Principles of Sounding RETRIEVAL OF TEMPERATURE AND MOISTURE PROFILE FROM SOUNDER Virendra Singh Scientist-E(Operation) [email protected]
Welcome message from author
This document is posted to help you gain knowledge. Please leave a comment to let me know what you think about it! Share it to your friends and learn new things together.
Transcript
Principles of Sounding RETRIEVAL OF TEMPERATURE AND
MOISTURE PROFILE FROM SOUNDER
Virendra Singh
Scientist-E(Operation)
Atmospheric Sounding
Definition
Why we need sounding
Weather monitoring
NWP input
Climate Applications
Source
Radiosonde/Dropsonde/rocketsonde/GPSsonde
• Limitation :Spatial (land only)/temporal coverage/cost
Alternate source: Satellite sensors
• Advantage: High spatial (global) /temporal resolution
• Disadvantage: Accuracy, vertical resolution
Vaisala: 51% (35% in 1993)
VIZ (Sippican): 9%
Global radiosonde network (WMO report, July 2007)
BASIC PRINCIPLES
The upwelling radiation sensed by a satellite sensor is governed by a) emission from the earth's surface transmitted through the atmosphere and b) emission from the atmospheric layers transmitted through the outer layers of the atmosphere.
Satellite radiometers can “see” in a wide range of electromagnetic spectral intervals. These intervals are called windows, channels or spectral bands. The three most common channels: visible light (0.6 microns), longwave infrared (10 to 12 microns), and a special channel near one of the infrared absorption bands of H20 (6.7 microns) that we call the “water vapor channel.” Polar orbiting satellites provide information about the earth and our atmosphere via the visible, infrared, and microwave regions of the electromagnetic spectrum. The radiation from the earth surfaces,Cloud top and atmospheric constituents are received at satellite through the process of REFLECTION -VIS EMISSION -IR SCATTERING-VIS,MW
The microwave portion of the spectrum covers the range from
approximately 1cm to 1m in wavelength. Because of their long
wavelengths, compared to the visible and infrared, microwaves
have special properties that are important for remote sensing.
Longer wavelength microwave radiation can penetrate through
cloud cover, haze, dust. so microwave radiometers don't need
clear skies to produce images.
Sounding is accomplished with a group of spectral bands
selected to detect radiation emitted from successively lower layers
of the atmosphere.
Irradiance –The radiant flux per unit area
SPECTRAL IRRADIANCE
Transmittance-Energy difference of energy incident and energy absorbed
TRANSMITTANCE
Earth emitted spectra overlaid on Planck function envelopes
CO2
H20
O3
CO2
What Causes Absorption
• Molecules in the Atmosphere (CO2, H2O, O3,
O2, CH4, N2O etc.)
– Vibrational Transition
– Rotational Transition
• For any layer of the atmosphere, molecular
absorption determines the layer emissivity
and transmitivity
ABSORPTION IN INFRARED REGION
Gases IR Mircrowave
CO2 4.3,15um -
H2o 6.3um 22.235Ghz
o3 9.6um -
o2 - 60,118.75Ghz
Satellite Sounding in the infrared region
The infrared region of the spectrum we can use CO2 spectral bands at 15 and 4.3 microns to give us information on the temperature structure of the atmosphere. Information on water vapour content can be gained from a large number of H2O lines between 5 and 8 microns. In addition, parts of the infrared spectrum are sensitive to ozone (8.9-10.1 micron band).
Atmospheric sounding techniques exploit all three phenomena
that play important role in radiative transfer:
absorption,
scattering and
thermal emission.
Most observations are made in the thermal infrared and
microwave bands. At infrared wavelengths scattering due to
atmospheric gases is negligible, hence it is not considered in the
radiative transfer process.
