Atmospheric Boundary Layer
Observations
Radio occultation bending angle and refractivity captures sharp inversions
RO bending
angle captures
altitude of
sharp
inversion
Radiosonde
Note: impact
height ≈ height
above surface +
2 km
Measured and
reconstructed
refractivity
profiles
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)
Xie, F., D. L. Wu, C. O. Ao, E. R. Kursinski, A. J. Mannucci, and S. Syndergaard (2010), Super‐refraction
effects on GPS radio occultation refractivity in marine boundary layers, Geophys. Res. Lett., 37, L11805,
1
doi:10.1029/2010GL043299.
Mannucci/JPL 02-13-12
Atmospheric Boundary Layer Height
Eastern Pacific Stratocumulus Region
Mean height
VOCALS
campaign
region
Height
variability (std)
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.
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, 903–2
918, 2012 doi:10.5194/acp-12-903-2012.
Mannucci/JPL 02-13-12
Atmospheric Boundary Layer Height
Global & Regional Climatology
• 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).
2006-2009
Monthly averages of ABL heights in the Sahara region
ERA – ECMWF-Interim reanalysis
REF – refractivity criterion for height
PWV – water vapor criterion for height
Diurnal variation of ABL heights in the
Sahara region, summer season JJA.
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 to3
Mannucci/JPL 02-13-12
JGR.
New Algorithm to Retrieve
Atmospheric Boundary Layer Moisture
Varying
moisture
lapse
•
•
•
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
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.
4
23(12), pp 1629–1644.
Mannucci/JPL 02-13-12
Madden-Julian Oscillation
Temperature Anomalies
AIRS
Pressure (hPa)
GPS
Composites of MJO temperature anomalies, tropics (10S-10N)
1 January 2006 to 31 December 2010
High resolution GPS data shows similar structures to AIRS but quantitative differences
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
5
Measurements”, in preparation (submission within 1-2 weeks)
Mannucci/JPL 02-13-12
Comparisons Between RO and CMIP5
Model Runs
• 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)
Anomalies
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
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,” to6
Mannucci/JPL 02-13-12
be submitted by July 31, 2012 deadline for consideration in AR5.
Diurnal Tide From RO
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.
F. Xie, D. L. Wu, C. O. Ao, and A. J. Mannucci (2010) “Atmospheric diurnal variations observed with GPS radio
7
occultation soundings”, Atmos. Chem. Phys., 10, 6889–6899, 2010, doi: 10.5194/acp-10-6889-2010
Mannucci/JPL 02-13-12
Stratospheric Drying Events
“Cold events” have a significant impact on large-scale drying of the stratosphere
COSMIC
Temperature
Tropopause/Lower Stratosphere
Aura Water
Vapor
Takashima, H., N. Eguchi, and W. Read (2010), A short‐duration cooling event around the tropical tropopause and its effect on
8
water vapor, Geophys. Res. Lett., 37, L20804, doi:10.1029/2010GL044505.
Mannucci/JPL 02-13-12
Thin Cirrus Case Study
Thin cirrus clouds in the Tropical Tropopause Layer (TTL) and their important
ramifications for radiative transfer, stratospheric humidity, and vertical transport
CALPISO
thin cirrus
observations
COSMIC RO
temperatures
J. R. Taylor, W. J. Randel, and E. J. Jensen, Cirrus cloud-temperature interactions in the tropical tropopause layer: a case
9
study Atmos. Chem. Phys., 11, 10085–10095, 2011, doi:10.5194/acp-11-10085-2011
Mannucci/JPL 02-13-12
Global Tropopause Structure
Global tropopause climatology from COSMIC, and comparisons to NCEP-NCAR
Reanalysis (NNR)
“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.
Son, S.‐W., N. F. Tandon, and L. M. Polvani (2011), The fine‐scale structure of the global tropopause derived from
10
COSMIC GPS radio occultation measurements, J. Geophys. Res., 116, D20113, doi:10.1029/2011JD016030. Mannucci/JPL 02-13-12
Static Stability of the Atmosphere
S-2
Measure the long-term mean structure and variability of the global static stability field in the
stratosphere and upper troposphere.
