Transcript Temperature and humidity profile from GPS sounding

Some Observations on retrieval of
geophysical parameters from GPS-RO
derived refractivity
International Conference on Data Assimilation
13-15 July 2011 at CAOS, IISc, Bangalore
D Jagadheesha
Atmospheric Science Programme Office
ISRO Head Quarters, New BEL Road
Bangalore 560 094
Email: [email protected]
Introduction
• Radio occultation sensor can give very good climate
quality data.
• Methods other than 1-d var typically use surface
meteorological parameters to derive humidity
parameters.
• Retrieve geophysical parameters from RO data without
any additional parameters but some observation based
statistics.
• We use refined temperature method (Jagadheesha et al
2009 JAOT) and detected water vapor height (above
which water vapor is practically negligible) in a large
number of radiosonde derived refractivity profiles over
the tropics.
Steps involved in RO data
processing
 Calculation of Bending angle from
‘Excess Doppler’
 Refractivity from Abel transform
 Conversion to atmospheric variables
GPS phase delay
 Phase path
(meters):
(n-refractive index)
 Excess phase
path:
 Phase change :
(Excess Doppler)
LEO
L
 nds
GPS


L  L  rLEO  rGPS
dL
D 
 f  [v t  kˆ t  v r .kˆ r  (v t  v r ). kˆ ]
dt
Bending angle to Refractivity
 Abel transform
(Fjeldbo 1971):
1
n( r0 )  e xp(


a
 ( x)
x a
2
2
dx)
a
r0 
, N ( r0 )  106  ( n( r0 )  1)
n( r0 )
Horizontal Resolution
Precision Estimates
(Anthes et al. BAMS 2008)
Advantages





Very accurate, High vertical resolution.
All weather sounding capability.
Independent of radiosonde or other calibration.
No instrument drift and no satellite-to-satellite bias.
Measurement type is volume average as compared to
‘point’ measurements of conventional radiosonde and
other data making it ideal for comparison and
validation with numerical weather prediction models
and climate studies.
 Retrieval process does not involve the use of ‘initial
guess profile’.
GPS radio occultation missions
Mission
Launch-Duration # Soundings/day Remarks
GPS-MET
4/1995
2+
~125
Proof of Concept
CHAMP
11/2000
~5
~250
Improved receiver,
tracking
SAC-C
11/2000
~3
~300
Improved receiver,
open loop tracking
test
GRACE
5/2002
~5
~500
COSMIC
12/2005
~5
~2500
Real time-ops
TerraSAR-X
7/2005
~5
~400
COSMIC RX &
Antennas
EQUARS
7/2006
~3
~400
COSMIC RX &
CHAMP antennas
Oceansat-2
2008/09
~5
~500
MeghaTropiques
?
~5
~700
Dense
observations in the
tropics
Accuracy Requirements of GPS
occultation receiver.





Accuracy (Minimum Requirement)- Pseudorange <
20 cm, Carrier Phase < 0.05 cm (both ionosphere
free).
Should be capable of pseudorange and carrier phase
measurements at 1 and 50 Hz respectively on L1 and
L2, even with encrypted P(Y) code.
Additional Parameters – Navigation Solution (POD) <
50 cm, Bending angle <1.2 x 10-6 Rad, Doppler < 3.8
mm/sec.
Time Calibration accuracy - < 1 micro sec from GPS
time.
USO Clock stability – Clock drift less than 10-12 over
1-100 s, USO stability <10-11
Refractivity to Geophysical Parameters
• Refractivity (N) profile retrieved from GPS-RO
sounding contains information on temperature (T),
pressure (P) and water vapor partial pressure (e).
• Operationally 1-D variational assimilation technique is
being used to derive T, P and e from N.
• There are techniques which use surface or lower
altitude pressure and temperature information to
derive T, P and e. (e.g., O’Sullivan et al 2000, BAMS;
Jagadheesha et al., 2009, JAOT).
• In light of 1-D var technique highly influenced by
model forecast initial guess, it may be worthwhile to see
how other techniques fare.
The problem
Wet Atmosphere
Two equations and three
unknowns
P
5 e
N  77 .6  3.73  10
T
T2

gmd N 3.73105 gmd e g (md  mw )e
P   [


]dz
2
77.6R
77.6RT
RT
z
Requires one of T or P or e for solution.
Generally T profile is used, as the solution is less sensitive to
reasonably small errors in T.
Dry Atmosphere
Two equations and two unknowns. Practically valid above 10-12
km in humid tropical atmospheres. Generally the hydrostatic
equation is initialized with climatological or reanalysis derived
temperature at altitude of ~60 km.
Typical ‘Dry’ T profile
Water Vapor point
Since e >0, in lower
troposphere
‘dry’ T < Actual T
O’Sullivan method (BAMS, 2000)
Temperature quadratic estimation (Surface T and P known).