THEORETICAL BACKGROUND
So for vertical sounding at infrared wavelengths the significant terms in the radiative transfer equation (RTE) are absorption and thermal emission. Assumed that there are no clouds in the atmosphere, there is no scattering and earth surface is a black body. The outgoing radiance at frequency v reaching the satellite can be expressed as a sum of two terms: In this eq. The first term on the right hand side is spectral Radiance I0 at frequency v emitted by earth surface, denoted by Z0 and attenuated by the atmosphere. The second term is the Integrated spectral radiance at frequency v emitted by various Layers of the atmosphere and attenuated by the atmosphere.
0
)()}({)()( 00
z
v
vvvv dzdz
zdzTBzIR
vR
Bv is the plank function for frequency v and temperature T at
Hight z, and is the transmittance from level z to the top of the atmosphere.
The simplified form of RTE can be written as
v
0
)()}({)()( 00
z
vvvvv dzzKzTBzIR
vK = weighting function
WEIGHTING FUNCTION
A weighting function is used to multiply or weight the Plank function in the atmospheric component of the emitted radiation. It indicates the relative contribution from a given level of the atmosphere to the radiance received by the satellite through a given channel. Consider three air-parcel at different heights. The radiation emitted to space is determined by three factors: temperature of the air parcel, number density of emitting gas, and transmittance of the atmosphere from the air parcel to space. For the lowest parcel, the atmospheric density is high and so the amount of radiation emitted is high, but most of the radiation is absorbed in the atmosphere above it and very little reaches space.
For the highest parcel, the transmittance to space is high, but
comparatively little radiation is emitted because atmospheric density
decreases exponentially with height. These two contrasting effects
combine in such a way that, at some intermediate height, the
contribution of a parcel to the radiation reaching space is a
maximum. The variation of the radiance to space as a function of
height is shown by the curve .
So the weighting function which is the derivative of the
transmittance profile, will peak higher in the atmosphere for the
frequency at which the absorption is stronger.
0
0
.)(
)].([).0().1(.)0(
)].([)0().(.)0(
s
s
p
p
isss dp
p
pppTBpdp
p
ppTBpTBpI
Upwelling radiance
from surface
Direct atmospheric
emission
Reflected downward
atmospheric emission
Weighting function
1-
0- 1 2 3
P
0 (p0) 1
2
3
1
p
0 wf 1
2
3
1
Radiative Transfer Equation
Atmospheric Sounding
CO2
H20
O3
CO2
Weighting
Function
Earth Emitted Radiance
Spectra Overlaid on
Planck Radiance
Function Envelopes
Bell-Shape of WF
Why WF has a peak at a particular
altitude?
• Lower atmosphere
– Strong radiation is emitted
because of high atmospheric
density
– But, almost all is absorbed by the
upper atmosphere
• Upper atmosphere
– Little radiation is absorbed by the
further upper atmosphere
– But, little radiation is emitted
because of low density.
• As a result, radiation from the
atmosphere at an intermediate
altitude has a maximum
contribution.
Moisture Weighting Functions
Advanced Sounder
(3074) GOES
(18)
1000 1000
100 100
UW/CIMSS
High spectral resolution advanced sounder will have more
and sharper weighting functions compared to current GOES
sounder. Retrievals will have better vertical resolution.
WF do not have sharp peaks, so the radiance can be attributed Only to the mean properties of thick layers not to single levels. Higher degree of overlap.
PROBLEMS FOR SELECTING WEIGHTING FUNCTION
• Once T-profile is
known, WV-profile can
be retrieved using H2O
absorption band
• Similarly for trace gas
concentration, O3 CH4
etc. (Hyperspectral
sounding)
For temperature profile, the source of emission must be abundant gas of known and uniform distribution, e.g. CO2 in IR and O2 in MW.
For uniformly distributed gas, emitted radiance is function of T-profile only
Uncertainty in the gas concentration makes determination of temperature very difficult.
Basic assumptions: uniform distribution of emitting gas, non-overlapping absorption lines, and local thermal equilibrium.
