- Email: [email protected]
A TOVS cloud-clearing process using AVHRR data
Adv. Space Res. Vol. 12, No. 7, pp. (7)28 1—(7)284, 1992
0273—1177/92 $15.00 Copyright © 1992 COSPAR
Printed in Great Britain. All rights reserved.
A TOYS CLOUD-CLEARING PROCESS USING AVHRR DATA L. Lavanant, P. Brunel, M. Derrien and H. LeGléau Centre de Météorologie Spatiale, 22302 Lannion, BP 147, France
ABSTRACT A cloud description ,made inside the HIRS F.0.V by the AVHRR imager, is used to compute the cloud contribution in the sounding measurements.The study indicates,for the main HIRS chan— nels,an accuracy of cloudy TOVS retrievals of about 0.5°Cin mean and 1°Cin standard devia— tion.The process using GAC insteed of AVHRR data is also tested. INTRODUCTION The general purpose of the study is to improve the accuracy of the declouded HIRS radiances by using the cloud parameters inferred inside the HIRS F.O.V by the AVHRR radiometer.The cloud parameters are the cloud type,the cloud top temperature and the effective emissivity.In this study,we have evaluated the performances of our method by comparing computed cloudy HIRS radiances to the measured one.In this case,the atmospheric profile is known by collocated ra— diosonde and the expected HIRS cloudy radiances are obtained by only using the AVHRR cloud parameters and the atmospheric profile. The procedure, first developped on a NOAA7 data—set , is now tested in an automatic scheme RACINE which acquires radiosonde profiles and collocated NOAA11 satellite data.In a first step,we present the data and then describe the method.In the last section,we compare the fIRS cloudy radiances to the observations. DESCRIPTION OF THE RETRIEVAL SCHEME The Data For every NOAA11 orbits received at the Centre de Netéorologie Spatiale of Lannion,the RACINE procedure acquires automatically radiosonde profiles and collocated satellite data,over 20 marine and coastal stations.For coastal stations, 2 geographical positions are possible for the satellite data extraction: one at the station position itself,the second in the vicinity but over the sea.The AVHRR processes are made in boxes of 34*39 AVHRR pixels centered around the fIRS sounder.9 HIRS ellipses are considered at each geographical position.One of them is selected if more than 20% of AVHRR pixels are declared cloudy.Noreover,one of the 9 boxes has to contain more than 10% of completely clear pixels in order to make a good determination of the brightness surface temperature for the 3 IR AVHRR channels. AV}IRR Processing A cloud detection algorithm is first used: it is a succession of threshold tests applied every AVHRR pixels to various combinations of channels IlJ.A classification algorithm is then applied to the detected cloudy pixels: it makes the distinction between low clouds,high thick clouds,thinner cold cirrus... .121 and allows the description of the box in one or several cloud layers.The classification is applied only on night situations and in the next sec— tions,only night and single cloud layer situations are considered.When a black—body cloud layer is detected,the cloud top temperature is directly estimated from the coldest llp brightness temperature in the box.For semi—transparent clouds or partially cloud cover pixels,the cloud top temperature is determined the least square fit of a theoritical 2p/llp) by /3/.In this study,the selected situations curve have on bidimentional (llp—l classes:the first one,identified been the roughly sorted inhistogramm two different as ‘low—level’ clouds,corresponds to low clouds or situations with some blackbody cloud pixels.The second one corresponds to semi—transparent layers or partially cloud covered pixels and is identified as high—level’ clouds. (7)281
L. Lavanant et a1.
(7)282
In order to estimate the performances of the processing which retrieves the cloud top temperature Tc,a flag is assigned,function of A (Tc -Tmin) / (Tc — Ts) where Tam is the coldest AVHRR pixel temperature in the box and Ts the surface temperature. The flag takes the value: 0 for directly measured cloud temperature (TC Tmin) 1 when A < 10% 2 when 10% < A < 25% 3 when 25% < A < 40% 4 when A > 40% The cloud AVHRR effective emissivity N*E is derived from the ce in the ellipse,the blackbody cloud and surface radiances.
