Traditional and new Radio Occultation Sensors, COST 723

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Traditional and new Radio Occultation Sensors, COST 723
OBS 16: Traditional and new Radio
Occultation Sensors
COST 723 UTLS Summerschool
Cargese, Corsica, Oct. 3-15, 2005
Stefan A. Buehler
Institute of Environmental Physics
University of Bremen
www.sat.uni-bremen.de
Outline
Motivation / Scientific goals
“Classical” GPS radio occultation
Future radio occultation with transmission
measurements
Summary and Outlook
Acknowledgements:
Axel von Engeln
ACE+ Mission Advisory Group (in particular Gottfried
Kirchengast und Tobias Wehr)
Stefan Buehler, COST 723 UTLS Summerschool, Cargese, Oct. 3-15, 2005
2
Outline
Motivation / Scientific goals
“Classical” GPS radio occultation
Future radio occultation with transmission
measurements
Summary and Outlook
Stefan Buehler, COST 723 UTLS Summerschool, Cargese, Oct. 3-15, 2005
3
Temperature Variability and Trends
IPCC 2001:
“The globally averaged
surface temperature is
projected to increase by
~1.4–5.8°C over the period
1990 to 2100.”
(= average increase ~0.1°C
to 0.5°C per decade)
(Figures: IPCC 2001)
Stefan Buehler, COST 723 UTLS Summerschool, Cargese, Oct. 3-15, 2005
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Water Vapour and Climate Change
Hydrological cycle for doubling
CO2, IPCC scenario (simulation)
Water vapour is the most important greenhouse gas.
But radiative forcing and feedbacks associated with water vapour changes
remain disputed and uncertain.
Water vapour abundance is most unknown in the upper troposphere.
Stefan Buehler, COST 723 UTLS Summerschool, Cargese, Oct. 3-15, 2005
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Outline
Motivation / Scientific goals
“Classical” GPS radio occultation
Future radio occultation with transmission
measurements
Summary and Outlook
Stefan Buehler, COST 723 UTLS Summerschool, Cargese, Oct. 3-15, 2005
7
“Classical” Radio Occultation
See posters by M. Borsche and S. Schweitzer
Use GPS satellite signals at 1.6 and 1.2 GHz
Receiver on satellite (CHAMP, GRASS on Metop) or
ground
Measures phase delay (time measurement)
Phase delay related to speed of propagation in
atmosphere
catmosphere = cvacuum / n
Refractive index
Stefan Buehler, COST 723 UTLS Summerschool, Cargese, Oct. 3-15, 2005
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The Huygens-Fresnel Principle
c 1, n 1
c 2, n 2
(Gerthsen-Kneser-Vogel)
Secondary waves at
every point along the
wave front
Wave travels more slowly
in medium
Ray bends
Christiaan Augustin
Huygens
Fresnel
1629-1695 1788-1827
sin
sin
n2
c1
n1
c2
c: speed of light
Stefan Buehler, COST 723 UTLS Summerschool, Cargese, Oct. 3-15, 2005
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Snell´s Law
sin
1
n2
sin
2
n1
n: Refractive index
α1
n1≈1 (e.g., air)
n2>1 (e.g., water)
Willebrord Snel
van Royen
(Snellius)
1580-1626
α
2
Stefan Buehler, COST 723 UTLS Summerschool, Cargese, Oct. 3-15, 2005
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Refraction of Visible Light
Stefan Buehler, COST 723 UTLS Summerschool, Cargese, Oct. 3-15, 2005
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Refractive Index and Refractivity in the
Atmosphere
Refractive index n (surface: 1.000300)
Refractivity N = 106(n − 1) (surface: 300)
Refractivity for low frequencies:
N = 77.6 p T-1 + 3.73 · 105 e T-2
pressure
temperature
water vapor pressure
Atmosphere refraction exponentially decreases with
altitude
Snell’s law gives direction modification of ray
Stefan Buehler, COST 723 UTLS Summerschool, Cargese, Oct. 3-15, 2005
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Ray Bending
Stefan Buehler, COST 723 UTLS Summerschool, Cargese, Oct. 3-15, 2005
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Radio Occultation Geometry
Phase delay (time
measurement)
Bending angle
Refractivity
(Figure courtesy Axel von Engeln and/or
University of Graz)
Stefan Buehler, COST 723 UTLS Summerschool, Cargese, Oct. 3-15, 2005
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Refractivity depends on
pressure, temperature,
and humidity
Influence of humidity negligible in stratosphere
 With assumption of hydrostatic balance sensor can
measure temperature
Stefan Buehler, COST 723 UTLS Summerschool, Cargese, Oct. 3-15, 2005
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CHAMP RO data
over Hawaii
Figure courtesy
Grace Peng <
[email protected]
aero.org >
Humidity and temperature both important in troposphere
 Can (in principle) retrieve humidity if temperature is
known from GCM analysis (or vice versa)
Stefan Buehler, COST 723 UTLS Summerschool, Cargese, Oct. 3-15, 2005
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Ground Based Receivers
Path delay due to dry atmosphere practically
constant (about 2m)
Path delay due to water vapor (about 1/10 of the dry
delay) highly variable
 Can be used to retrieve total column water vapor
Absolute measurement accuracy of about 1 kg/m2
 Very good relative accuracy in tropics, not good
for extremely dry locations
Note: All numbers here are from the top of my head
and very uncertain. Better check for yourself before
using them!