0
0
.)(
)].([).0().1(.)0(
)].([)0().(.)0(
s
s
p
p
isss dp
p
pppTBpdp
p
ppTBpTBpI
Satellite Observing Capabilities Geostationary vs Polar satellites
Low Earth Orbiting:
• Global coverage, Low temporal resolution • High spatial resolution • Hyper spectral IR Sounding (AIRS/IASI/CrIS) • Microwave sounding /GPS
• Active LIDAR for clouds, aerosols, winds, H2O & O3
Geostationary Earth Orbiting:
• High temporal resolution (weather dynamics) • Regional Coverage • Hyper spectral resolution IR sounding (Future) • No MW Sounding • No Polar Sounding
Atmospheric sounding (IR vs MW) Infrared (IR) sounders
Less variable surface emissivity
Higher spatial resolution
Sensitive to clouds => cannot see inside and under clouds
NOAA/HIRS(20ch), GOES/SOUNDER(19ch), INSAT-3D Sounder (19ch)
Hyper-spectral sounders, provide higher spectral resolution data • Higher vertically resolved information can be derived
• Aqua/AIRS(2378ch), Metop/IASI(8461ch), NPP/CrIS
Microwave (MW) sounders
Less sensitive to clouds => much wider observation coverage
Lower horizontal resolution
High variable surface emissivity
NOAA/AMSU-A(15ch), AMSU-B(5ch), MHS(5ch), FY3/MWTS(4~17ch),
MWHS(8ch), DMSP/SSMIS(24ch), MT-SAPHIR (6ch)
ATOVS=HIRS + AMSU-A + AMSU-B (or MHS)
Status of Atmospheric Sounding
Comparison of T and q-profiles accuracy from Multi- and hyper-spectral IR sounder and NWP requirement
• Multispectral sounder still far from desired accuracy for NWP.
• Reasonably accurate sounding of temperature and humidity profiles achieved from polar orbiting hyper spectral IR sounder in clear-sky conditions.
Gap areas • Hyper spectral sounder in Geostationary sounder (GIFTS planned) • High spatial and vertical resolution sounding under cloudy conditions • Microwave sounding from Geostationary satellites
Status of the Atmospheric Sounding
Sounding
system
Satellite/sensors Horizontal
resolution
Vertical
resolution
Accuracy
Multispectral
Sounder
NOAA/ATOVS, GOES/Sounder,
INSAT/Sounder
10 km (IR)
50 km (MW)
~5 km for WV,
~3 km for T
30 % WV,
1-2 K for T
Hyperspectral
Sounder
Aqua/AIRS, Metop/IASI,
NPOESS/CrIS-ATMS,
GOES-R/HES, GIFTS
10 km ~2 km for WV,
~1 km for T
10-20 % for WV,
< 1 K for T
GPS Radio
Occultation
CHAMP, SAC-C, COSMIC,
OS-II, MT
500 km < 1 km 30% for WV,
2 K for T
If we know the vertical profile of the temp and optical
depth of the absorbing gases along with absorption and
emission characteristic of the atmosphere, we can
compute the value of the radiance Rv, this is called the
forward problem.
If we measure Rv at several frequencies in an
absorption band , it should be possible to compute
either the vertical profile of the temp or the optical
depth of the absorbing gases. This is called inverse
problem and it is much more complicated.
So in a given frequencies,
PROCEDURE OF RETRIEVING PROFILE
Inverse problem
• Ill-posed (underconstrained) : Finite measurements, unknown a
continuous function atmospheric profile is discritised at N-vertical
levels,
• Ill-conditioned : Broad and
overlapping weighting functions
combined with errors in the radiance
measurements can be greatly
amplified, making solution
meaningless Required first guess
information
Inverse problem is to find one that is reasonable
and, if possible, to find the profile which is best or
most reasonable in some sense. In addition, the
measurements always contain some error or "noise“,
so we must find a method of solution that does not
amplify the noise to an unacceptable degree.