averaged AVHRR measured radian-
Cloudy fIRS radiances retrievals For each fIRS channel,the clear—sky radiance Is is computed by using the surface temperature (inferred from AVHRR observations) and the atmospheric transmittances derived from the application of a radiative transfert equation (ITPP3 fast direct model) to the radiosonde profi— les.The upwards blackbody cloud radiances Ic is computed by using the cloud top temperature and the atmospheric transmittances from the cloud pressure to the satellite.The cloud emissi— vity N*E depends on the wavelength /4/ and statistical regressions between AVHRR and HIRS channels were established on NOAA7 data /5/.In this study, these regressions have been applied without updating,excepted the new channels 10 and 17 of NOAA11 for which regressions have been settled on RACINE. Then,for each channel i, the synthetic cloudy fIRS radiances Ii are computed by the equation: Ii = (1 — N*2i) * Isi + N*Ni * Ici
—~0 ~ •0
—00
—42
—30
—20
—10
0
0
20
to tomB
©.~jjComparison of the cloud top temperature retrieved by AV8RI1 or NAC Number of retrieva1s=247,bias~.39,Standarddeviation=3.03
GAC Processing For all the selected situations,the GAC data are simulated from AVHRR by degrading the resolution as indicated in the NESDIS documentation.All the previous processes have been applied on these data. The comparison of the surface temperatures determined with GAC and AVHRR data gives,on 901 daily,night,marine and land situations,a biais (GAC—AVHRR) of —.11°Cand a standard deviation of .42°C.Atpresent time,no modifications of the thresholds,in the GAC cloud detection algo— rithm,have been made as it should be done specially for the variance threshold.Consequently,a few cloudy AVHRR pixels have been declared as clear—sky pixels,giving a negative biais. Figure 1 shows the variation of the cloud top temperature determined with GAC and AVHRR ta.The figure also shows the distribution in temperature of the two cloud classes. CNN 9! SO
1
2
3
4
5
6
7
8
1.2 2.6
.98 1.2
.58 .91
.64 .53
.39 .57
.21 .59
.07 .59
.27 —.01 .6 1.84 .5 .9 2.48 3.47
10
11
12
TABLE 1: Statistics in clear—sky conditions.Number of retrievals H omean, SD ~standard deviation
13
14
15
.42 .55
.29 .59
.69 2.16 9.28 —.32 —.16 .69 1.42 1.97 .28 .35
=
89
16
17
18
19
da—
A TOYS Cloud-Clearing Process
(7)283
RESULTS AND DISCUSSION Table 1 gives the accuracy of the ITPP3 radiative transfert model ,in clear—sky condi— tions,for night and marine conditions.The results have been obtained with EVA, a similar data set than RACINE but with only clear sky situations.This gives an idea of the best results that we can expect for cloudy radiance restrievals. For the retrieved cloudy fIRS radiances with AVHRR ,figures 2 and 3 show the bias and standard deviation statistics for respectively ‘low—level’ and ‘high—level’ classes, (flag <4).On— ly the channels affected by clouds are presented.Channel 17 have been discarded from the process due to its bad result in clear sky conditions. n—-
L
‘~——~—- ~~I•I~l!