Stefan Buehler, COST 723 UTLS Summerschool, Cargese, Oct. 3-15, 2005
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CHAMP / GRASS
CHAMP is a German managed radio occultation
instrument (GFZ)
Launch: July 15, 2000
Provides measurements of stratospheric
temperature and tropopause altitude.
Mixed T/humidity product in troposphere
Height of planetary boundary layer (by-product from
altitude where signal is lost)
GRASS will be the RO instrument on Metop, with
capabilities broadly similar to Champ
Stefan Buehler, COST 723 UTLS Summerschool, Cargese, Oct. 3-15, 2005
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ECMWF Analysis Cases
GNSS-LEO Performance
Statistics of retrieval results versus requirements
height [km]
height [km]
height [km]
 GPS occultation well demonstrated  GPS/MET, CHAMP, SAC-C
Low-latitude ensemble
Mid-latitude ensemble
High-latitude ensemble
 Important climate parameters with respect to GNSS-LEO are:
 refractivity, geopotential height, dry temperature, humidity (< 5–8 km)
abs. spec. hum. error [g/kg]
abs. spec. hum. error [g/kg]
abs. spec. hum. error [g/kg]
height [km]
height [km]
height [km]
Absolute errors of specific humidity profiles
Dry temperature accuracy from example
Expected 25-year temperature
end-to-end simulation; each average
trends 2001–2025
profile (every 10-deg lat.) involves ~50
(Model: Hamburg ECHAM5 at
simulated individual GNSS-LEO
T42L39 resolution).
occultation profiles sampled by ACE+
constellation within a full summer season
temperature error [K]
temperature error [K]
temperature error [K]
(June-July-August).
Errors of retrieved temperature profiles
Stefan Buehler, COST 723 UTLS Summerschool, Cargese, Oct. 3-15, 2005
31
19
Atmospheric Refractivity
an indicator for climate change
Climate-induced refractivity variations
15 km
Inter-annual refractivity variations
15 km
5 km
5 km
(Figures: Vedel & Stendel, DMI)
Atmospheric refractivity is particularly sensitive to climate change,
similar to the geopotential height of pressure levels
Stefan Buehler, COST 723 UTLS Summerschool, Cargese, Oct. 3-15, 2005
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Outline
Motivation / Scientific goals
“Classical” GPS radio occultation
Future radio occultation with transmission
measurements
Summary and Outlook
Stefan Buehler, COST 723 UTLS Summerschool, Cargese, Oct. 3-15, 2005
21
The Refractive Index
Actually a complex quantity
Real part: Responsible for
phase delay, measured by
GPS systems
Imaginary part: Responsible
for absorption, measured
indirectly by most other
remote sensing techniques
Re(n) spectrally flat for sum of
many lines
 Must measure Im(n) to get
trace gas signatures
Re(n)
Im(n)
1
0
Frequency
Behavior of complex refractive
index near an absorption line
Stefan Buehler, COST 723 UTLS Summerschool, Cargese, Oct. 3-15, 2005
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H2O Spectrum
AMSU-B, UARS-MLS
Take lowest
frequency line
to minimize
impact of
clouds and
precip.
Good for radio occultation
22 GHz line is also used for uplooking passive MW
measurements (see poster by A. Haefele)
Stefan Buehler, COST 723 UTLS Summerschool, Cargese, Oct. 3-15, 2005
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What I will show now is from a concept for a RO
mission involving several small satellites
Mission name: ACE+ (unrelated to Canadian ACE
mission)
Phase A study in 2003/2004
Not selected, but similar proposals in current call
Stefan Buehler, COST 723 UTLS Summerschool, Cargese, Oct. 3-15, 2005
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GPS / Galileo
L-Band Transmitter
Observation Technique
GNSS-LEO Occultation Component
2 ACE+ satellites
at ~800 km:
X/K-Band Transmitter
L-Band Receiver
2 ACE+ satellites
at ~650 km:
X/K-Band Receiver
L-Band Receiver
 Exploits refraction of L-band signals between GPS/Galileo and ACE+
receiving satellites.
