1UNAVCO Science Workshop 2012
Monitoring Water Vapor Variability with Ground-based
GPS Measurements: Diurnal cycle to long-term trend
Junhong (June) WangEarth Observing Laboratory
National Center for Atmospheric Research, Boulder, CO
Collaborators: Liangying (Liz) Zhang (NCAR/EOL), Aiguo Dai (NCAR/CGD), John Braun & Teresa Van Hove (UCAR/COSMIC), Steve Worley & Zaihua Ji (NCAR/CISL), Tong Ning & Gunnar Elgered (Chalmers University of Technology)
Gaffen et al. (1995)
2UNAVCO Science Workshop 2012
Challenge: Large variability
3UNAVCO Science Workshop 2012
GPS Radiosonde Satellite
Availability all weather Difficulty in thunderstorms
IR: clearMW: ocean
Temporal resolution
High (5 min-2 hourly)
1-2/daily >12-hourly
Temporal coverage
~17 years >50 years ~30 years
Spatial coverage
~1000 stations ~1000 stations globe
Accuracy High (< 3mm) Low, various, bias Low, depending on radiosonde
Long-term stability
Stable Significant temporal inhomogeneity
Significant temporal inhomogeneity
Comparisons of water vapor measurement techniques
• Diurnal variation• Climate extremes
Validations of other measurements
Climate trends
4UNAVCO Science Workshop 2012
NCAR global, 2-hourly GPS-PW data (1995-present) • Jan. 1995 to Dec. 2011• 2 hourly (0100, 0300, …, 2300 UTC)• 380 IGS, 169 SuomiNet, 1223 GEONET
• Accuracy: < 3 mm• Ps, Tm, ZHD and ZWD also
available•
http://dss.ucar.edu/datasets/ds721.1/
Wang et al. (2007)
5UNAVCO Science Workshop 2012
Global PW Diurnal Cycle
• The diurnal cycle is less than 5% of annual mean PW• Larger magnitude in summer than in winter• Peak around late afternoon to early evening • An order of magnitude smaller than seasonal
variation
GlobeS. H.N. H.
Wang & Zhang (2009)
Seasonal variations of diurnal anomalies in four regions Europe 30-70S
N.H. Mountains Darwin region
6UNAVCO Science Workshop 2012
Vaisala RS92
Validating radiosonde dataBefore correction
After correction
Before correction After correction
GPS
7UNAVCO Science Workshop 2012
Diurnal Signal Feb vs Aug 2009
Braun et al. (2012 MWR)
00
18 06
12
Lin et al. (2010)
9UNAVCO Science Workshop 2012
Water Vapor Extremes (Miami, USA)
10UNAVCO Science Workshop 2012
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
Surf
ace
pres
sure
(hPa
)
PWPs
8/28 8/318/308/29
Hurricane Ernesto (24 Aug – 1 Sep. 2006)
Connections between water vapor and precipitation extremes
Foster et al. 2003
• Ka’ū storm, Big Island of HI, Nov. 1-2 2000;
• > 100mm/hr (~4”/hr) maximum hourly rain rate
• Most intense, widespread rain event in 20 years
• $70 M property damage
• Impacts on roads and other infrastructure for weeks afterward
• Using GPS PW data to predict rain rate and validate model results
11UNAVCO Science Workshop 2012
12UNAVCO Science Workshop 2012
PW Anomaly in 2010 (GPS v.s. Microwave satellite)
Mears et al. (2010)Mears et al. (2011)
Mears et al. (2011)
13UNAVCO Science Workshop 2012
PW Anomaly in 2011 (GPS v.s. Radiosonde)
14UNAVCO Science Workshop 2012
Inter-annual and Long-Term PW Variability
El NinoLa Nina
Land
Ocean
Mears et al. (2012)
15UNAVCO Science Workshop 2012
Review on GPS PW Trend StudiesRegions Years Trends Comments
Gradinarsky et al. (2002)
Scandinavia (17)
1993-2000 0.1-0.2 mm/yr Variations with regions & seasons
Jin et al. (2007)
Globe (150) 1994-2006 ~2 mm/d(15 mm/d. ZTD)
Positive in N.H.Negative in S.H.
Nilsson & Elgered (2008)
Finland & Sweden
1996-2006 -0.2 ~ 1 mm/d Uncertainty in trend mainly due to PW natural vari.
summer winter summer winter
Long-Term PW Trend (1995-2011)
16UNAVCO Science Workshop 2012
17UNAVCO Science Workshop 2012
Changes in the IGS ZTD product (1997-2011)Products Period Agencies Comments
2-hrly combined 2/1997-11/2006 GFZ
5-min PPP-IGS00 10/2000 – 11/2006 JPL relative antenna phase model & IGS00
5-min PPP-IGS05 11/2006 – 4/2011 JPL Absolute antenna phase model & IGS05
Reprocessed 5-min PPP
1/1995 – 4/2011 JPL Absolute antenna phase model & IGS05
USNO 5-min PPP 4/2011 - present USNO IGS08 and other changes
NCAR Data Version Period ZTD ProductsV1: non/before-reprocessed
2/1997-12/2010 “2-hrly combined”“5-min PPP-IGS05”
V2: Reprocessed 1/1995 – 12/2011 “Reprocessed 5-min PPP”“USNO 5-min PPP”
2-hrly combined to 5-min PPP IGS05
18UNAVCO Science Workshop 2012
19UNAVCO Science Workshop 2012
Long-term trend (before/after reprocessing)
Before
After
BRUS
WSRT
4/17/2011
ZTD differences in 2011 between consistently processed and IGS ZTD
20UNAVCO Science Workshop 2012
April 2011
Differences of monthly mean PW anomalies (GPS – Radiosonde)
4/17/2011
21UNAVCO Science Workshop 2012
Challenges for Climate Variability: Incompleteness
2008 Annual PW at 252 stations2008: 560 total; 308 not enough for annual meanContinuous data for 1997-2010: 70
22UNAVCO Science Workshop 2012
23UNAVCO Science Workshop 2012
The GCOS Reference Upper Air Network· Provide long-term high-quality
upper-air climate records· Constrain and calibrate data from
more spatially-comprehensive global observing systems
· Fully characterize the properties of the atmospheric column
GRUAN GNSS-PW Task Team:1. To define GRUAN
requirements on GNSS-PW observations, the state-of-art GNSS site, data & meta-data;
2. To provide guidelines for GNSS-PW uncertainty analysis;
3. To provide guidelines on how to better manage changes.
24UNAVCO Science Workshop 2012
Summary1. The GPS PW data have been approved very useful for
studying water vapor diurnal, inter-annual and long-term variations, and extreme events.
2. However, the temporal inhomogeneity of the GPS-PW data is introduced by changes in instruments, data processing algorithms and other factors. This raises concerns on long-term stability of GPS-PW data and its usefulness for water vapor variability.
3. There is a urgent need to consistently reprocess the GPS-PW data for climate studies, and better manage changes in the future, including maintaining complete metadata on changes and always evaluating the impacts of changes before they are implemented.
4. Best efforts should be made to continuously collect data.