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Climate applications of a global, 2-hourly atmospheric precipitable water dataset from IGS tropospheric products. Junhong (June) Wang Earth Observing Laboratory National Center for Atmospheric Research. - PowerPoint PPT Presentation
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IGS Analysis Center Workshop, 4 June 2008IGS Analysis Center Workshop, 4 June 2008
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Climate applications of a global, 2-hourly Climate applications of a global, 2-hourly atmospheric precipitable water dataset atmospheric precipitable water dataset
from IGS tropospheric productsfrom IGS tropospheric products
Junhong (June) WangJunhong (June) WangEarth Observing LaboratoryEarth Observing Laboratory
National Center for Atmospheric ResearchNational Center for Atmospheric Research
Collaborators: Liangying (Liz) Zhang (EOL), Aiguo Dai (CGD), Teresa Van Hove and Ted Iwabuchi (UCAR/COSMIC), and Joel Van Baelen (CNRS)
Thank Support from NOAA Climate Change Data and Detection program
IGS Analysis Center Workshop, 4 June 2008IGS Analysis Center Workshop, 4 June 2008
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OutlineOutline
1.The analysis technique and GPS PW dataset
2.Application #1: Quantifying systematic errors in global radiosonde humidity data
3. Application #2: Diurnal variations
4. Summary
5. Future needs
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Total delay = Ionosphere + dry + wet
ZWD = ZPD - ZHD
PW = * ZWD = f (Tm)
ZPD = ZHD + ZWD
)sf(PZHD
How does GPS estimate precipitable water?
Noise (geodesy)
Signal (meteorology)
IGS Analysis Center Workshop, 4 June 2008IGS Analysis Center Workshop, 4 June 2008
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A global, 11-year, 2-hourly PW dataset from ground-based GPS measurements(Wang et al. 2007, JGR)
• Feb. 1997 to Dec. 2007
• 2 hourly (0100, 0300, …, 2300 UTC)
• 370 IGS, 169 SuomiNet, 1223 GEONET
• Accuracy: < 3 mm
• Ps, Tm, ZHD and ZWD also available
• Request data: [email protected]
IGS Analysis Center Workshop, 4 June 2008IGS Analysis Center Workshop, 4 June 2008
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Highlight of GPS-PW data
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Hurricane Ernesto (Miami, 8/28-8/31/2006)
35
40
45
50
55
60
65
70
75
240 241 242 243 244
Julian days
PW
(m
m)
1005
1006
1007
1008
1009
1010
1011
1012
1013
1014
1015
Su
rfac
e p
ress
ure
(h
Pa)
PW
Ps
8/28 8/318/308/29
Hurricane Ernesto (24 Aug – 1 Sep. 2006)
IGS Analysis Center Workshop, 4 June 2008IGS Analysis Center Workshop, 4 June 2008
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Problems:• Errors and biases
• Spatial and temporal inhomogeneity
• Spatial sampling errors
• Diurnal sampling errors
Results: The role of radiosonde observations in climate studies is limited.
Solutions: To quantify radiosonde errors and correct them.
IGS Analysis Center Workshop, 4 June 2008IGS Analysis Center Workshop, 4 June 2008
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Matched GPS and radiosonde data(< 50 km in distance, < 100 m in elevation, < 2 hours; 14 types and 136 stations)
Humidity sensors:•Capacitive•Carbon hygristor•Goldbeater’s skin
Wang and Zhang (2008a)
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Systematic errors – mean biases
Comparisons of PW (IGRA-GPS 1997-2006 106 stations)(only significant ones)
-11-10
-9-8-7-6-5-4-3-2-10123456789
10
PW
(m
m IG
RA
-GP
S)
RS80A RS80H RS90 RS92Modem Meisei VIZ-type IM-MK3MSS Shang MRZ/Mars
CapacitiveCarbon Hygristor
Goldbeater's skin
1.934.151.72S.D.0.811.97-1.67median
Goldbeater’s skinCarbon hygristorCapacitive
Wang and Zhang (2008a)
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Impacts of the sensor boom cover on Vaisala
RS80 dry bias
with cover
without cover
with cover
without cover
Vaisala RS80-A
Vaisala RS80-HP
W d
iffer
ence
(m
m r
adio
sond
e-G
PS
)P
W d
iffer
ence
(m
m r
adio
sond
e-G
PS
)
Sensor boom cover
Wang et al. (2002)
Wang and Zhang (2008a)