z
g
T(σs)=aσs+bσs2+c
(1)
T
(

)
d



dz (3)


R
z
T(σwv)=aσwv+bσwv2+c
(2) 
wv
wv
s
s
T 2 N  77.6 PT
0.622e
e
&w 
5
P
3.73  10
T (1  1.61w )
Tv 
(1  w )
z wv
P ( z )  Pwv exp( 
z
g
dz )
Rd Tv
First iteration starts with ‘dry’ P
Option 1 – Approximate R with gas constant for dry air
Option 2 – Iteratively update R (and hence a,b,c) by updating
water vapor info
Retrieved T, P and e profiles for OS method : A humid case
First iteration
Dry P
Dry T
Symbols represent
Actual values
First iteration
Inferences on O’Sullivan method
• When tested on a large number of diverse radiosonde
profile derived refractivity profiles, the method seem to
give reasonably good RMSE and mean deviation.
• When applied on a sequence of radiosonde data over
Bay of Bengal, it appears to give bias and RMSE in
temperature of ~3 K (Option2) and ~4 K (Option 1).
• Bias and RMSE are not uniform functions of profile
and season.
Empirical method developed (Jagadheesha et al.,
2009, JAOT)
•
•
•
•
Input: Surface P and T, and N profile.
Get ‘dry’ T and P.
Estimate water vapor height.
Get a crude estimate of hydrostatic term (77.6P/T) in the
form of log-linear fit w.r.t height using surface and water
vapor height P and T values.
• Solve the following system of equations to get T, P and e
profiles (first cut retrieval)
P 
zw

z
e( m w  m d )
g N 1md
(

)dz
R 77.6
T
N1
77.6 P
N 2T 2
e
3.73  10 5
T 
• Refined temperature profile -average of linear fit T using
surface and water vapor height and first cut retrieval.
• Using refined temperature and N profile get P and e profiles.
Height (km)
Temperature (K)
Histogram of linear fit, first cut, and refined temperature for
radiosonde data (over 3000 profiles 30S-30N)
REFINED
LINEAR FIT
FIRST-CUT
Water Vapor height detection
• RT method is used to detect height above which
water vapor is assumed to be relatively less (water
vapor height)
• We vary ‘wet’ component of refractivity at
lowermost altitude and detect the three reference
profiles explained below.
• Kdry – Temperature profile which is just greater
than dry temperature.
• Kwet – profile which has relative humidity in the
range 0 to 100%.
• Profile with mean lapse rate between lowermost
altitude and 230K dry T height = 6.5 K/km
Selection of appropriate temperature
profile to compare with dry T profile
• If Kdry > 6.5 K/km and Kwet < Kdry then
actual temperature is closer to Kdry.
• If Kdry < 6.5 K/Km and Kwet < Kdry then
actual temperature is closer to 6.5 K/km
• If Kwet > Kdry and Kwet < 6.5 K/km then
likely humid conditions and actual temperature
is between Kwet and 6.5 K/km.
• Correlation between relative humidity profiles
is also used mainly to avoid false detection of
water vapor point at lower altitudes.
6.5 K/km
Tdry
Kdry
Kwet
Mean = -0.11 K (0.32 hPa)
RMS = 0.85 K (0.47 hPa)
Temperature
5040 profiles over Tropics
(30S to 30N)
Pressure
X-axis Radiosonde Y-axis - Retrieved
Mean = -0.34 K (0.45 hPa)
RMS = 1.12 K (0.77 hPa)
Kdry > 6.5 K/km
Kwet < Kdry
995 profiles
Temperature
Pressure
X-axis Radiosonde Y-axis - Retrieved
Mean = 0.11 K (0.26 hPa)
RMS = 0.70 K (0.33 hPa)
6.5 K/km > Kdry
and Kwet < Kdry
2462 profiles
Pressure
Temperature
X-axis Radiosonde Y-axis - Retrieved
Mean = -0.38K (0.35hPa)
RMS = 0.88 K (0.43 hPa)
Kwet > Kdry and
Kwet < 6.5 K/km
1282 profiles
Temperature
Pressure
X-axis Radiosonde Y-axis - Retrieved
Kdry > 6.5 K/km and Kwet < Kdry
Summary
• Water vapor height has been detected in
diverse radiosonde based refractivity
profiles alone using certain assumptions.
• Temperature and Pressure at water
vapor heights are retrieved to a
reasonable accuracy.
• Future work will focus on retrieving
humidity parameters with this work as
baseline.