This implies that we need information additional to
the measurements in order to reach a solution. For
atmospheric remote sensing, additional information is
available in the form of numerical model forecast and
surface observation analysis.
There are several approaches for solution of this problem:
1. Physical retrieval
2. Statistical retrievals and
3. Hybrid retrieval
PHYSICAL RETRIEVAL
In a physical retrieval, the forward problem is exploited in an iterative procedure:
1. A first-guess temperature profile is chosen.
2. The weighting functions are calculated.
3. The forward problem is solved to yield estimates of the radiance in each channel.
4. If computed radiances match the observed ones (within noise) then the current profile is accepted as the solution.
5. If convergence is not achieved, the current profile is adjusted.
6. Steps 3-5 are repeated until a solution is found.
DRAWBACKS
Method is computationally intensive. Requires an accurate knowledge about the transmittance. For the microwave bands, microwave surface emissivity is required as input parameter. Which has significant effect on the calculated brightness temperatures and is very difficult to obtain accurately.
STATISTICAL RETRIEVALS
In statistical retrieval, a training dataset, comprising
radio-sonde observations that are nearly coincident in
time and space with satellite soundings are compiled. A
relationship between the observed radiances and
atmospheric profiles is established. In this approach
physical processes are embedded in the statistics.
Advantages of this approach are (1) the actual
retrievals are computationally simple (does not use
RTE) and (2) it requires no knowledge of the
transmittances. It only uses the statistical properties of
the atmosphere. The disadvantage is.
A large training data set is required and need to be periodically updated. As satellite viewing geometry may vary during different passes over the same area, so limb correction has to be applied.
Lacks the capability of retrieving temperature profiles in extreme cases and fails to address non-liner problems.
HYBRID RETRIEVAL
Hybrid retrieval methods are in between the first two approaches. They are much like statistical retrievals, but they do not require a large training dataset. They use weighting functions like physical retrievals, but they do not directly involve integration of the RTE.
Hybrid methods are easier to apply than the statistical or physical methods. They require knowledge of the transmittances, and they employ statistical knowledge of the atmosphere.
To summarize, there is no unique solution for the detailed vertical
profile of temperature or an absorbing constituent because:
a. The outgoing radiances arise from relatively deep layers of the
atmosphere,
b. The radiances observed within various spectral channels come
from overlapping layers of the atmosphere, and As a consequence,
there are a large number of analytical approaches to the profile
retrieval problem. The approaches differ both in the procedure for
solving the set of spectrally independent radiative transfer
equations (e.g. matrix inversion, numerical iteration) and in the
type of ancillary data used to constrain the solution to insure a
meteorologically meaningful result.
Some of the analytical approaches are:
GA, RBF, GRNN
Infrared Sounding
The road to improved
IR soundingMultispectral (broad band)
To
Hyperspectral (high
resolution)
VAS (geo experimental)
GOES Sounder (geo operational)
GIFTS (geo experimental)
(12)
(18)
(~1600)
(~1600)
HES (geo operational)
Time
(# of spectral bands)
VTPR, HIRS (leo operational)
CrIS (leo operational)
IASI (leo operational)
AIRS (leo pseudo-operational)
HIS (airborne experimental)
IRIS (leo experimental)
(2378)
(~8400)
IMG (leo experimental)
NAST-I (airborne experimental)
2010
INSAT-3D Sounder Channels Characteristics
Ch c
(m)
m)
Principal
absorbing
gas
Purpose
1 14.71 0.281 CO2 Stratosphere temperature
2 14.37 0.268 CO2 Tropopause temperature
3 14.06 0.256 CO2 Upper-level temperature
4 13.64 0.298 CO2 Mid-level temperature
5 13.37 0.286 CO2 Low-level temperature
6 12.66 0.481 water vapor Total precipitable water
Long w
ave
7 12.02 0.723 water vapor Surface temp., moisture
8 11.03 0.608 window Surface temperature
9 9.71 0.235 ozone Total ozone
10 7.43 0.304 water vapor Low-level moisture
11 7.02 0.394 water vapor Mid-level moisture Mid
wav
e
12 6.51 0.255 water vapor Upper-level moisture
13 4.57 0.048 N2O Low-level temperature
14 4.52 0.047 N2O Mid-level temperature
15 4.45 0.0456 CO2 Upper-level temperature
16 4.13 0.0683 CO2 Boundary-level temp.
