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Atmospheric Humidity Profile Retrieval Algorithms for
Megha-Tropiques SAPHIR:
A Simulation Study & Analysis of AMSU-B Data
B.S. Gohil and A.K. Mathur
Oceanic Sciences Division
Meteorology & Oceanography Group
Space Applications Centre (ISRO)
Ahmedabad – 380015, INDIA
SAPHIR & AMSU-B CHANNEL SPECIFICATIONS
Channel SAPHIR Central #SAPHIR Freq. *AMSU-B Freq.
No. Freq. (GHz) used (GHz) (GHz)
1 183.310.15 183.16 ----
2 183.311.20 182.11 182.31 (B5)
3 183.312.80 180.51 180.31 (B4)
4 183.314.30 179.01 ----
5 183.316.60 176.71 176.31 (B3)
6 183.3111.00 172.31 ----
*Other channels
#Earlierversion
are 89 & 150 GHz
Latest specifications have minor deviations
EMISSION OF MICROWAVE RADIATION
RADIATIVE TRANSFER MODEL
Total brightness temperature radiated by a non-scattering earth-
atmospheric system under LTE
TB( , , p) Ts. ( , , p). ( , ) TBup( , ) TBdn(( , ).{ ( , , p)}. ( , )
1
( , ) exp{ sec ( , z )z}
0
= atmospheric absorption
TBdn( , ) sec T ( z ). ( , z ). ( , ,0 z )z = surface emissivity
0
= atm. transmittance
TBup( , ) sec T ( z ). ( , z ). ( , , z )z = incidence angle
0 = frequency
z2
( , , z1 z 2) exp{ sec ( , z )z} Tbup = up-welling BT
z1 TBdn = down-welling BT
Ts = surface temperature
p = polarization
RADIATIVE TRANSFER MODEL (CONT…)
At microwave sounding channels, total transmittance is mostly negligible
yielding total brightness temperature as
TB( , ) TBup( , )
TBup( , ) sec T ( z ). ( , z ). ( , , z )z
0
TBup( , ) sec T ( z ).w( , , z ).z
0
w( , , z ) ( , z ). ( , , z )
c / 2 w=weighting function
w( , , z ) w( f , , z )f
c / 2
=channel bandwidth
c=channel central freq.
SAPHIR CHANNELS’ RESPONSE (NADIR VIEW)
Dry Atmosphere Moist Atmosphere
At nadir view with dry atmosphere, the low freq channels are
contaminated by surface contributions
SAPHIR CHANNELS’ RESPONSE (OBLIQUE VIEW)
Dry Atmosphere Moist Atmosphere
At oblique view with moist atmosphere, the low freq channels are
less sensitive to boundary layer humidity which contributes the most
to many meteorological & oceanographic processes
Status/Conclusions of Previous Studies…(1)
• Studied the impact of humidity, temperature, observational
geometry & frequency of SAPHIR on weighting functions
• Large changes in altitude/pressure of weighting function
peaks due to changes in humidity & temperature suggest
that it is better to infer layer integrated moisture than the
level moisture
• Effective thickness of layers must be chosen by
considering the largest variation in the weighting function
peak altitudes of channels
Status/Conclusions of Previous Studies….(2)
• Developed algorithms for retrieving humidity profiles over
land & ocean using layer integrated moisture from SAPHIR
through simulations for clear-sky conditions
• Combined use of layer water vapour from SAPHIR with
total water vapour from MADRAS improves low level
humidity profile over oceans
• Use of EOF technique for retrieving low level humidity
profile specifically over land is suggested in view of non-
availability of total water content from MADRAS over land
and due to surface contaminations in low-freq channels
SIMULATION STUDIES &
RETRIEVALS FROM NOAA-AMSU/B
RELATIVE HUMIDITY PROFILES OF VAISALA RADIOSONDE DATA
Data depicts large
variations in profiles
of relative humidity
MODEL USED FOR SIMULATING RELATIVE HUMIDITY PROFILES
RELATIVE HUMIDITY VARIATIONS
ALTITUDE
INTERVAL (%)
MINIMUM (%) MAXIMUM (%)
OR CASES
SURFACE 10 80 10%
LOWER LAYER -80% OF +80% OF 5 CASES FOR
AT 4 KM SURFACE VALUE SURFACE VALUE TOTAL RANGE
UPPER LAYER -80% OF LOWER +80% OF LOWER 5 CASES FOR
AT 8 KM LAYER VALUE LAYER VALUE TOTAL RANGE
TROPOPAUSE
(TROPICAL) 5 10 5%
(16 KM)
TOP OF THE
FIXED VALUE (0 %)
ATMOSPHERE
STATISTICS OF SIMULATED TROPICAL ATMOSPHERES
Parameter Min Max Mean SD
SST (K) 285.0 305.0 296.1 6.73
WV (g/cm2) 1.00 6.82 2.60 1.33
SW (m/s) 3.0 6.0 4.45 1.50
AMSU-B CHANNELS’ WEIGHTING FUNCTION PEAK VARIATIONS
CHANNEL
LOWER LIMIT UPPER LIMIT
FREQUENCY
(hpa) (hpa)
(GHz)
176.31 1000 500
180.31 900 400
182.31 750 300
Clear sky cases
RT SIMULATION BASED SELECTION OF LAYERS
USED FOR RETRIEVALS FROM AMSU-B
