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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

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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)

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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: junhong@ucar.edu

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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)

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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.

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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)

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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)

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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

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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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