Atmospheric Boundary Layer Observations

Xie, F., D. L. Wu, C. O. Ao, E. R. Kursinski, A. J. Mannucci, and S. Syndergaard (2010), Super‐refractioneffects on GPS radio occultation refractivity in marine boundary layers, Geophys. Res. Lett., 37, L11805, doi:10.1029/2010GL043299.

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Radiosonde

RO bending angle captures altitude of sharp inversionNote: impact height ≈ height above surface + 2 km

Measured and reconstructed refractivity profiles

Radio occultation bending angle and refractivity captures sharp inversions

Accuracy of a new ABL reconstruction method

High‐resolution radiosonde (black) sounding in Lihue, Hawaii, USA on 12 UTC 10 December 2006 and the close coincident COSMIC RO sounding (blue)

Mannucci/JPL 02-13-12

Atmospheric Boundary Layer HeightEastern Pacific Stratocumulus Region

F. Xie, D. L. Wu, C. O. Ao, A. J. Mannucci, and E. R. Kursinski (2012) “Advances and limitations of atmospheric boundary layer observations with GPS occultation over southeast Pacific Ocean” Atmos. Chem. Phys., 12, 903–918, 2012 doi:10.5194/acp-12-903-2012.

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Altitude maps (km) of the strong temperature inversion and sharp moisture gradient across the ABL top. RO shows significant ABL deepening in the western edge that is not captured in high-resolution ECMWF analyses (TL799L91). Period: Sept-Nov 2007-2009.

VOCALS campaign region

Mean height

Height variability (std)

Mannucci/JPL 02-13-12

Atmospheric Boundary Layer HeightGlobal & Regional Climatology

Chi O. Ao, Duane E. Waliser, Steven K. Chan, Jui-Lin Li, Baijun Tian, Feiqin Xie, Anthony J. Mannucci (2012) Planetary Boundary Layer Heights from GPS Radio Occultation Refractivity and Humidity Profiles, submitted to JGR.

• Using RO to understand PBL height variations in different global regions.• Gradient methods work well for dry convective boundary layers that develop over

subtropical deserts during daytime (e.g. Sahara).

Monthly averages of ABL heights in the Sahara region

ERA – ECMWF-Interim reanalysisREF – refractivity criterion for heightPWV – water vapor criterion for height

Diurnal variation of ABL heights in the Sahara region, summer season JJA.

2006-2009

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New Algorithm to RetrieveAtmospheric Boundary Layer Moisture

• Strong inversion layers create ill-posed retrievals

• New algorithm recovers set of possible retrievals based on Xie et al., 2006.

• Adding GOES cloud top temperature breaks degeneracy

• Recovered humidity profile detects presence of decoupled layer

• Effective within or beneath heavy cloud cover• Uses GPS and GOES cloud top temperature

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Reference: Xie, F., S. Syndergaard, E. R. Kursinski, and B. M. Herman, 2006. An approach for retrieving marine boundary layer refractivity from GPS occultation data in the presence of superrefraction, J. Atmos. Ocean. Technol. 23(12), pp 1629–1644.

Varying moisture lapse

Mannucci/JPL 02-13-12

Madden-Julian Oscillation Temperature Anomalies

GPS AIRS

Composites of MJO temperature anomalies, tropics (10S-10N) 1 January 2006 to 31 December 2010

Pre

ssur

e (h

Pa)

Baijun Tian, Chi O. Ao, Duane E. Waliser, Eric J. Fetzer, Anthony J. Mannucci, and Joao Teixeira “Intraseasonal Temperature Variability in the Upper Troposphere and Lower Stratosphere from the GPS RO and AIRS Measurements”, in preparation (submission within 1-2 weeks)

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High resolution GPS data shows similar structures to AIRS but quantitative differences

Mannucci/JPL 02-13-12

Comparisons Between RO and CMIP5 Model Runs

C. O. Ao, A. J. Mannucci, J. Jiang, C. Zhai, H. Su, J. Cole, L. Donner, M. Ringer, A. Del Genio (2012) “Geopotential height field comparison between CMIP5 simulations and GPS radio occultation measurements,” to be submitted by July 31, 2012 deadline for consideration in AR5.

