Surface-Reflected GPS Wind Speed Sensing Results for 2010 Atlantic Season
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Transcript of Surface-Reflected GPS Wind Speed Sensing Results for 2010 Atlantic Season
Surface-Reflected GPS Wind Speed Sensing Results for 2010 Atlantic Season
6565thth Interdepartmental Hurricane Conference Interdepartmental Hurricane ConferenceMiami, FL
Feb. 28 – March 3, 2011
Michael S. Grant, (NASA/Langley Research Center)
Stephen J. Katzberg,(NASA/Distinguished Research Associate)
Jason P. Dunion (Univ. Miami/NOAA/AOML/Hurricane Research Division)
Surface-Reflected (Bistatic) GPS Method
2010 Storm Season Wind Speed Retrievals Retrieval examples Quantitative comparisons to SFMR and dropsondes
Summary Statistics for Measurement Comparisons
Future Research Objectives
Presentation Outline
Ocean Roughness / Wind Speed from Bistatic Surface Reflections
Constant Path Delay Ellipses in Reflection Area
z
Py
h
x
Reflection Area
GPS GPS
Sγ
Increasing Surface Roughness
(Instrument Correlation Peak)
delay, τ
Cor
rela
tion
(Ref
lect
ed P
ower
)
Ocean ‘roughness’ (surface slope variance) used to infer surface wind speed Slope-to-wind speed: empirical relationship.
Reflected GPS signal strength (~ power) vs. delay is measured Waveform widens (more scattering) with increasing surface roughness. Sensing location on surface depends on satellite-aircraft reflection path geometry
GPS Instrument easily deployed Light aircraft and up Flown (2004) Aerosonde UAV – 10’ wing span
Instrument - Receiver Unit and two Antennae
Instrument size ~ 16 x 12 x 7 inches Weight < 10 lbs. 3.5” nadir antenna
NASA Bistatic GPS Instrument Accommodation
NASA-Langley Bistatic GPS Instrument
AOC WP-3D Orion
Cessna 206
Aerosonde UAV
NASA-Langley bistatic GPS instruments deployed on ‘N42 and ‘N43 P-3 Hurricane Hunter Aircraft (Aircraft Operations Center, Tampa)
Instruments operated by AOC personnel (Power on, autonomous operation, power off, upload flight data to ftp site post-mission)
31 total P-3 flights (incl. ferry) where GPS instruments were operated. Data sets acquired on all flights – no instrument anomalies. 18 GPS data sets had contemporaneous data available for comparison:
SFMR, Flight-Level winds, and dropsondes (16 of 18) Variety of SFMR/dropsonde wind speeds ranges for the 18 GPS data sets:
lowest: 2 – 12 ms-1
highest: 5 – 60+ ms-1
For 2010 quantitative comparisons, only GPS over-land reflections data removed. No other data exclusions or masking operations were performed.
GPS Instrument 2010 Atlantic Storm Season Deployments
Stepped-Frequency Microwave Radiometer (SFMR) [1]
Operational instrument, high precision (within 2% at 30 ms-1) [2]
Emissivity (brightness temp., TB), surface wind speed proportional to % sea foam.
NASA-Langley Bistatic GPS Instrument - surface wind speed obtained from measured sea-surface slopes through empirical relationship [3][4]
[1] Black, P. G., and C. L. Swift, 1984: Airborne stepped frequency microwave radiometer measurements of rainfall rate and surface wind speed in hurricanes. Second Conf. on Radar Meteorology, Zurich, Switzerland, Amer. Meteor. Soc.
[2] Uhlhorn, Black, et al., Hurricane Surface Wind Measurements from an Operational Stepped Frequency Microwave Radiometer, Monthly Weather Review, 2007, Vol. 135, p. 3070
Wind Speed Retrieval Intercomparisons - Background
[3] Katzberg, Stephen J., Omar Torres, and George Ganoe, “Calibration of reflected GPS for tropical storm wind speed retrievals”; Geophys. Res. Lett., 33, L18602, doi:10.1029/2006GL026825, 2006[4] Katzberg, S. J., and J. Dunion , “Comparison of reflected GPS wind speed retrievals with dropsondes in tropical cyclones,” Geophys. Res. Lett., 36, L17602, doi:10.1029/2009GL039512., 2009
Initial calibration using Navy COAMPS model [3]
Comparison with dropsondes at hurricane wind speeds [4]
Low-wind speed example -Tropical Depression #5 (Aug 11, 2010)
Wind Speed Retrieval Intercomparisons - Quantitative
7.2 7.4 7.6 7.8 8 8.2 8.4 8.6 8.8 9 9.2
x 104
0
5
10
15
20
25
30
35
40
45
UTC "today"(sec)
U10
WS
pd (m
/s)
GPS-Derived Surface Winds - Trop Depression #5, N42 (Aug 11, 2010)
GPS 2s-AvgSFMR0.8Flt-LvlSondes
Histogram of (SFMR – GPS) differences (ms-1)
99oW 90oW
81o W
72o W
63o W
16o N
24o N
32o N
40o N
Begin EndLon
Lat