 Measurements of phase delay  bending angle  real refractivity 
temperature, pressure (> ~8 km). Humidity, temperature, pressure with
a priori information on temperature (< ~8 km).
Stefan Buehler, COST 723 UTLS Summerschool, Cargese, Oct. 3-15, 2005
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GPS / Galileo
L-Band Transmitter
Observation Technique
LEO-LEO Occultation Component
2 ACE+ satellites
at ~800 km:
X/K-Band Transmitter
L-Band Receiver
2 ACE+ satellites
at ~650 km:
X/K-Band Receiver
L-Band Receiver
 Exploits refraction and absorption of X/K-band
signals (~10, 17, 23 GHz at water vapour
absorption line) between transmitting and receiving
satellites (2 pairs  4 satellite constellation).
 Measurements of phase delay & amplitude 
bending angle & transmission  real & imaginary
refractivity  humidity, temperature, pressure
(independently above ~2–6 km).
Stefan Buehler, COST 723 UTLS Summerschool, Cargese, Oct. 3-15, 2005
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Need two
frequencies to
separate humidity
absorption from
cloud absorption
Third frequency
needed for dynamic
range
Stefan Buehler, COST 723 UTLS Summerschool, Cargese, Oct. 3-15, 2005
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Tangent altitude [km]
Different
curves =
different
frequencies
Large dynamic range required for receiver
Stefan Buehler, COST 723 UTLS Summerschool, Cargese, Oct. 3-15, 2005
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 Self-calibration of
both refraction and
transmission
measurements
Stefan Buehler, COST 723 UTLS Summerschool, Cargese, Oct. 3-15, 2005
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Orbits
R1
R2
T1
T2
Example LRO
event timing
 Sun-synchr. orbits at 9:30/21:30 local time
 Inter-satellite phasing: 180o for TX; ~80o or 45o for RX
 Altitudes: ~650 km (RX) and ~800 km (TX)
 TX-RX pointing at each other to use simple and highly
directive antennas
Stefan Buehler, COST 723 UTLS Summerschool, Cargese, Oct. 3-15, 2005
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ACE+ Geographical Coverage
1 day
1 month
LEO-LEO coverage 1 day
LEO-LEO coverage 1 month
(232 occultations = 116 setting [] + 116 rising [])
(7203 occultations = 3601 setting [] + 3602 rising [])
1 day
GPS-LEO + Galileo-LEO coverage 1 day
Coverage:
 LEO-LEO
 ~230 profiles/day
 ~7000 profiles/month
 GNSS-LEO (GPS & Galileo)
 24 GPS & 27 Galileo sats
 ~4500 profiles/day
(4515 occultations = 2240 setting [] + 2275 rising [])
Stefan Buehler, COST 723 UTLS Summerschool, Cargese, Oct. 3-15, 2005
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Satellites
 TX and RX satellites identical except
for LRO electronics
flight
direction
 Modular concept
 Configuration driven by antenna
accommodation and thermal stability
 Star-tracker(s) tightly coupled to LRO
antennas
LRO
antennas
Earth limb
nadir
startracker
GRO
antenna
 Good attitude control performance:
< 0.05o pointing accuracy, < 0.005o
pointing drift over 30 s
 Thermal stability for LRO electronics
(RX: < 0.1o/minute)
 Mass: ~160 kg
 Power consumption: ~250 W
 Data rate: ~250 Mb/orbit
 Dimensions: < 1.3 m  0.8 m  0.7 m
Stefan Buehler, COST 723 UTLS Summerschool, Cargese, Oct. 3-15, 2005
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Geophysical Products
Performance Assessment
Performance Simulator
Model atmosphere
e.g. from climatology
or ECMWF analyses




Temperature,
Humidity,
Pressure,
...
Observation Simulator
Instrument parameters
 Geometry and signal
propagation modelling
 Instrument simulation
ACE+ Level 1b
data products
Comparison
 Doppler shift
profiles
 Bending angle
profiles
 Transmission
profiles
 ...
Retrieved atmosphere




Temperature,
Humidity,
Pressure,
...
Geophysical
Retrieval
Processor
Stefan Buehler, COST 723 UTLS Summerschool, Cargese, Oct. 3-15, 2005
Background profiles
(for retrievals without
absorption meas.)
 Temperature,
 ...
