junhong (june) wang ncar/eol/t ii mes
DESCRIPTION
A global, 2-hourly atmospheric precipitable water dataset from ground-based GPS measurements and its scientific applications. Junhong (June) Wang NCAR/EOL/T II MES. Co-Authors: Liangying Zhang (NCAR/EOL) - PowerPoint PPT PresentationTRANSCRIPT
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