A global, 2-hourly atmospheric precipitable A global, 2-hourly atmospheric precipitable water dataset from ground-based GPS water dataset from ground-based GPS

measurements and its scientific measurements and its scientific applicationsapplications

Junhong (June) WangJunhong (June) WangNCAR/EOL/TNCAR/EOL/TIIIIMESMES

Co-Authors: Liangying Zhang (NCAR/EOL)

Acknowledgement: Aiguo Dai, Joel Van Baelen, Teresa Van Hove, Gunnar Elgered, Todd Humphreys, John Braun, Imke Durre and Dennis Shea.

Thanks support from NOAA/OGP

Goals1) To develop an analysis technique to derive atmospheric

precipitable water (PW) using existing IGS tropospheric product (zenith tropospheric delay, ZTD) on a global scale;

2) To apply the technique to global ZTD data from 1997 to present to create a global, 2-hourly PW dataset, and make the dataset available to the public;

3) To use the data for various climate and weather studies:

• To quantify time- and space-dependent biases in global radiosonde humidity records

• To document and understand PW diurnal variations

• To create a corrected global radiosonde PW dataset

How does it work and Why using GPS data?How does it work and Why using GPS data?

• All weather• Continuous measurements• High temporal resolution• High accuracy (~1-2 mm)• Long term stability

Total delay = Ionosphere + dry + wet

Global ZPD data from International GNSS Service (IGS)Global ZPD data from International GNSS Service (IGS)~378 stations, 1997-present, 2-hourly~378 stations, 1997-present, 2-hourly

See Wang et al. (2005, 2006)

Ps from global surface synoptic observations with adjustment

Tm from NCEP/NCAR Reanalysis with horizontal and vertical interpolation

(Wang et al. 2005)

Comparisons with radiosonde, MWR and other data

ZWD = ZPD - ZHD

Output:PW = * ZWD

= f (Tm)

Input:ZPD = ZHD + ZWD

),(2779.2

Hf

PZHD s

m

m

T

T

PW

PW

N

i ii

vi

N

i ii

vi

v

v

m

zT

P

zT

P

dzT

P

dzT

P

T

1 2

1

2

Analysis Technique and Validations

Wang et al. 2006 (JGR, revised) for the technique and the dataset

The role of radiosondes observations in the climatic record is limited, in part, by sensor characteristics that

vary substantially in time and space.

1. Systematic errors

2. Spatial and temporal inhomogeneity

3. Spatial sampling errors

4. Diurnal sampling errors

Co-located GPS and radiosonde stations(< 50 km in distance and < 100 m in elevation)

Total 98%

0

5

10

15

20

25

30

35

40

Vaisal

a RS80

-A

Vaisal

a RS80

-H

Vaisal

a RS90

MRZ/M

ars

SHANG VIZ

IM-M

K3

MEIS

EI

Mod

emGra

w

Pe

rce

nta

ge

of

sta

tio

ns

Comparison results

1. Systematic biases for 10 types of radiosondes

2. Day/night differences of systematic biases

3. Characteristics of systematic biases

4. Temporal inhomogeneity

5. Impacts on long-term changes

6. Diurnal sampling errors

Comparisons of PW(IGRA-GPS 2003/2004 102 stations)

-9

-8

-7

-6

-5

-4

-3

-2

-1

0

1

2

3

4

82397

64500

68906

61967

48698

61641

17607

68816

08023

94294

83378

89611

78016

16144

08579

10184

85469

02527

89571

08221

93986

04202

04202

96996

04018

89571

04018

17062

94975

26422

71924

02591

10200

11722

01415

94998

71801

71701

71913

93112

71917

71957

94610

06447

71926

94578

94866

71082

03808

08495

03882

03882

70350

72403

72403

72403

91765

72202

08001

12843

91592

12425

07110

12374

12374

15420

12374

07145

06181

11952

61998

12374

06260

07761

01004

07145

01004

91938

55591

55591

51463

54511

57494

29572

32540

24959

33393

33345

29634

21824

33631

32150

31736

14015

72393

47122

91212

89532

43295

43295

43128

10238

GPS stations

PW

(m

m I

GR

A-G

PS

)

RS80A RS80HRS90 ShangMRZ/Mars VIZ-typeMeisei IM-MK3Graw Modem

Systematic biases for 10 types of radiosondes

Vaisala

MRZ/Mars

IM-MK3

-8

-7

-6

-5

-4

-3

-2

-1

0

1

2

3

4

82397

64500

61967

68906

61641

08023

17607

83378

68816

94294

08579

08221

94120

10184

89611

02527

16144

89571

85469

96996

78016

04018

93986

04202

04202

04018

89571

17062

02591

10200

94975

11722

71924

01415

71701

94998

71801

93112

71913

06447

71917

71957

94610

94866

71926

71082

26422

03808

08495

03882

03882

70350

72403

91765

72202

72403

12843

91592

12374

12425

07110

15420

12374

12374

07145

06181

11952

12374

06260

61998

07761

91938

01004

01004

07145

55591

55591

51463

54511

57494

29572

32540

24959

33345

31736

33393

29634

21824

33631

32150

72393

47122

91212

14015

89532

43295

43295

43128

10238

station

PW

Dif

fere

nce

(m

m IG

RA

-GP

S)

RS80A-day RS80A-nightRS80H-day RS80H-nightRS90-day RS90-nightShang-day Shang-nightMRZ/Mars-night MRZ/Mars-dayVIZ-type-night VIZ-type-dayMeisei-day Meisei-nightIM-MK3-night IM-MK3-dayGraw-night Graw-day

Day/Night Differences

Vaisala RS90

Characteristics of systematic biases

Temporal inhomogeneity

VIZ RS80H

Impacts on long-term changes

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

VIZ RS80H

Diurnal sampling

errors (%) of twice daily radiosonde

data

DJF

82%

MAM

88%

SON

92%

JJA

92%

U.S.A.: (Dai et al., 2002)

• < 3% for 2/day

• 5-10% for 1/day

Summary

1. Dataset: A global, 10-year, 2-hourly GPS-PW dataset is created for various scientific applications. You are welcome to use it.

2. Systematic biases: systematic biases in three widely-used radiosonde types, including dry biases in Vaisala sondes and wet

biases in MRZ and IM-MK3 radiosondes. 3. Day/night differences: The dry bias in Vaisala sondes has larger

magnitudes during the day than at night, especially for RS90.

4. Characteristics of systematic biases: The dry bias in Vaisala sonde increases with PW. The wet bias does not vary significantly with PW for MRZ, but prevails at PW < 30 mm for IM-MK3.

5. Temporal inhomogeneity and impacts on long-term variations: The radiosonde type change from VIZ to Vaisala at a U.S. station was detected by the time series of PW differences between radiosonde and GPS. Such change would have significant impact on long-term trend estimate.

6. Diurnal sampling errors: Generally within 2% twice daily soundings, but can be large for once a day

Hurricane Pressure and PW

Correlation coefficient of water vapor and station pressure is -0.71Storms with winds of 70 mph have coefficient of -0.76

From John Braun (UCAR/COSMIC)

Connections between water vapor and precipitation (Foster et al. 2000, 2003; Champollion et al. 2004)

Foster et al. 2003