4
1
P
4
WL H H ~ 2
4
6
6
I IC 12 OIN4IELS
I 14
•1•I~ 16
~ lB
2
__J____1__J___J 4 6
~3j
!~~L Comparison of the computed and measured cloudy HISS radiasces,for the ‘low—level classe.tlag <4.Number of retrievals 109
I B
I 2 12 CHANNELS
14
Same as fig 2 but for the level’ classe.Nb 144
lB
16
high
The results for water vapor channels 11 and 12 are very bad,but even in clear—sky condi— tions,the statistics are not good.For the window channel 8, the statistics are better than in clear sky conditions: this is due to the fact that HIRS 8 and AVHRR 4 have very close wave— length numbers and then make dependant measurements.For the 15p and 4.3ii channels,the cloud process has not remarquely degradated the statistics compared to table 1.The accuracy in cloudy conditions 3.7p is very on the quality of the transfert model a as indicated by are of dependant poor quality because we do not have, sofar, AVHRR 3.7p ra/5/.The results at model to compute the blackbody cloud radiance.So,we have assumed a transdiative transfert mittance value of 1 above the cloud to compute the AVHRR 3.71’ cloud emissivity.This degrades the accuracy of the retrieval,specially for low—level cloud layers. The comparison of the synthetic and measured fIRS radiances is a test of the quality of the top temperature retrieval: a bad estimation of the cloud height should degrade the determination of the fIRS cloudy radiances, specially for the channels sounding around this altitu— de.To show that,we have modified the cloud top temperature for all the situations by a factor a: Tc~Tc+a (Ts—Tc)
~—-~~+j
-~
—-~
-I -i
oi~i_ U -~ ----j—-— —.-—— —2 LaLLU~U.11LLLUL1JILLASJLLLIJJLAJUJ_I.LJ.I14~IJAI.L11~_LI_IJ~1LI —SS —50 —25 —26 —15 —16 —6 2 5 IC 11 50 22
~~jj
Influence of Tc on retrieved cloudy HISS radiances. a solid lines = standard deviation, dashed lines bias
=
UTc/(Ts—Tc)
L. Lavanant et al.
(7)284
- -
ex: for Tc =—40°c,Ts = 10°C and a. =20%,the cloud is cooled by a value of lO°C.For the 15p channels 4,5,6,7,figure 4 shows the influence of Tc on the statistics ,for the ‘high—level’ classe.The 0 value of a stands for the standard process. If not well retrieved,the cloud temperature is probably too warm and had to be cooled (a 0).In this case,the standard deviation (solid lines of the figure) increases dramatical— ly,even for a = —10% .This is particulally true for channel 6 which has its weighting func— tion peak at 800mb.In the other hand,channel 4 (weighting function peak at 400mb) is quite insensitive to clouds.A too cold cloud temperature retrieval is more improbable,but even in this case,a positive value a increases the bias (dashed lines) .From this figure,the top tea— perature seems determined with an accuracy better than 5°Cfor cold clouds. FL
0
1
N
8
62
ii SD
2
3
4
37
37
86
—.05
—.05
.39
.15
.72
.68
.98
1.17
~78
1.34
TABLE 2: Statistics on channel 6,function of the cloud temperature flag. N=number of retrievals ,fl=mean, SD=ntandard deviation
For ‘high—level’ clouds,we separate the statistics for the different flags applied on the cloud top temperature.Table 2 gives the results for channel 6,the most affected by bad temperature retrievals.The results for flag 3 are better than expected,because the degradation in top temperature is attenuated by the weak contribution of the cloud to the radiance (little values of the cloud emissivities) Concerning the GAC processing,figure 5 shows the bias and standard deviation statistics for ‘high—level’ clouds.As expected from figure 1,the results are very similar than for figure 3. CONCLUSION AND FUTURE DEVELOPMENTS Cloud parameters and specially cloud top temperature and effective emissivity,inferred by the AVHRR imager, are used to retrieve cloudy fIRS radiances.In this study,the atmospheric profile is known (radiosonde).The accuracy of the fIRS retrievals is function of the quality of the cloud top temperature retrieval,but more data have to be acquired in the next months. The next step is to use the method in a cloud clearing process in which the atmospheric file is unknown and for which the search of a closest situation is necessary.
~
pro-
~I~i~i-iifi~ j
Same as fig 3 but for CAC processing
REFERENCES 1. Le Gleau,H. ,M.Derrien,Operational cloud mask using the AVHRR of NOAA— ll.in:4th AVHRR data users’ meeting,l989,p85. 2. Derrien,M.,H.Le Gleau,An automatic cloud classification using AVHRR at night.in:4th AVHRR data users’ meeting 1989,p69. 3. Derrien,N.,L.Lavanant and H.Le Gleau,Retrieval of top temperature of semi transparent clouds with AVHRR in:IRS88 l988,pl99. 4. Yamamoto,G.,M.Tanaka and70,p282. S..Asano,Radiative transfer in water clouds in the Infrared re— gion.J.Atm. Sciences. .27,19 5. Lavanant,L. ,AVHRR/HIRS coupling.in:Sth Int.TOVS Study Conf.1989,p229.
