julien p. nicolas 1 , david h. bromwich 1 , and ian thomas 2

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Validating the moisture predictions of AMPS at McMurdo using ground- based GPS measurements of precipitable water Julien P. Nicolas 1 , David H. Bromwich 1 , and Ian Thomas 2 1 Polar Meteorology Group, Byrd Polar Research Center, The Ohio State University 2 School of Civil Engineering and Geosciences, Newcastle University , UK

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Validating the moisture predictions of AMPS at McMurdo using ground-based GPS measurements of precipitable water. Julien P. Nicolas 1 , David H. Bromwich 1 , and Ian Thomas 2 1 Polar Meteorology Group, Byrd Polar Research Center, The Ohio State University - PowerPoint PPT Presentation

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Page 1: Julien  P. Nicolas 1 , David H. Bromwich 1 ,  and Ian Thomas 2

Validating the moisture predictions of AMPS at McMurdo using ground-

based GPS measurements of precipitable water

Julien P. Nicolas1, David H. Bromwich1, and Ian Thomas2

1Polar Meteorology Group, Byrd Polar Research Center, The Ohio State University2School of Civil Engineering and Geosciences, Newcastle University , UK

Page 2: Julien  P. Nicolas 1 , David H. Bromwich 1 ,  and Ian Thomas 2

Outline

•Motivations•Data & background on precipitable water

from ground-based GPS•Comparison between PW from

observations, AMPS and GPS •Vertical profile of moisture bias in AMPS•Future work•Conclusion

Page 3: Julien  P. Nicolas 1 , David H. Bromwich 1 ,  and Ian Thomas 2

Motivations• Compared to other variables, lower skill of AMPS to predict

low-level moisture and cloud cover.• Importance for USAP operations at McMurdo. Aircraft

landing requires minimum visibility conditions.

• Total precipitable water (PW) can be derived from GPS measurements.

• Provides information on the atmospheric moisture content where no radiosonde observations are available.

• Benefits of assimilating GPS PW data demonstrated in mid-latitudes.

• Vast array of GPS sites recently installed in the Ross Ice Shelf and in West Antarctica.

• Similar study was conducted in 2006, but problems with PW data quality

Page 4: Julien  P. Nicolas 1 , David H. Bromwich 1 ,  and Ian Thomas 2

Antarctic GPS network

http://www.polenet.org

Page 5: Julien  P. Nicolas 1 , David H. Bromwich 1 ,  and Ian Thomas 2
Page 6: Julien  P. Nicolas 1 , David H. Bromwich 1 ,  and Ian Thomas 2

[Parish and Bromwich, 2007]

[Nicolas and Bromwich, 2010]

The Ross Ice Shelf air stream (RAS)The Ross Ice Shelf air stream (RAS)

The “atmospheric corridor”across West Antarctica

The “atmospheric corridor”across West Antarctica

CLOUD COVER PRECIP

2M POT. TEMP 700-hPa WIND & Q

Page 7: Julien  P. Nicolas 1 , David H. Bromwich 1 ,  and Ian Thomas 2

AMPS data•AMPS archived

forecasts for Grid 2 (20 km)

•Forecasts generated with Polar WRF 2.2

•Run in parallel to Polar MM5 (switched off in July 08)

•Period: Jan. 07 – May 08•6-72h forecasts (3 days)

Grid 2

Page 8: Julien  P. Nicolas 1 , David H. Bromwich 1 ,  and Ian Thomas 2

PW from ground-based GPS

•GPS PW data processed and provided by I. Thomas and colleagues

•2-hourly PW data for 8 Antarctic coastal stations for 2007-05/2008

•Thomas et al. (2008) used 12 years of GPS data, 1995-2006, to study changes in atmospheric moisture at 12 Antarctic stations. They found good agreement between GPS PW and radiosonde observations.

Page 9: Julien  P. Nicolas 1 , David H. Bromwich 1 ,  and Ian Thomas 2

PW from ground-based GPS• Delay in GPS signal between

satellite and ground-based GPS receiver due, in part, to the atmospheric moisture content (zenith wet delay)

• Knowledge of the mean atmospheric temperature (Tm) required to derive PW

• Here, Tm is estimated based on the surface temperature [Bevis et al, 1994]:

Tm = 70.2 + 0.72 Ts

• The empirical relationship was derived for mid-latitudes (U.S.) [Bevis et al., 1992]

