direction (Degree)

“True” B
0

20m/s

CMOD5n vs New B
0

CMOD5n B
0

New B
0

Wind Speed (m/s)

ASCAT vs QuikSCAT speed (before)

Cumulative
 distribution
 curve

ASCAT vs QuikSCAT speed (after)

Cumulative
 distribution
 curve

Across Swath Speed Biases (before)

2 m/s reference line

Across Swath Speed Biases (after)

2 m/s reference line

Across Swath Speed RMS errors (before)

2 m/s reference line

Across Swath Speed RMS errors (after)

2 m/s reference line



( Oct’07 ~ May’08 )
250
200
150
100

212

188
50

2 
39

38 
19
0
QS -12.5 km QS - 25 km AS - opr AS - new GFS OBS


North Atlantic Extratropical Storm Example
Operational ASCAT

New ASCAT

Operational ASCAT wind product detects
STORM force wind.
One wind warning category lower than the actual winds.

QuikSCAT & New
ASCAT wind products detects HF wind.

QuikSCAT


Operational

ASCAT

Sonde Wind Speed
28.8m/s

SFMR 28 m/s

ASCAT 22 m/s


New

ASCAT

Sonde Wind Speed
28.8m/s

SFMR 28m/s

New ASCAT 25m/s

Outlook

QuikSCAT and DFS Scatterometer Designs
~2m
~1m
Ku-band
HH
VV
41
Ku-band+ C-band
HH
VV


DFS vs QuikSCAT Errors

Simulated Katrina QuikSCAT Performance

Measuring winds or rain?

SeaWinds Scatterometer

Surface Winds

Weather Research and Forecasting
Model (WRF) Surface Wind “Truth”

Weather Research and Forecasting
Model (WRF) Rain Rate “Truth”

Simulated Retrievals based on Katrina (2005)

QuikSCA
T t

54k t

64k

“Truth
” t

54k
 t
64k

DFS t

54k t

64k

T
S
•
DFS better captures true wind signal where QuikSCAT high winds are tied to rain
•
DFS accurately depicts hurricane-force wind radii and retrieves winds into cat 2 range
•
DFS cannot identify small scale wind maxima

Katrina Simulated Retrievals

DFS

QuikSCAT
•
At high wind speeds, the dual frequency scatterometer can improve performance up to 50%
•
The resolution of the phenomena measured is higher than resolution of DFS measurements therefore
• highest winds in TC’s can not be resolved
It is expected that DFS can provide wind speeds up to category 2 hurricane

Summary




QuikSCAT OSVW had tremendous benefits to operational weather forecasting and warning
 Data from a research satellite was succesfully transition to operational utilization
The importance of satellite OSVW to climate monitoring and prediction is evident


Characterization of the important air-sea interface
Monitoring of extreme events
ASCAT can only partially mitigate the loss of QuikSCAT
NOAA is pursuing a QuikSCAT follow-on mission working the JAXA and
NASA/JPL (Dual-Frequency Scatterometer)


Effort is ongoing but still far from certain
The U.S. does not have a well defined process by which to objectively identify and transition required core satellite observing system capabilities from research to sustained operations

 backup


Courtesy of Prof Mark
Bourassa FSU

Near coherent decadal variability in the Indo-Pacific region captured by
QuikSCAT & ERS scatterometers & altimeter data: opposite trends before & after year 2000

Decadal tendencies in the 1990s & 2000s are opposite in sign in much of the Indo-Pacific domain for wind ( ERS and QSCAT ) and sea level (T/P and J1). The wind patterns explains the sea level pattern
(through local Ekman pumping and westward propagation of Rossby waves.

They have strong implications to ocean circulation and heat transport, and highlight the oceanic and atmospheric teleconnection in the Indo-
Pacific region.

Lee & McPhaden
(2008), GRL.


Comparison of
QuikSCAT and Drift-
Age Model

Large reduction of
 perennial sea ice
 between years

2005-2007.

Red line represents the bound-
 ary of perennial ice from the
 the Drift-Age Model (>1 year)
NASA QuikSCAT scatterometer

Nghiem, Rigor, Perovich, Clemete-
Colón, Weatherly, and Neumann,
GRL, 2007.


2007

March

1970-2000:

Decrease of 0.5x10
6 km 2 /decade.

In the decade
2000s

Rapid reduction of perennial sea ice suggested by the model and verified by QuikSCAT data.

Decrease of 1.5x10
6 km 2 /decade, triple that in 1970-2000.

Climate Monitoring

Scatterometer OSVW measurements directly supports climate research and services by providing measurements needed to document:


 heat uptake, transport, and release by the ocean; ocean carbon sources and sinks
(ocean surface vector winds modulates air-sea exchanges of gases such as CO 2 ) and air-sea exchange of water and the ocean ’s overturning circulation

Study Approach


Scatterometer wind retrievals are dependent on Geophysical
Model Function (GMF) that relates backscatter to ocean surface wind fields
The ASCAT operational GMF (CMOD5n) is written in harmonic terms:

0

B
0
(

,

)

[ 1

B
1
(

,

) cos
 
B
2
(

,

) cos 2

]
1 .
6



Hersbach, et al., J. Geophys. Res. (2007)
Assumption is that B
1 and B
2 are modeled correctly.
ASCAT/QuikSCAT match-ups show B wind speeds
0 is biased high at high

C-Band V-Pol sigma0 dependence on wind speeds

Aircraft experiment

(IWRAP)

CMOD5n

IWRAP valid range

25-65 m/s



QuikSCAT

ASCA
T

Mean Wind Speed Field
2007-2009 Winter Seasons - North Pacific

QuikSCAT

NEW ASCAT

Mean Wind Speed Field
2007-2009 Winter Seasons - North Pacific

QuikSCAT

NEW ASCAT



QuikSCAT
Hurricane Force Wind
Frequency

Storm Force Wind
Frequency

Gale Force Wind
Frequency

ASCAT

Hurricane Force Wind
Frequency

Storm Force Wind
Frequency

Gale Force Wind
Frequency


QuikSCAT

Hurricane Force Wind
Frequency

Storm Force Wind
Frequency

Gale Force Wind
Frequency

NEW ASCAT

Hurricane Force Wind
Frequency

Storm Force Wind
Frequency

Gale Force Wind
Frequency