0273—1177/92 $15.00 Copyright © 1992 COSPAR
Printed in Great Britain. All rights reserved.
A TOYS CLOUD-CLEARING PROCESS USING AVHRR DATA L. Lavanant, P. Brunel, M. Derrien and H. LeGléau Centre de Météorologie Spatiale, 22302 Lannion, BP 147, France
ABSTRACT A cloud description ,made inside the HIRS F.0.V by the AVHRR imager, is used to compute the cloud contribution in the sounding measurements.The study indicates,for the main HIRS chan— nels,an accuracy of cloudy TOVS retrievals of about 0.5°Cin mean and 1°Cin standard devia— tion.The process using GAC insteed of AVHRR data is also tested. INTRODUCTION The general purpose of the study is to improve the accuracy of the declouded HIRS radiances by using the cloud parameters inferred inside the HIRS F.O.V by the AVHRR radiometer.The cloud parameters are the cloud type,the cloud top temperature and the effective emissivity.In this study,we have evaluated the performances of our method by comparing computed cloudy HIRS radiances to the measured one.In this case,the atmospheric profile is known by collocated ra— diosonde and the expected HIRS cloudy radiances are obtained by only using the AVHRR cloud parameters and the atmospheric profile. The procedure, first developped on a NOAA7 data—set , is now tested in an automatic scheme RACINE which acquires radiosonde profiles and collocated NOAA11 satellite data.In a first step,we present the data and then describe the method.In the last section,we compare the fIRS cloudy radiances to the observations. DESCRIPTION OF THE RETRIEVAL SCHEME The Data For every NOAA11 orbits received at the Centre de Netéorologie Spatiale of Lannion,the RACINE procedure acquires automatically radiosonde profiles and collocated satellite data,over 20 marine and coastal stations.For coastal stations, 2 geographical positions are possible for the satellite data extraction: one at the station position itself,the second in the vicinity but over the sea.The AVHRR processes are made in boxes of 34*39 AVHRR pixels centered around the fIRS sounder.9 HIRS ellipses are considered at each geographical position.One of them is selected if more than 20% of AVHRR pixels are declared cloudy.Noreover,one of the 9 boxes has to contain more than 10% of completely clear pixels in order to make a good determination of the brightness surface temperature for the 3 IR AVHRR channels. AV}IRR Processing A cloud detection algorithm is first used: it is a succession of threshold tests applied every AVHRR pixels to various combinations of channels IlJ.A classification algorithm is then applied to the detected cloudy pixels: it makes the distinction between low clouds,high thick clouds,thinner cold cirrus... .121 and allows the description of the box in one or several cloud layers.The classification is applied only on night situations and in the next sec— tions,only night and single cloud layer situations are considered.When a black—body cloud layer is detected,the cloud top temperature is directly estimated from the coldest llp brightness temperature in the box.For semi—transparent clouds or partially cloud cover pixels,the cloud top temperature is determined the least square fit of a theoritical 2p/llp) by /3/.In this study,the selected situations curve have on bidimentional (llp—l classes:the first one,identified been the roughly sorted inhistogramm two different as ‘low—level’ clouds,corresponds to low clouds or situations with some blackbody cloud pixels.The second one corresponds to semi—transparent layers or partially cloud covered pixels and is identified as high—level’ clouds. (7)281
L. Lavanant et a1.
(7)282
In order to estimate the performances of the processing which retrieves the cloud top temperature Tc,a flag is assigned,function of A (Tc -Tmin) / (Tc — Ts) where Tam is the coldest AVHRR pixel temperature in the box and Ts the surface temperature. The flag takes the value: 0 for directly measured cloud temperature (TC Tmin) 1 when A < 10% 2 when 10% < A < 25% 3 when 25% < A < 40% 4 when A > 40% The cloud AVHRR effective emissivity N*E is derived from the ce in the ellipse,the blackbody cloud and surface radiances.