17 3.98 0.0663 window Surface temperature
Short
wav
e
18 3.74 0.140 window Surface temp., moisture
Vis 19 0.695 0.05 visible Cloud
Atmospheric profiles of temperature and trace gases such as water vapor,
ozone, CO, CH4 etc can be retrieved from satellite observations in high
spectral resolution channels in the absorption bands of various gases.
INSAT-3D Sounder
Meteorological Parameters
Vertical Profiles of:
Temperature
Humidity
Ozone
Surface Skin Temperature
Total Ozone
Derived Products
* Geopotential height
* Layer and total precipitable water
* Lifted index
* Dry microburst index
* Maximum vertical theta-e differential
* Wind index
INSAT-3D Sounder SRF overlaid on IASI spectrum for Tropical Atmosphere
INSAT-3D Sounder FM-SRF
200
220
240
260
280
300
9 10 11 12 13 14 15
Wavelength (Micron)
Brigh
tness T
em
pe
ratu
re
(K)
INSAT-3D Sounder FM-SRF
200
220
240
260
280
300
5 6 7 8 9
Wavelength (Micron)
Brigh
tness T
em
pe
ratu
re
(K)
INSAT-3D Sounder FM-SRF
200
220
240
260
280
300
3.5 4 4.5 5
Wavelength (Micron)
Brig
htn
ess T
em
pe
ratu
re
(K)
INSAT-3D Sounder Channels Characteristics
Ch c
(m)
m)
Principal
absorbing
gas
Purpose
1 14.71 0.281 CO2 Stratosphere temperature
2 14.37 0.268 CO2 Tropopause temperature
3 14.06 0.256 CO2 Upper-level temperature
4 13.64 0.298 CO2 Mid-level temperature
5 13.37 0.286 CO2 Low-level temperature
6 12.66 0.481 water vapor Total precipitable water
Long
wav
e
7 12.02 0.723 water vapor Surface temp., moisture
8 11.03 0.608 window Surface temperature
9 9.71 0.235 ozone Total ozone
10 7.43 0.304 water vapor Low-level moisture
11 7.02 0.394 water vapor Mid-level moisture Mid
wave
12 6.51 0.255 water vapor Upper-level moisture
13 4.57 0.048 N2O Low-level temperature
14 4.52 0.047 N2O Mid-level temperature
15 4.45 0.0456 CO2 Upper-level temperature
16 4.13 0.0683 CO2 Boundary-level temp.
17 3.98 0.0663 window Surface temperature
Short
wav
e
18 3.74 0.140 window Surface temp., moisture
Vis 19 0.695 0.05 visible Cloud
Observation zenith angle INSAT-3D, Sub-
satellite point at 82E)
6400 km x 6400 km scan takes 180 minutes
INSAT-3D Sounder mode of Observations
A: 6 x 5 = 30 Frames x 1.8 minutes = 54 min
B: 4 x 7 = 28 Frames x 1.8 minutes = 51 min
Sounder Scan Schedule (6 Hour cycle): 00:00-00.54Z : Region A
01:00-01.54Z : Region A
02:00-02.54Z : Region A
03:00-03.54Z : Region A
04:00-04.51Z : Region B
05:00-05.54Z: Region A
.........repeat above cycle
64 x 64 pixel scan takes 1.80 minutes
PROFILE RETRIEVALS FROM GOES AND INSAT-3D
INSAT-3D will carry an 18-channel infrared Sounder (plus a
visible channel) along with a 6 channel Imager. The algorithm
is designed for retrieving vertical profiles of atmospheric
temperature and moisture along with total column ozone
content in the atmosphere from clear sky infrared radiances in
different absorption bands observed through INSAT-3D.