• Assessing the CMIP5 model runs against 11 years of RO data• Using new Level-3 gridded RO products – monthly time series• 200 mb pressure surface geopotential height (~average layer temperature)

Larger biases referenced to GPS in coupled models (ocean-atm CNRM, BCC), than in atmosphere-only model (uncoupled, CCCMA)

GPS shows better agreement in anomalies with atmosphere model, less agreement with coupled models

Anomalies

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Diurnal Tide From RO

F. Xie, D. L. Wu, C. O. Ao, and A. J. Mannucci (2010) “Atmospheric diurnal variations observed with GPS radiooccultation soundings”, Atmos. Chem. Phys., 10, 6889–6899, 2010, doi: 10.5194/acp-10-6889-2010 7

Diurnal variations in the stratosphere

Amplitudes of the diurnal temperature tide at 9 hPa (~32 km) as a function of latitude (30S–30 N) and month of COSMIC RO observations from 2007 to 2009. The contour interval is 0.25 K. Gray shading indicates acceptable signal to noise ratio in recovering amplitude of diurnal tide.

Mannucci/JPL 02-13-12

Stratospheric Drying Events

Tropopause/Lower Stratosphere

Takashima, H., N. Eguchi, and W. Read (2010), A short‐duration cooling event around the tropical tropopause and its effect on water vapor, Geophys. Res. Lett., 37, L20804, doi:10.1029/2010GL044505.

“Cold events” have a significant impact on large-scale drying of the stratosphere

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COSMIC Temperature

Aura Water Vapor

Thin Cirrus Case Study

J. R. Taylor, W. J. Randel, and E. J. Jensen, Cirrus cloud-temperature interactions in the tropical tropopause layer: a case study Atmos. Chem. Phys., 11, 10085–10095, 2011, doi:10.5194/acp-11-10085-2011

Thin cirrus clouds in the Tropical Tropopause Layer (TTL) and their important ramifications for radiative transfer, stratospheric humidity, and vertical transport

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CALPISO thin cirrus observations

COSMIC RO temperatures

Global Tropopause Structure

Son, S.‐W., N. F. Tandon, and L. M. Polvani (2011), The fine‐scale structure of the global tropopause derived fromCOSMIC GPS radio occultation measurements, J. Geophys. Res., 116, D20113, doi:10.1029/2011JD016030.

“Although the NNR tropopause data have been widely used in climate studies, they are found to have significant and systematic biases, especially in the subtropics. This suggests that the NNR tropopause data should be treated with great caution in any quantitative studies.” From Son et al., 2011.

Global tropopause climatology from COSMIC, and comparisons to NCEP-NCAR Reanalysis (NNR)

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Static Stability of the AtmosphereMeasure the long-term mean structure and variability of the global static stability field in the

stratosphere and upper troposphere.

“The GPS temperature dataset offers the only global, high vertical resolution measurements of atmospheric temperature: select radiosondes have comparable vertical resolution but cover only a fraction of the globe; other satellite temperature products provide global coverage but have coarse vertical resolution.” Grise et al., 2010

Kevin M. Grise, David W. J. Thompson, And Thomas Birner (2010) A Global Survey of Static Stability in the Stratosphere and Upper Troposphere, J Clim 23, p. 2275, DOI:10.1175/2009JCLI3369.1

Annual-mean, zonal-mean static stability (N2) in (top) conventional vertical coordinates and (bottom)tropopause-relative vertical coordinates. CHAMP RO data from 2002-2008.