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ECMWF Analysis Cases
Input (“true”) atmosphere for simulation:
 T511L60 ECMWF analysis (12 UTC analysis of Sept. 15, 2002; background shows
integrated liquid water density)
 Sampling of ACE+ LEO-LEO occ. in a day (including every 2nd event)
 Latitude bands of 30º each: low latitude, mid latitude, high latitude
Stefan Buehler, COST 723 UTLS Summerschool, Cargese, Oct. 3-15, 2005
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ECMWF Analysis Cases
Input atmosphere: ECMWF analysis used for retrieval simulation (continued)
specific humidity
temperature
liquid water density
ice water density
Exemplary latitude-height cross sections at 0º longitude through the ECMWF analysis used in
the simulations, indicating the variability of the relevant parameters.
Stefan Buehler, COST 723 UTLS Summerschool, Cargese, Oct. 3-15, 2005
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ECMWF Analysis Cases
Input atmosphere: ECMWF temperature and humidity profiles (all profiles of ensemble)
specific humidity [g/kg]
temperature [K]
specific humidity [g/kg]
height [km]
height [km]
height [km]
specific humidity [g/kg]
High-latitude ensemble
height [km]
Mid-latitude ensemble
height [km]
height [km]
Low-latitude ensemble
temperature [K]
Stefan Buehler, COST 723 UTLS Summerschool, Cargese, Oct. 3-15, 2005
temperature [K]
36
ECMWF Analysis Cases
Statistics of retrieval results versus requirements
height [km]
height [km]
threshold
requirement
Mid-latitude ensemble
target
requirement
abs. spec. hum. error [g/kg]
abs. spec. hum. error [g/kg]
High-latitude ensemble
height [km]
Low-latitude ensemble
abs. spec. hum. error [g/kg]
height [km]
height [km]
[km]
height
height [km]
[km]
height [km]
height
Absolute errors of specific humidity profiles
threshold
requirement
target
requirement
rel.
spec. hum.error
error[K]
[%]
temperature
rel.
spec. hum.error
error[K][%]
temperature
rel. spec. hum.
error
temperature
error
[K] [%]
Relative
Errors of
errors
retrieved
of specific
temperature
humidityprofiles
profiles
Stefan Buehler, COST 723 UTLS Summerschool, Cargese, Oct. 3-15, 2005
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Scintillations
Performance at low altitude is limited by scintillations
(included in simulations shown)
Scintillations = random fluctuations of signal
strength due to small scale refractivity variations
associated with turbulence (also affects GOMOS on
Envisat)
New mission proposal in the current call exploits
pairs of channels at close frequencies to remove
scintillations (use derivative of spectrum, same trick
as in TDL hygrometer)
Stefan Buehler, COST 723 UTLS Summerschool, Cargese, Oct. 3-15, 2005
38
Climate Variability and Trends Measurement Performance
Global mean spec.humidity error, 500 hPa
Specific humidity error [%]
dominated by
LEO-LEO data
(sensitivity at high
altitudes)
dominated by
GNSS-LEO data
(large number of
measurements)
Averaging window [days]
Averaging window [days]
Global mean temperature error, 300 hPa
Global mean temperature error, 500 hPa
Temperature error [K]
Temperature error [K]
Specific humidity error [%]
Global mean spec.humidity error, 300 hPa
Averaging window [days]
Averaging window [days]
Global-mean (ACE+) climatological humidity and temperature accuracy as function
of averaging interval and number of ACE+ satellites.
Dotted lines: Desired climatological accuracy (to be achieved within < 30 days) for
climate variability and trend analysis.
Stefan Buehler, COST 723 UTLS Summerschool, Cargese, Oct. 3-15, 2005
39
Outline
Motivation / Scientific goals
“Classical” GPS radio occultation
Future radio occultation with transmission
measurements
Summary and Outlook
Stefan Buehler, COST 723 UTLS Summerschool, Cargese, Oct. 3-15, 2005
40
Summary
“Classical” GPS RO provides stratospheric temperature with
high absolute accuracy
Refractivity in the troposphere, a priori needed to separate
humidity and temperature
Future RO with phase and amplitude measurement can
provide accurate humidity throughout the free troposphere
Advantages:
Self-calibrating, all-weather, well-understood measurement
Disadvantages:
Sparse sampling with only 4 satellites,
Poor horizontal resolution
Stefan Buehler, COST 723 UTLS Summerschool, Cargese, Oct. 3-15, 2005
41
Outlook
Three (3!) RO transmission missions proposed in
current call:
1: Combined with IR laser transmission measurement
2: Combined with water vapor lidar
3: Using channel pairs to remove scintillations
 Fair chance that one of these goes to phase A.
Stefan Buehler, COST 723 UTLS Summerschool, Cargese, Oct. 3-15, 2005
42
...
Thanks for your
attention.
Questions?
Stefan Buehler, COST 723 UTLS Summerschool, Cargese, Oct. 3-15, 2005
43
ACE+ Mission Requirements
–
Stefan Buehler, COST 723 UTLS Summerschool, Cargese, Oct. 3-15, 2005
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