Monitoring Water Vapor Variability with Ground-based GPS Measurements:

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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)

Challenge: Large variability

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

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)

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

Vaisala RS92

Validating radiosonde dataBefore correction

After correction

Before correction After correction

Diurnal Signal Feb vs Aug 2009

Braun et al. (2012 MWR)

Lin et al. (2010)

Water Vapor Extremes (Miami, USA)

Hurricane Ernesto (Miami, 8/28-8/31/2006)

240 241 242 243 244

Julian days

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

PW Anomaly in 2010 (GPS v.s. Microwave satellite)

Mears et al. (2010)Mears et al. (2011)

Mears et al. (2011)

PW Anomaly in 2011 (GPS v.s. Radiosonde)

Inter-annual and Long-Term PW Variability

El NinoLa Nina

Mears et al. (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)

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

Long-term trend (before/after reprocessing)

Before

4/17/2011

ZTD differences in 2011 between consistently processed and IGS ZTD

April 2011

Differences of monthly mean PW anomalies (GPS – Radiosonde)

4/17/2011

Challenges for Climate Variability: Incompleteness

2008 Annual PW at 252 stations2008: 560 total; 308 not enough for annual meanContinuous data for 1997-2010: 70

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.

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.

Monitoring Water Vapor Variability with Ground-based GPS Measurements:

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