averaged AVHRR measured radian-
Cloudy fIRS radiances retrievals For each fIRS channel,the clear—sky radiance Is is computed by using the surface temperature (inferred from AVHRR observations) and the atmospheric transmittances derived from the application of a radiative transfert equation (ITPP3 fast direct model) to the radiosonde profi— les.The upwards blackbody cloud radiances Ic is computed by using the cloud top temperature and the atmospheric transmittances from the cloud pressure to the satellite.The cloud emissi— vity N*E depends on the wavelength /4/ and statistical regressions between AVHRR and HIRS channels were established on NOAA7 data /5/.In this study, these regressions have been applied without updating,excepted the new channels 10 and 17 of NOAA11 for which regressions have been settled on RACINE. Then,for each channel i, the synthetic cloudy fIRS radiances Ii are computed by the equation: Ii = (1 — N*2i) * Isi + N*Ni * Ici
—~0 ~ •0
—00
—42
—30
—20
—10
0
0
20
to tomB
©.~jjComparison of the cloud top temperature retrieved by AV8RI1 or NAC Number of retrieva1s=247,bias~.39,Standarddeviation=3.03
GAC Processing For all the selected situations,the GAC data are simulated from AVHRR by degrading the resolution as indicated in the NESDIS documentation.All the previous processes have been applied on these data. The comparison of the surface temperatures determined with GAC and AVHRR data gives,on 901 daily,night,marine and land situations,a biais (GAC—AVHRR) of —.11°Cand a standard deviation of .42°C.Atpresent time,no modifications of the thresholds,in the GAC cloud detection algo— rithm,have been made as it should be done specially for the variance threshold.Consequently,a few cloudy AVHRR pixels have been declared as clear—sky pixels,giving a negative biais. Figure 1 shows the variation of the cloud top temperature determined with GAC and AVHRR ta.The figure also shows the distribution in temperature of the two cloud classes. CNN 9! SO
1
2
3
4
5
6
7
8
1.2 2.6
.98 1.2
.58 .91
.64 .53
.39 .57
.21 .59
.07 .59
.27 —.01 .6 1.84 .5 .9 2.48 3.47
10
11
12
TABLE 1: Statistics in clear—sky conditions.Number of retrievals H omean, SD ~standard deviation
13
14
15
.42 .55
.29 .59
.69 2.16 9.28 —.32 —.16 .69 1.42 1.97 .28 .35
=
89
16
17
18
19
da—
A TOYS Cloud-Clearing Process
(7)283
RESULTS AND DISCUSSION Table 1 gives the accuracy of the ITPP3 radiative transfert model ,in clear—sky condi— tions,for night and marine conditions.The results have been obtained with EVA, a similar data set than RACINE but with only clear sky situations.This gives an idea of the best results that we can expect for cloudy radiance restrievals. For the retrieved cloudy fIRS radiances with AVHRR ,figures 2 and 3 show the bias and standard deviation statistics for respectively ‘low—level’ and ‘high—level’ classes, (flag <4).On— ly the channels affected by clouds are presented.Channel 17 have been discarded from the process due to its bad result in clear sky conditions. n—-
L
‘~——~—- ~~I•I~l!