INSAT-3D Sounder channels are similar to those in GOES-8
Sounder and many of the spectral bands are similar to High
resolution Infrared Radiation Sounder (HIRS) onboard NOAA-
ATOVS.
Present algorithm for INSAT-3D Sounder is adapted from the
operational HIRS and GOES algorithms developed by
Cooperative Institute for Meteorological Satellite Studies
(CIMSS), University of Wisconsin.
In GOES-8, selection of spectral bands in and around the
CO2 and H20 absorbing bands is designed to yield information
about the vertical structure of atmospheric temperature and
moisture.
INSAT-3D Sounder observations will provide vertical profiles
of temperature and humidity in clear-sky conditions besides
total column ozone and various other derived products.
Atmospheric profile retrieval algorithm for INSAT-3D
Sounder is a two-step approach. The first step includes
generation of accurate hybrid first guess profiles using
combination of statistical regression retrieved profiles and
model forecast profiles.(LAM or MM5)
INSAT-3D SOUNDER
The second step is nonlinear physical retrieval to
improve the resulting first guess profile using
Newtonian iterative method.
The retrievals will be performed using clear sky
radiances measured by Sounder within a 5x5 field of
view (approximately 50 km resolution) over land and
ocean for both day and night.
Four sets of regression coefficients will be
generated. Two sets for land and ocean daytime
conditions and the other two sets for land and ocean
night-time conditions using a training data set
(Radiosonde)
INSAT-3D Sounder retrieval scheme involves two-step
approach. In first step, first-guess temperature and humidity
profiles are derived based on regression retrieval combined
with model forecast. (Hybrid)
In the second step, accurate temperature and humidity
profiles are retrieved based on physical retrieval procedure
that uses non-linear Newtonian iterative method to adjust
first guess profiles.
The methodology which will be used in the development of
retrieval algorithm for atmospheric profiles from INSAT-3D
Sounder measurements.
PROCEDURE ADOPTED BY SAC
1.Generation of hybrid first-guess profiles using weighted
average of regression retrieval and forecast profiles.
2. First-guess profile is adjusted to match the observed
radiances in an iterative procedure in physical retrieval
routine.
IMPLEMENTATION
• Development of fast forward radiative transfer model to
compute Sounder channels radiances given atmospheric state
as input.
• Identification of cloud free pixels.
• Derivation of first-guess atmospheric profiles of temperature,
humidity and ozone, and surface skin temperature from Sounder
radiances using statically regression.
• Retrieval of final profiles through physical retrieval routine,
which iteratively adjusts first-guess profiles to match the
observed radiances.
FAST FORWARD RADIATIVE TRANSFER MODEL:
Remote sensing of atmospheric profiles from satellite is
critically dependent on our ability to calculate observed
radiances as a function of the atmospheric state. This “forward
problem” is the heart of the physically based retrieval
algorithms.
The high rate of satellite observations requires a forward
model fast enough to keep pace with the observations. Though
line-by-line models exist to accurately compute atmospheric
transmittances, they are far too slow to be practical.
Thus fast atmospheric transmittance models are required for
operational atmospheric sounding using physical methods.
For INSAT-3D Sounder channels , Pressure Layer Optical
Depth (PLOD) model, (used in AIRS) also known as pressure
layer fast algorithm for atmospheric transmittances (PFAAST),
developed by Hanon et al (1996) will be used.
Hannon, S., L. L. Strow, and W. W. McMillan, 1996:
Atmospheric Infrared Fast Transmittance Models: A Comparison
of Two Approaches. Proceeding of SPIE conference 2830,
Optical Spectroscopic Techniques and Instrumentation for
Atmospheric and Space Research II.