IGS Analysis Center Workshop, 4 June 2008IGS Analysis Center Workshop, 4 June 2008
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Temporal inhomogeneity of radiosonde PW data
Wang and Zhang (2008)
Miami, U.S.A
Suwon-Shi, Korea
Beijing, China
carbon hygristor
carbon hygristor
Goldbeater’s skin Carbon
hygristor
capacitivecapacitive with cover
Rel
ativ
e P
W d
iffe
ren
ces
(% R
adio
son
de-
GP
S)
IGS Analysis Center Workshop, 4 June 2008IGS Analysis Center Workshop, 4 June 2008
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Impacts of temporal inhomogeneity
Miami, Florida
-6
-4
-2
0
2
4
6
8
Jan
-97
Jan
-98
Jan
-99
Jan
-00
Jan
-01
Jan
-02
Jan
-03
Jan
-04
mo
nth
ly a
no
mal
y P
W (
mm
)
RAOB GPS
Carbon hygristor
Capacitive
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PW diurnal variations in four regionsEurope 30-70S
N.H. Mountains Darwin region
Mo
nth
Mo
nth
Mo
nth
Mo
nth
LST
LSTLST
LST
Wang and Zhang (2008b)
mm
IGS Analysis Center Workshop, 4 June 2008IGS Analysis Center Workshop, 4 June 2008
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Seasonal variations of diurnal and sub-monthly variability over EuropeGPS NCEP/NCAR
JRA ERA-40
mmWang and Zhang (2008c)
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Summary
1. Dataset: A global, 11-year, 2-hourly GPS-PW dataset is created from IGS tropospheric products for various scientific applications.
2. Climate applications: The dataset is used to quantify systematic errors in global radiosonde PW data, validate global reanalysis products and study diurnal variations.
3. More information:Wang, J., and L. Zhang, 2008: Validation of Atmospheric Precipitable Water in Three Reanalysis
Products using Ground-based GPS Measurements, extended abstract for Third WCRP International Conference on Reanalysis, Jan. 28 – Feb. 1, 2008, Tokyo, Japan.
Wang, J., and L. Zhang, 2008: Climate applications of a global, 2-hourly atmospheric precipitable water dataset from IGS ground-based GPS measurements, J. of Geodesy, accepted.
Wang, J., and L. Zhang, 2008: Systematic errors in global radiosonde precipitable water data from comparisons with ground-based GPS measurements. J. Climate, in press.
Wang, J., L. Zhang, A. Dai, T. Van Hove and J. Van Baelen, 2007: A near-global, 8-year, 2-hourly atmospheric precipitable water dataset from ground-based GPS measurements, J. Geophys. Res., 112, D11107. doi;10.1029/2006JD007529. .
Wang, J., L. Zhang, and A. Dai, Global estimates of water-vapor-weighted mean temperature of the atmosphere for GPS applications. J. Geophys. Res., 110, D21101, doi:10.1029/2005JD006215, 2005.
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Future NeedsRecommendations on improving future IGS products
1.To continuously produce the ZTD product and maintain its long-term stability and high quality
2.To reduce diurnal biases in the ZTD product
3.To improve and increase sfc-met data
4.To co-locate with radiosonde stations
5.To increase the spatial and temporal coverage
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Fortaleza, Brazil (FORT vs. 82397)
0
5
10
15
20
25
30
35
40
45
50
55
60
Jan-
97
Jul-9
7
Jan-
98
Jul-9
8
Jan-
99
Jul-9
9
Jan-
00
Jul-0
0
Jan-
01
Jul-0
1
Jan-
02
Jul-0
2
Jan-
03
Jul-0
3
Jan-
04
Jul-0
4
Jan-
05
Jul-0
5
Jan-
06
Jul-0
6
Mo
nth
ly m
ean
PW
(m
m)
GPS
IGRA
IGRA-GPS (+20)
1. To maintain long-term stability and high quality of the ZTD product
Fortaleza, Brazil (FORT vs. 82397)
-4
-3
-2
-1
0
1
2
3
4
5
6
7
Jan-
97
Jul-9
7
Jan-
98
Jul-9
8
Jan-
99
Jul-9
9
Jan-
00
Jul-0
0
Jan-
01
Jul-0
1
Jan-
02
Jul-0
2
Jan-
03
Jul-0
3
Jan-
04
Jul-0
4
Jan-
05
Jul-0
5
Jan-
06
Jul-0
6
Mo
nth
ly m
ean
PW
an
om
aly
(mm
)
Linear (GPS 2.16 mm/decade)
Linear (IGRA -7.82 mm/decade)
GPS
IGRA
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4. To co-locate with radiosonde stations
• Provide long-term, high-quality climate records• Constrain/calibrate data from more spatially-comprehensive global
observing systems• Measure large suite of co-related climate variables
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