PRESSURE SL1/L1 L2 L3 L4
1000
900
800
750
700
600
500
400
300
200
100
STATISTICS OF HUMIDITY FOR SELECTED LAYERS
Parameter Min Max Mean SD
LARH-1(%)
13.75 80.00 48.66 16.72
(1000-500 hpa)
LARH-2 (%)
12.33 80.00 46.02 16.82
(900-400 hpa)
LARH-3 (%)
10.50 79.25 41.85 17.83
(750-300 hpa)
LARH-4 (%)
9.57 59.55 29.27 13.22
(500-100 hpa)
Sensitivity of AMSU-B Channel to Humidity in Different Forms
•BT has higher correlation with Layer-WVC at constant temperature as
compared to combined Layer-WVC
•BT better correlated to Layer Average RH as compared to Layer-WVC
Dual Nature of Low Frequency AMSU-B Channel
under Different Atmospheric Conditions
Layer 900 to 700 hpa
285K 290K
Under cold conditions, 176
GHz channel behaves
•Like radiometer indicating
increase in BT with initial
small increase in RH
indicated by +ve slope
•Like sounder depicting
decrease in BT with ALL
further increase in RH 305K
depicted by –ve slope
This has impact on
humidity retrievals
SENSITIVITY OF BT ON LAYER AVERAGED RH WITH
VARIABLE THICKNESS
Sub layer-1: 900 to 700 hpa Layer-1: 1000 to 500 hpa
GIVEN RANGE OF BT YIELDS WIDER RANGE OF RH FOR THICKER LAYER
Response of Mid-Frequency AMSU-B Channel to Cold Atmospheres
Layer 900 to 700 hpa
285K 290K
180 GHz channel does
not show duality even
under cold & dry
conditions
305K ALL
Response of AMSU-B Channels to Different Layers
IMPACT OF DRY CONDITIONS ON HUMIDITY RETRIEVAL
LAYER CORRELATION COEFF. (%) MULTI. RMS
AVERAGED CORR. ERROR
RH (%) 176 GHz 180 GHz 182 GHz (%) loge(RH)
1000-500 -37.4 -80.2 -71.2 84.6 0.252
hpa -79.1 -79.5 -65.8 82.6 0.219
900-400 -42.5 -87.8 -82.4 90.5 0.206
hpa -82.4 -88.8 -79.3 89.1 0.186
750-300 -48.5 -93.4 -93.3 96.1 0.147
hpa -80.3 -94.0 -92.0 95.1 0.149
500-100 -47.2 -86.1 -95.5 95.6 0.140
hpa -67.0 -85.8 -95.9 96.3 0.127
VALUES FOR FULL WVC RANGE VALUES FOR WVC >1.0 g/cm2
Total WVC data useful in better retrievals
Use of separate algorithm for cold-dry conditions suggested
Response of AMSU-B Channels to Humidity in Selected Layers
CH2 CH1
• BT & RH
CH3 dependency in
general is
logarithmic
• Atmospheres
with WVC >1
g/cm2 used for
algorithm
CH3 development
CH1
HUMIDITY PROFILE RETRIEVAL MODEL
exp{A
iN
LARHp 0, p A
i 1
.TBi
i, p }
Where
LARH = Layer averaged relative humidity in percent
A0,p = retrieval constant for pth layer
Ai,p = retrieval coefficient for ith channel
TBi = Brightness temperatures of ith AMSU-B channel
N = Total number of AMSU-B channels used (N=3)
HUMIDITY RETRIEVAL FROM AMSU-B DATA
DATA:
NOAA-16 AMSU-B BT data
REGION:
Indian Region (45° E to 115° E Lon, 0° to 50° N Lat)
PERIOD:
A) June 9, 2002 at 7 & 19 GMT
B) October 22, 2002 at 7 & 20 GMT
LAYER AVERAGED RELATIVE HUMIDITY:
1) 1000-500 hpa
2) 900-400 hpa
3) 750-300 hpa
INTERCOMPARISON:
NOAA CIRES Climate Diagnostics Center Data of RH
At 2.5° Lon-Lat Grid within 2 hours
Comparison of Derived Humidity with Model
090607r1
Comparison of Derived Humidity with Model
090607r2
Comparison of Derived Humidity with Model
090607r3
Comparison of Derived Humidity with Model
090619r1
Comparison of Derived Humidity with Model
090619r2
Comparison of Derived Humidity with Model
090619r3
Comparison of Derived Humidity with Model
221007r1
Comparison of Derived Humidity with Model
221007r2
Comparison of Derived Humidity with Model
221007r3
Comparison of Derived Humidity with Model
221020r1
Comparison of Derived Humidity with Model
221020r2
Comparison of Derived Humidity with Model
221020r3
CONCLUSIONS
Results of the present preliminary study indicate that:
• Layer averaged RH is better represented by BT as compared to
Layer WVC
• Due to wide & overlapping weighting functions of humidity
sounding channels, retrievals using multi-channel data have been
performed
• Low absorbing channel depicts dual dependency on humidity
under cold conditions suggesting the need of moisture/
temperature dependent retrieval algorithms
• Total moisture either to be derived from humidity channels or to be
supplemented externally (from imager data over ocean & land-?)
• Algorithms for deriving level RH from layer averaged RH to be
developed
Detailed studies are in progress for operationalization.