CLOUD DETECTION:
Atmospheric profiles can be retrieved only over cloud free
observations, it is essential to detect the cloudy pixels. For
each sounder pixel, a cloud detection algorithm is to be
applied to get the clear/cloudy index. Various cloud-detection
schemes are developed and implemented for infrared
sounders (Smith et al. 1979; McMillen and Dean 1982;
Hayden 1988). Each pixel undergoes several tests for
clear/cloudy identifications:
• During daytime if visible channel#19 count is greater then a
threshold, then the pixel is considered cloudy.
• If longwave window channel (channel#8, 11.0μm) brightness
temperature is too cold (< 250) then the pixel is classified
cloudy.
• If the longwave window brightness temperature is 4 K cooler
than that of the warmest pixel from 8 adjacent pixels, then this
pixel is classified as cloudy.
Sounder channel brightness temperatures are simulated for
each pixel using numerical model forecast profiles. If difference
between simulated and observed sounder brightness
temperature for cloud sensitive channels (3, 4, 5, 8, 13, 14, 15)
is large (threshold decided by the forward model error and
forecast error), then this pixel is classified as cloudy.
ECMWF model analysis is essential.
REGRESSION RETRIEVAL:
A computationally efficient method for determining temperature
and moisture profiles from satellite sounding measurements uses
previously determined statistical relationships between observed
(or modeled) radiances and the corresponding atmospheric
profiles.
This method is often used to generate a first-guess for a
physical retrieval algorithm, as is done in the International TOVS
Processing Package and International ATOVS Processing
Package (IAPP, Li et al, 2000). The statistical regression algorithm
for atmospheric temperature is described in detail in Smith et. al.
(1970)
Regression coefficients are required to correlate observed
radiances with vertical profile vectors. This derivation of
regression coefficients in turn requires generation of training
dataset from radiosonde profiles( use TIGR database) of
temperature, humidity, ozone profiles, surface pressure
representing the entire variations.
Emissivity and surface pressure are also used as predictors
along with the brightness temperature observations to improve the
retrieval accuracy.
Separate regression coefficients are generated for land and sea
for day and night conditions.
Surface emissivity's at each sounder channelwavelength for all
profiles are assigned from the global emissivity dataset.
0.1
1
10
100
1000
175 195 215 235 255 275 295
Temperature
Pre
ss
ure
(h
Pa
)
-100 -75 -50 -25 0 25 C
15 km
30 km
50 km
70 km
Temperature Profile
0
100
200
300
400
500
600
700
800
900
1000
0 5 10 15 20 25
Specific Humidity (g/kg)
Pre
ssu
re (
mb
)
15 km
Humidity (water vapor) Profile
Inverse problem is:
• Ill-posed (underconstrained) : finite measurements, unknown a
continuous function atmospheric profile is discritised at N-
vertical levels,
• Ill-conditioned : errors in the radiance measurements can be greatly
amplified, making solution meaningless
To overcome the first problem the atmospheric
profile is descritized at N levels, so that the
inverse problem becomes well posed and
theoretically an exact solution becomes
possible.
However, RTE is still ill-conditioned due to the
measurement errors. To overcome this problem
a priori information is required, e.g. first guess
profiles from forecast or linear regression.
INSAT-3D Retrieval Algorithm
Hybrid First Guess (X0) X0 = Wfcst.Xfcst + Wreg.Xreg
Compute radiances: y=F(Xn)
(For first iteration X1 = X0)
Compare Calculate residual
Sounder Radiances (FOR: 5x5, 3x3, or single pixel)
Cloud detection/clearing
Radiance Bias Correction
Rad Tb (Y)
Exit Physical retrieval fail
Regression as final retrieval
Iteration < 8
Residual > Threshold
Iterate with new profile
Iteration = 8
Residual > Threshold
Residual < Threshold
Output (X) Physical retrieval successful
Xn+1 = X0 + (KnT E-1 Kn + I)-1.