Annual-mean, zonal-mean static
stability (N2) in (top) conventional
vertical coordinates and (bottom)
tropopause-relative vertical
coordinates. CHAMP RO data
from 2002-2008.
“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 11
Stratosphere and Upper Troposphere, J Clim 23, p. 2275, DOI:10.1175/2009JCLI3369.1
Mannucci/JPL 02-13-12
Air Pollution and Static Stability
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.
Whitt, D. B., M. Z. Jacobson, J. T. Wilkerson, A. D. Naiman, and S. K. Lele (2011), Vertical mixing of commercial aviation
12
emissions from cruise altitude to the surface, J. Geophys. Res., 116, D14109, doi:10.1029/2010JD015532.
Mannucci/JPL 02-13-12
Southern Polar Precipitation Trends
Assessment of Precipitation Changes over Antarctica and the Southern Ocean
since 1989 in Contemporary Global Reanalyses
Precipitation
Net Precipitation
Assimilation of COSMIC data into the ERA-Int and CFSR reanalyses begins in 2006.
These diverge from the other reanalyses at that time.
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 .
13
Mannucci/JPL 02-13-12
Longwave Forcing and Feedback
RO helps determine
these feedbacks
Derived from Huang
et al., Table 2
“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
14
Infrared Spectra and GNSS Radio Occultation, J Clim 23, p. 6027, DOI: 10.1175/2010JCLI3588.1
Mannucci/JPL 02-13-12
Operational Impact
Ron Gelaro, NASA/GMAO
15
From Ector et al. presentation AMS Annual Meeting, January 2012 Mannucci/JPL 02-13-12
Operational Impact
GOES-Rad
MTSAT-Rad
MET 9-Rad
MET 7-Rad
AMSU-B
MHS
AMSR-E
SSMI
GPS-RO
IASI
AIRS
AMSU-A
HIRS
TEMP-m ass
DRIBU-m ass
AIREP-m ass
SYNOP-m ass
SCAT-w ind
MODIS-AMV
MET-AMV
MTSAT-AMV
GOES-AMV
PILOT-w ind
TEMP-w ind
DRIBU-w ind
AIREP-w ind
SYNOP-w ind
Relative FC error reduction per system
0
2
4
6
8
10
12
14
16
18
20
FEC %
GOES-Rad
MTSAT-Rad
MET 9-Rad
MET 7-Rad
AMSU-B
MHS
AMSR-E
SSMI
GPS-RO
IASI
AIRS
AMSU-A
HIRS
TEMP-m ass
DRIBU-m ass
AIREP-m ass
SYNOP-m ass
SCAT-w ind
MODIS-AMV
MET-AMV
MTSAT-AMV
GOES-AMV
PILOT-w ind
TEMP-w ind
DRIBU-w ind
AIREP-w ind
SYNOP-w ind
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.
(C. Cardinali)
Relative FC error reduction per observation
0
5
10
15
20
25
30
FEC per OBS %
Use of Satellite Data at ECMWF
P. Bauer
Ⓒ ECMWF
16
Mannucci/JPL 02-13-12
Hurricane Forecasting Impact (1)
The impact of using RO refractivity observations on analyses and forecasts of
Hurricane Ernesto’s genesis (2006)
Observed
Conventional data assimilated
All available RO data + CTRL data
RO data only above 6 km + CTRL data
Ensemble mean of 48-h forecasts of Ernesto’s central sea level pressure (hPa) initialized
from the analyses at 0000 UTC 25 Aug 2006.
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.
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, 17
v140, p. 151 DOI: 10.1175/MWR-D-11-00024.1
Mannucci/JPL 02-13-12
Hurricane Forecasting Impact (2)
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.
18
Hui Liu, et al. (2012) Monthly Weather Review, v140, p. 151 DOI: 10.1175/MWR-D-11-00024.1 Mannucci/JPL 02-13-12