Flight Path (GPS) - Trop Depression #5, N42 (Aug 11, 2010)
Comparison to SFMR Mean diff. = 4.3 ms-1 (GPS underestimate) RMS diff. = 2.3 ms-1
Comparison to dropsondes Mean diff. = -0.5 ms-1 (GPS overestimate) RMS diff. = 2.9 ms-1
High-wind speed example 1 – Hurricane Earl (Sept 1, 2010)
Wind Speed Retrieval Intercomparisons – Quantitative, cont’d
Histogram of (SFMR – GPS) differences (ms-1)
Comparison to SFMR Mean diff. = -0.4 ms-1
RMS diff. = 7.2 ms-1
12 - 20 ms-1 difference at eyewall
Comparison to dropsondes Mean diff. = 2.7 ms-1 (HSA-file, single near-surface value)
8 8.2 8.4 8.6 8.8 9 9.2 9.4 9.6
x 104
0
10
20
30
40
50
60
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UTC "today"(sec)
U10
WS
pd (m
/s)
GPS-Derived Surface Winds - Hurr. Earl, N43 (Sept 01, 2010) (>30 sat-elev)
GPS mdl-filtSFMR0.8Flt-LvlSondes
96oW 84
o W 72o W 60
o W
48o W
0o
12o N
24o N
36o N
48o N
Begin
End
Lon
Lat
Flight Path (GPS) - N43, Sept 01, 2010
High-wind speed example 2 – Hurricane Earl along East Coast (Sept 3, 2010)
Wind Speed Retrieval Intercomparisons – Quantitative, cont’d
Comparison to SFMR Mean diff. = 5.9 ms-1
RMS diff. = 7.2 ms-1
Comparison to dropsondes Mean diff. = 4.2 ms-1 (GPS underestimate) RMS diff. = 7.2 ms-1
Histogram of (SFMR – GPS) differences (ms-1)
90o W
80o W 7
0o W
60o W
50o W
18o N
27o N
36
o N
45o N
54o N
Begin
End
Lon
Lat
Flight Path (GPS) - N42, Sept 03, 2010
4.2 4.4 4.6 4.8 5 5.2 5.4 5.6 5.8
x 104
0
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20
30
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60
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UTC "today"(sec)
U10
WS
pd (m
/s)
GPS-Derived Surface Winds - Hurr. Earl, N42 (Sept 03, 2010) (>30 sat-elev)
GPS mdl-filtSFMR0.8Flt-LvlSondes
GPS measurement differences with dropsondes and SFMR generally increased with storm maximum wind speed.
Wind Speed Retrieval Intercomparisons – Summary Statistics
10 15 20 25 30 35 40 45 50 55 60 65
2
4
6
8
10
12
14
16
Max Wspd (ms-1)
RM
S D
ev. (
ms-1
)
RMS Deviations Relative to SFMR vs. Max Wind Speed
data 1 linear
Higher variability and larger range in true wind speed.
10 15 20 25 30 35 40 45 50 55 60 65 70-10
-5
0
5
10
15
20
Max Wspd (ms-1)
Mea
n D
ev. (
ms-1
)
Mean Deviations Relative to SFMR and Dropsondes vs. Max Wind Speed
(Rel. to SFMR)(Rel. to Drops.) Primarily due
to hurricane eyewall.
GPS wspd. absolute mean differences: 0.3 – 7.5 ms-1 (SFMR) 0.1 – 16.3 ms-1 (drops.)
GPS wspd. RMS differences:
2.3 – 11.8 ms-1 (SFMR) 0.4 – 8.9 ms-1 (drops.)
The 18 retrievals (data sets) were categorized in 3 maximum wind speed ranges ( 6 sets per category )
Wind Speed Retrieval Intercomparisons – Summary Statistics, cont’d
Max Wspd
Aggregate GPS Measurement Performance per Max Wind Speed Category (all entries in ms-1)
12 - 20
(vs. SFMR)
28 - 35
40 - 62
0.6 ± 3.6
3.7 ± 9.3
0.4 ± 11.8
(vs. Dropsondes)
Category Differences: Mean ± 1σ †
1.5 ± 4.0
2.7 ± 7.9
7.8 ± 8.9
† σ is the largest RMS diff. in each category
NASA Bistatic GPS instruments on NOAA ‘N42 and ‘N43 a/crft performed well No anomalies, equivalent retrieval quality from each instr.
GPS-derived surface wind speed generally compared well to SFMR and dropsonde measurements.
Best performance (vs. dropsondes) currently over the 0 – 35 ms-1 range: bias (underestimate) less than 3 ms-1 precision better than 4 ms-1 (1σ): 0 – 20 ms-1 precision better than 8 ms-1 (1σ): 0 – 35 ms-1
Measurements with peak winds in 40 – 60+ ms-1 range: bias (underestimate) less than 8 ms-1
precision better than 9 ms-1 (1σ) Significant underestimates primarily of winds in hurricane eyewall
Future Reduce bias in peak wind/eyewall measurements and improve precision for P-3
hurricane/TC missions. GPS wind speed assimilation in intensity forecasting (or other) models
Data product quality control and accuracy, precision reqt’s
Add missions/platforms for wind field mapping of developing storm systems.
Summary, Future Objectives
Acknowledgements
~ For the excellent support ~Thank you !!
Dr. James McFadden Dr. James McFadden (AOC Chief, Programs and Projects)(AOC Chief, Programs and Projects)
Terry Lynch Terry Lynch (Chief, Technical Section)(Chief, Technical Section)
Joe Bosko Joe Bosko (N42RF Science systems)(N42RF Science systems)
Dana NaeherDana Naeher (N43RF Science systems(N43RF Science systems))