Page 10: Julien  P. Nicolas 1 , David H. Bromwich 1 ,  and Ian Thomas 2

Deriving the mean tropospheric temperature (Tm)• Tm estimated from the

surface temperature is compared with Tm computed from ERA-40 temperature profiles [Wang et al., 2005]

• Dotted areas: Tm(Ts) > Tm(ERA-40)

ANNUAL JULY

JANUARY

Page 11: Julien  P. Nicolas 1 , David H. Bromwich 1 ,  and Ian Thomas 2

Deriving the mean tropospheric temperature (Tm)• Tm estimated from the

surface temperature is compared with Tm computed from ERA-40 temperature profiles [Wang et al., 2005]

• Dotted areas: Tm(Ts) > Tm(ERA-40)

ANNUAL JULY

JANUARY

Page 12: Julien  P. Nicolas 1 , David H. Bromwich 1 ,  and Ian Thomas 2

Observations•Radiosonde observations

at McMurdo are taken from the IGRA database of the National Climatic Data Center.

•PW values are calculated by vertical integration of the water vapor mixing ratio (q)

Page 13: Julien  P. Nicolas 1 , David H. Bromwich 1 ,  and Ian Thomas 2

AMPS 6-24h fcsts AMPS 30-48h fcsts

AMPS 54-72h fcsts GPS

PW time series: Obs, AMPS, GPS

Page 14: Julien  P. Nicolas 1 , David H. Bromwich 1 ,  and Ian Thomas 2

Observed PW vs AMPS & GPS (Bias)

AMPS 6-24h fcsts AMPS 30-48h fcsts

AMPS 54-72h fcsts GPS

Page 15: Julien  P. Nicolas 1 , David H. Bromwich 1 ,  and Ian Thomas 2

Observed PW vs AMPS & GPS (Bias)

AMPS 6-24h fcsts AMPS 30-48h fcsts

AMPS 54-72h fcsts GPS

Page 16: Julien  P. Nicolas 1 , David H. Bromwich 1 ,  and Ian Thomas 2

-15%

-10%

-5%

0%

5%

10%

15%

10%

15%

20%

25%

30%

35%

40%

45%

Statistics

0

100

200

300

400

500

600

700

800

RMSD Correlation

BiasNumber of data used

GPS AMPS forecasts

12h

48h24h

36h 60h

72h

0.7

0.8

0.8

0.9

0.9

1.0

1.0

00UTC only

Page 17: Julien  P. Nicolas 1 , David H. Bromwich 1 ,  and Ian Thomas 2

Vertical profile of bias in mixing ratio (q)

Page 18: Julien  P. Nicolas 1 , David H. Bromwich 1 ,  and Ian Thomas 2

Vertical profile of bias in mixing ratio (q)

[Fogt and Bromwich, 2008]

AMPS/PMM5 bias in relative humidity wrt. ice (Dec.03-Jan.05)

Page 19: Julien  P. Nicolas 1 , David H. Bromwich 1 ,  and Ian Thomas 2

Case study: 16 Jan. 2007, 0000 UTC

Bias in q for AMPS fcsts Wind and q at 700 hPa

Page 20: Julien  P. Nicolas 1 , David H. Bromwich 1 ,  and Ian Thomas 2

Future work• Better tuning of PW retrievals for polar regions• Compelling reasons for testing GPS data

assimilation in AMPS once GPS data from sites in West Antarctica become available

• The Polar WRF can assimilate:▫ PW retrievals from GPS data▫ Or directly the zenith wet delay (ZWD)• Requires that GPS data be available for

operational assimilation in a timely manner • 3D-Var or 4D-Var? • GPS PW/ZWD assimilation tested for mid-

latitudes, data-dense regions only, NOT for the polar, data-sparse Antarctic environment

• Validation of AMPS PW for other Antarctic locations

Page 21: Julien  P. Nicolas 1 , David H. Bromwich 1 ,  and Ian Thomas 2

Conclusions• Good quality of GPS-

derived PW data (confirms the work from I. Thomas)

• AMPS/Polar WRF compares favorably with PW observations

• But potential for improvement through GPS data assimilation

• Will provide better constraints on moisture fluxes coming from the south

[Monaghan et al., 2005]

Annual precip in the McM region from AMPS

Page 22: Julien  P. Nicolas 1 , David H. Bromwich 1 ,  and Ian Thomas 2

Acknowledgements

•This research is funded by the AMPS Grant from the National Science Foundation, Office of Polar Programs. UCAR Subcontract S01-22961.

Page 23: Julien  P. Nicolas 1 , David H. Bromwich 1 ,  and Ian Thomas 2

Thank you