climate applications of ground-based global navigation satellite systems (gnss ) measurements
DESCRIPTION
Climate Applications of ground-based Global Navigation Satellite Systems (GNSS ) measurements. Junhong (June) Wang Dept. of Atmospheric and Environmental Sciences University at Albany, SUNY, Albany, NY Earth Observing Laboratory, NCAR, Boulder, CO. Outline. Motivation - PowerPoint PPT PresentationTRANSCRIPT
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Climate Applications of ground-based Global
Navigation Satellite Systems (GNSS) measurements
Junhong (June) WangDept. of Atmospheric and Environmental
SciencesUniversity at Albany, SUNY, Albany, NY
Earth Observing Laboratory, NCAR, Boulder, CO
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Outline
1. Motivation
2.GNSS precipitable water (PW) data
3.Climate applications:
• Validations of radiosonde and other data
• PW Variabilities: Diurnal cycle, annual anomaly, inter-annual variability, long-term trends and extremes
4. Other water cycle products from GNSS & other geodetic techniques
5. Summary
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Gaffen et al. (1995)
Challenge: Large variability
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How does GNSS estimate precipitable water?
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Total delay = Ionosphere + dry + wet
Meteorological data (Ps, Ts) & estimated Tm
Dual-frequency GNSS mea.:GNSS EphemerisStation position
Timing
Atmospheric delay (ZTD = ZHD+ZWD)
Precipitable Water (PW)ZWD = ZTD - ZHD
ZHD = f(Ps)PW = * ZWD = f (Tm)
Forward modelGeodetic Noise to Meteorological signals
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GNSS 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
<~20 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
Why GNSS for Climate Research?
• Diurnal variation• Climate extremes
Validations of other measurements
Climate trends
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GNSS Networks
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GNSS Satellites & Network Expansion
Number of GNSS Satellites
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How good are GNSS measurements?
Orbit Accuracy of GPS Satellites: ~2 cm
1994
2012
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NCAR global, 2-hourly GNSS-PW data (1995-present) • Jan. 1995 to Dec. 2012• 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)
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GNSS-PW Data Highlights
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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
Surf
ace
pres
sure
(hPa
)
PWPs
8/28 8/318/308/29
Hurricane Ernesto (24 Aug – 1 Sep. 2006)
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Radiosonde validation: Systematic errors
Comparisons of PW (IGRA-GPS 1997-2006 106 stations)(only significant ones)
-11-10
-9-8-7-6-5-4-3-2-10123456789
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PW (m
m IG
RA
-GPS
)
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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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
capacitive
capacitive with cover
Rel
ativ
e PW
diff
eren
ces
(% R
adio
sond
e-G
PS)
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Diurnal Variations (Lindenberg)Radiosonde before corr.GNSS
Vaisala RS92
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Radiosonde after corr.
ERA-Interim Japanese Reanalysis
Wang et al. (2013)
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Global PW diurnal anomaly from GNSS
• 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.
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PW Anomaly in 2010 (GPS v.s. Microwave satellite)
Mears et al. (2010)Mears et al. (2011)
Mears et al. (2011)
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Inter-annual and Long-Term PW Variability
El NinoLa Nina
Land
Ocean
Mears et al. (2012) 17
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Land: X Radiosonde; O GPS Ocean: MWR satellites
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Comparisons of Day & Night Trends (GPS)
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Correlation between monthly anomaly PW and Ts
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Variations of PW with Ts, Tmax and Tmin
Clausius-Clapeyron
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PLATTEVILLE (PLTC)Akronar
pt (P044)
ALTERNATE MASTER (AMC2)
SA62
2013 CO Flooding
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Tropical Strom Fay, Aug 17-29, 2008• Fatalities: 13 dir./23 indirect• $560 million• Area affected: Puerto Rico, Dominican Republic, Haiti, Jamaica, Cuba, Bahamas, South Eastern USFay Dumps Record-Breaking Rain; Flood Victims Warned Of Alligator Swimming In Streets Homeowners Forced To Use Canoes POSTED: August 20, 2008
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Tropical Strom Fay, Aug 17-29, 2008
http://xenon.colorado.edu/spotlight/index.php?product=spotlight&station=sg05
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Reflected GPS Signals
http://xenon.colorado.edu/portal/
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2012 AnomalyNMRI: Vegetation Water Content
NDVI: photosynthesis
NLDAS: precipitation PW: Water Vapor
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http://xenon.colorado.edu/portal/index.php
Soil Moisture Variability at Marshall, CO
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DORIS (Doppler Orbitography and Radiopositioning Integrated by Satellite)
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Very Long Baseline Interferometry (VLBI)
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Summary1. The ground-based GNSS PW data have been approved very
useful for studying water vapor diurnal, inter-annual and long-term variations, and extreme events, and play an important role in various climate studies.
2. Other potential applications and other techniques of GNSS measurements to climate should be explored, GPS-RO, 3-D water vapor retrieval, other geodetic techniques.
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http://www.eol.ucar.edu/homes/junhong/homepage.htm
http://xenon.colorado.edu/portal/