S-2

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Air Pollution and Static Stability

Whitt, D. B., M. Z. Jacobson, J. T. Wilkerson, A. D. Naiman, and S. K. Lele (2011), Vertical mixing of commercial aviation emissions from cruise altitude to the surface, J. Geophys. Res., 116, D14109, doi:10.1029/2010JD015532.

Understanding vertical mixing of commercial aviation emissions from cruise altitude to the surface

Jet fuel burned by commercial aircraft in the year 2006 as a function of latitude (degrees) and TR altitude (km). The fuel burn is zonally and annually summed. The dark lines are contours of static stability derived from CHAMP and COSMIC data, zonally and annually averaged.

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Southern Polar Precipitation Trends

David H. Bromwich And Julien P. Nicolas, Andrew J. Monaghan (2011), An Assessment of Precipitation Changes over Antarctica and the Southern Ocean since 1989 in Contemporary Global Reanalyses, J. Clim 24, p. 4189, DOI10.1175/2011JCLI4074.1 .

Assessment of Precipitation Changes over Antarctica and the Southern Oceansince 1989 in Contemporary Global Reanalyses

Assimilation of COSMIC data into the ERA-Int and CFSR reanalyses begins in 2006. These diverge from the other reanalyses at that time.

PrecipitationNet Precipitation

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Longwave Forcing and Feedback

“We have demonstrated that when the two measurements are jointly used to quantify the feedbacks, the additional information provided by the GNSS RO measurement can be critically helpful to improve the accuracy in the results.”

Yi Huang, Stephen S. Leroy, And James G. Anderson (2010) Determining Longwave Forcing and Feedback Using Infrared Spectra and GNSS Radio Occultation, J Clim 23, p. 6027, DOI: 10.1175/2010JCLI3588.1

Derived from Huang et al., Table 2

RO helps determine these feedbacks

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Operational Impact

Ron Gelaro, NASA/GMAO

From Ector et al. presentation AMS Annual Meeting, January 2012 15Mannucci/JPL 02-13-12

Operational Impact

Relative FC error reduction per system

Relative FC error reduction per observation

(C. Cardinali)

The forecast sensitivity (Cardinali, 2009, QJRMS, 135, 239-250) denotes the sensitivity of a forecast error metric (dry energy norm at 24 or 48-hour range) to the observations. The forecast sensitivity is determined by the sensitivity of the forecast error to the initial state, the innovation vector, and the Kalman gain.

Use of Satellite Data at ECMWF P. Bauer ECMWFⒸ 16Mannucci/JPL 02-13-12

Hurricane Forecasting Impact (1)

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Hui Liu, Jeffrey Anderson, And Ying-hwa Kuo (2012) Improved Analyses and Forecasts of Hurricane Ernesto’s Genesis Using Radio Occultation Data in an Ensemble Filter Assimilation System, Monthly Weather Review, v140, p. 151 DOI: 10.1175/MWR-D-11-00024.1

The impact of using RO refractivity observations on analyses and forecasts of Hurricane Ernesto’s genesis (2006)

Ensemble mean of 48-h forecasts of Ernesto’s central sea level pressure (hPa) initialized from the analyses at 0000 UTC 25 Aug 2006.

Observed

RO data only above 6 km + CTRL data

Conventional data assimilatedAll available RO data + CTRL data

Note: CTRL uses radiosonde temperature, winds, and specific humidity, aircraft winds and temperature, satellite cloud drift winds, and surface station pressure observations. Satellite infrared and microwave sounders, radiances, and images are not assimilated.

Mannucci/JPL 02-13-12

Hurricane Forecasting Impact (2)

18Hui Liu, et al. (2012) Monthly Weather Review, v140, p. 151 DOI: 10.1175/MWR-D-11-00024.1

The ensemble mean of the total column cloud liquid water of the 48-h forecasts initialized from the RO, RO above 6-km, and CTRL analyses at 0000 UTC 25 Aug 2006. Units: log(kg kg -1). The observations of the actual storm are from satellite IR cloud images.

Mannucci/JPL 02-13-12