{KnT E-1 [Yn
m +
Kn (Xn – X0)] +
(Xn – X0)}
Cloudy FOR
Exit
clear pixels
< 25 %
Forecast profile (Xfcst)
Regression Retrieval
Xreg= Xmean+RC.(dY)T
nch
k
nkkn nchxyYr1
2
1
2
1 /)]([
Average Radiances of
all clear pixels in FOR
clear pixels
> 25 %
(Threshold NET)
Physical Retrieval
Radiative Transfer simulation Y = F(X) +
RC = dX.dYT.(dY.dYT)-1
(K=YX)
(Li et al, 2000, JAM; Ma et al, 1997, JAM)
Hybrid First Guess (X0) X0 = best of (Xfcst and Xreg)
nch
k
nkkn nchxyYr1
2
1
2
1 /)]([
Temperature Profile (K) 14 MAY 2014 0800 UTC)
850 hPa 700 hPa
500 hPa 250 hPa 50 hPa
950 hPa
Humidity Profile (g/kg) (27 APR 2014 0600 UTC)
850 hPa 700 hPa
500 hPa 250 hPa 100 hPa
950 hPa
Geopotential Height
Calculate layer thickness between pressure levels starting from surface.
)ln(P
P
g
TRZ svd
ZR
ZRH
o
o
Ps – Surface Pressure, P – Given pressure where geopotential height is to be calculated,
Ro = 6356.766 km, the average radius of the earth
S.No. Parameter Data Input
1. Temperature, Humidity
profile and Ozone Brightness temperatures for 18 Sounder Channel
and grey count for channel 19
2. Geo-potential Height Sounder retrieved temperature and humidity
profiles at 40 pressure levels
3. Layer Perceptible Water Retrieved humidity at standard pressure levels
4. Total Perceptible Water Retrieved humidity at standard pressure levels
5. Lifted Index Sounder retrieved temperature and humidity
profiles at standard pressure levels
6. Dry Microburst Index Sounder retrieved temperature and humidity
profiles at standard pressure levels
7. Maximum Vertical Theta-E
Differential Sounder retrieved temperature and humidity
profiles at standard pressure levels
8. Wind Index Geo- potential Height and retrieved temperature
and humidity profiles at standard pressure levels
Geophysical parameters from Sounder
Geo-Physical Sounder Parameters/Products: Ozone, Lifted Index,
Layer and total precipitable water Layers (1000-900, 900-700, 700-300))
dpg
qPW
p
p
2
1
Dry microburst index (DMI)
500700 )()( dd TTTTDMI
Dry microburst occurs in situations characterized by high convective cloud bases and strong
evaporation cooling in the sub-cloud layer, resulting in little or no precipitation at the surface. Such
conditions occur in mountainous and high plain regions.
Wind Index
Wind index provide guidance on the maximum possible wind gusts that can occur with
given atmospheric conditions, if convection were to occur. This is useful for generating
short-range warnings and forecasts.
The equivalent potential temperature (e) is a measure of the total static energy (sensible heat,
latent heat and geopotential) in an atmospheric column. Due to its strong dependence on
moisture, e decreases rapidly with height above the boundary layer reaching a minimum in the
middle troposphere, then e increases again into the upper troposphere. The maximum vertical
e differential from boundary layer to the middle troposphere is a useful quantity in calculating
microburst potential etc.
Maximum vertical theta-e (e) differential
Product
Specified Accuracy
Achieved Accuracy
T-Profile 1-2 K in troposphere
1-1.5 K in troposphere
Humidity Profile 20-30% < 10% (surf-850) 10-25% (850-200 hPa) 15-25% (< 200 hPa)
Total Ozone
5% Validation Pending
Accuracies and Sensitivity of INSAT-3D Sounder Geophysical Parameters
Related Documents
