ITSC-12
Cloud processing in IASI context
Lydie Lavanant
Météo-France, Centre de Météorologie Spatiale, BP 147, 22300
Lannion Cedex France
Purpose:
Retrieval of cloud information and atmospheric profiles in cloudy
conditions
Steps:
» Test case description
» CO2-slicing method
» Avhrr cloud description in IASI fov
» IASI channel selection in cloudy conditions
» Preliminary results of profile retrieval in cloudy conditions
01/03/02
Test Case
Global IASI orbit simulation. Feb. 1996
13000 situations with:
• simulated IASI cloudy and noisy spectra 0.25 cm-1 (R. Rizzi model)
• Colocated atmospheric profiles:
NWP analyses in T, Q, O on RTIASI levels
Cloud description (cover, CLWV, CIWV) on 31 NWP levels
Dataset provided by Eumetsat (ISSWG)
CO2 slicing method
Ref: Menzel and Stewart 1983, Smith and Frey 1990
[(Rclr – Rmeas)k / (Rclr – Rmeas)ref] – [Nek (Rclr - Rcld)k / Neref(Rclr - Rcld)ref] = fpc
Rmeas: measured radiance
Rclr: clear radiance computed from the colocated forecast
Rcld: black-body radiance at the cloud level n
k= channel from 690 cm-1 to 810 cm-1
Ref= reference channel = 899.75 cm-1
For each channel k: cloud pressure = pressure which minimises equation
P_co2 = S (p_co2(k) w2(k)) / Sw2
W = dfpc / dlnp
Ne = (Rclr – Rmeas)ref / (Rclr - Rcld)ref
Assumption: one thin cloud layer
Rejections: (Rclr – Rmeas) < sqrt(2)*radiometric noise
Ne < 0
Preliminary results of CO2 method using CDS cloudy
spectra
100-200
23
158.4
102.4
200-300
358
47.1
103.3
300-400
396
-9.7
132.7
400-500
658
-31.8
131.7
500-600
802
-51.3
120.2
600-700
913
-97.2
112.3
700-800
1057
-126.6
125.9
800-900
793
-147.6
135.8
900-1000
1534
-129.9
164.0
1000-1050
72
-51.4
71.4
Method:
•
•
•
•
Adapt RTIASI for implementing RTTOV7 cloudy routines
developed by F. Chevallier and al. (2001)
Simulate cloudy noisy IASI spectra Rmeas for all CDS situations
using:
» NPW profiles (T,H2O,.., CC, CLWV, CIWV)
» radiometric noise
Compute clear noisy radiances Rclr for the same fov using:
» RTIASI clear
» noisy NWP profiles (apply forecast errors)
Apply CO2-slicing method
Examples of IASI
cloudy spectra
Variation with the number of channels
1 cloud layer
Ne > 0.3
Variation with emissivity
1 cloud layer
RTIASI cloudy + noise
Profile= analysis
24 channels. resolution:5cm-1
Variation with
emissivity
Several cloud layers
RTIASI cloudy + noise
Profile= analysis
Variation with
emissivity
1 cloud layer
RTIASI cloudy
Profile=forecast
P_Co2, e_Co2
s = 0.2 - 0.25
1 cloud layer
RTIASI cloudy + noise
Profile=forecast
CDS dataset cloud pressure
Cloud top pressure
CO2 retrieved cloud pressure
AVHRR Cloud mask in IASI fov
Operational routine for HIRS fov
(inside AAPP)
Based on a threshold technique
applied
. every AVHRR pixel in sounder fov
. to various combinations of channels
»
» Combinations of channels depend on:
. geographical location of the pixel
. solar illumination and viewing geometry
» Thresholds computed in-line with:
. constant values from experience
. tabulated functions defined off-line
through RTTOV simulations on
climatological data-set
. TWVC retrieved from colocated AMSU-A
Current products:
» percentage clear AVHRR in FOV
» surface temperature from AVHRR
split-window
» black body cloud coverage in FOV
» cloud top temperature for the black
body layer
» clear/cloudy flag for each AVHRR
pixel
Next version:
»Ts, Tcld, Cloud type for each Avhrr
pixel
»-> number of clouds
AVHRR Cloud mask in IASI fov
AVHRR Cloud mask in IASI fov
Validation over Europe
correctly detected
Cloudy targets
Cloud free targets
sea ; day
1774 (99.8%)
584 (87.8%)
sea ; glint
269 (98.8%)
72 (89%)
sea ; twilight
59 ( 98.3%)
12 (90%)
land ; day
995 (99.5%)
638 (80.9%)
land ; twilight
27 (100%)
11 (67.4%)
Comparison of satellite obs.
and Hirs 8 RTTOV6 Tbs using:
March
2001
Clear land
Clear sea
Cloudy
Cloudy sea
land Cv>50 Cv>50
* NWP profile,
N
937
103
136
55
* AVHRR clear cover +Ts,
Bias (K)
0.02
0.64
0.49
0.15
Std (K)
0.99
0.39
0.98
0.65
7007 targets of 5x5 AVHRR
pixels
Noaa12, 14, 15
for 3 years
38 cloud types
Mask comparison with visual
analysis of satellite imagery by
CMS nephanalysts
* AVHRR black-body cloud
cover +Tn
Channels selection and retrieval in clear conditions on CDS
the 300 most informative channels
Clear situations
nbsit= 187 (1/10)
• Rodgers DFS selection
• Guess error matrix = forecast
• Use a mean profile for mid-latitude
conditions
Channels selection above the cloud
Select channels from the 300 most informative channels in clear conditions
Ex: for p_cloud=850 hPa
. uncontaminated channels above the cloud top level: about 65% channels selected
. cloud contaminated channels with (Tbobs – Tbgucld) < 0.3K : about 85% channels
selected
Profile retrieval in cloudy conditions. CDS dataset
un-contaminated channels above the cloud
600 < p_cloud < 700
Nbsit= 132 (1/3)
700 < p_cloud < 800
Nbsit= 146 (1/4)
800 < p_cloud < 900
Nbsit= 138 (1/7)
900 < p_cloud < 1000
Nbsit= 166 (1/5)
Profile retrieval in cloudy conditions with cloud
information as control variable
Cloud control variables: ln(p_cld), e_cld
Cloud guess: CO2 p_cld and e_cld
selected channels: Tbobs – Tbgucld < 0.3K
-> more than 80% channels selected
all P_cld > 800hPa
1807 situations (15% of situations)
before
1d_var
P_cld
after
1d_var
e_cld
forecast as
background
with uncontaminated
channels above
cloud:
1DVar in clear
conditions
all selected
channels: 1DVar
cloudy
Summary:
•
•
•
•
Create IASI cloudy spectra using NWP analyses (T,H2o,.., CC, CLWV,
CIWV)
Use CO2 method to determine the cloud top pressure and emissivity
Retrieve temperature profile in cloudy condition with CO2 cloud
parameters as guess
validate on CDS dataset
Future:
•
•
•
•
Consolidate the results on recent NWP data (with cloud profile
information on 60 levels) -> package
» add the water vapor profile
» Combine IASI, AVHRR and AMSU information
Validate on AIRS observations
Adapt the method to the IASI stand-alone package
Test a cloud-clearing method (J. Joiner)