4
1
P
4
WL H H ~ 2
4
6
6
I IC 12 OIN4IELS
I 14
•1•I~ 16
~ lB
2
__J____1__J___J 4 6
~3j
!~~L Comparison of the computed and measured cloudy HISS radiasces,for the ‘low—level classe.tlag <4.Number of retrievals 109
I B
I 2 12 CHANNELS
14
Same as fig 2 but for the level’ classe.Nb 144
lB
16
high
The results for water vapor channels 11 and 12 are very bad,but even in clear—sky condi— tions,the statistics are not good.For the window channel 8, the statistics are better than in clear sky conditions: this is due to the fact that HIRS 8 and AVHRR 4 have very close wave— length numbers and then make dependant measurements.For the 15p and 4.3ii channels,the cloud process has not remarquely degradated the statistics compared to table 1.The accuracy in cloudy conditions 3.7p is very on the quality of the transfert model a as indicated by are of dependant poor quality because we do not have, sofar, AVHRR 3.7p ra/5/.The results at model to compute the blackbody cloud radiance.So,we have assumed a transdiative transfert mittance value of 1 above the cloud to compute the AVHRR 3.71’ cloud emissivity.This degrades the accuracy of the retrieval,specially for low—level cloud layers. The comparison of the synthetic and measured fIRS radiances is a test of the quality of the top temperature retrieval: a bad estimation of the cloud height should degrade the determination of the fIRS cloudy radiances, specially for the channels sounding around this altitu— de.To show that,we have modified the cloud top temperature for all the situations by a factor a: Tc~Tc+a (Ts—Tc)
~—-~~+j
-~
—-~
-I -i
oi~i_ U -~ ----j—-— —.-—— —2 LaLLU~U.11LLLUL1JILLASJLLLIJJLAJUJ_I.LJ.I14~IJAI.L11~_LI_IJ~1LI —SS —50 —25 —26 —15 —16 —6 2 5 IC 11 50 22
~~jj
Influence of Tc on retrieved cloudy HISS radiances. a solid lines = standard deviation, dashed lines bias
=
UTc/(Ts—Tc)
L. Lavanant et al.
(7)284
- -
ex: for Tc =—40°c,Ts = 10°C and a. =20%,the cloud is cooled by a value of lO°C.For the 15p channels 4,5,6,7,figure 4 shows the influence of Tc on the statistics ,for the ‘high—level’ classe.The 0 value of a stands for the standard process. If not well retrieved,the cloud temperature is probably too warm and had to be cooled (a 0).In this case,the standard deviation (solid lines of the figure) increases dramatical— ly,even for a = —10% .This is particulally true for channel 6 which has its weighting func— tion peak at 800mb.In the other hand,channel 4 (weighting function peak at 400mb) is quite insensitive to clouds.A too cold cloud temperature retrieval is more improbable,but even in this case,a positive value a increases the bias (dashed lines) .From this figure,the top tea— perature seems determined with an accuracy better than 5°Cfor cold clouds. FL
0
1
N
8
62
ii SD
2
3
4
37
37
86
—.05
—.05
.39
.15
.72
.68
.98
1.17
~78
1.34
TABLE 2: Statistics on channel 6,function of the cloud temperature flag. N=number of retrievals ,fl=mean, SD=ntandard deviation
For ‘high—level’ clouds,we separate the statistics for the different flags applied on the cloud top temperature.Table 2 gives the results for channel 6,the most affected by bad temperature retrievals.The results for flag 3 are better than expected,because the degradation in top temperature is attenuated by the weak contribution of the cloud to the radiance (little values of the cloud emissivities) Concerning the GAC processing,figure 5 shows the bias and standard deviation statistics for ‘high—level’ clouds.As expected from figure 1,the results are very similar than for figure 3. CONCLUSION AND FUTURE DEVELOPMENTS Cloud parameters and specially cloud top temperature and effective emissivity,inferred by the AVHRR imager, are used to retrieve cloudy fIRS radiances.In this study,the atmospheric profile is known (radiosonde).The accuracy of the fIRS retrievals is function of the quality of the cloud top temperature retrieval,but more data have to be acquired in the next months. The next step is to use the method in a cloud clearing process in which the atmospheric file is unknown and for which the search of a closest situation is necessary.
~
pro-
~I~i~i-iifi~ j
Same as fig 3 but for CAC processing
REFERENCES 1. Le Gleau,H. ,M.Derrien,Operational cloud mask using the AVHRR of NOAA— ll.in:4th AVHRR data users’ meeting,l989,p85. 2. Derrien,M.,H.Le Gleau,An automatic cloud classification using AVHRR at night.in:4th AVHRR data users’ meeting 1989,p69. 3. Derrien,N.,L.Lavanant and H.Le Gleau,Retrieval of top temperature of semi transparent clouds with AVHRR in:IRS88 l988,pl99. 4. Yamamoto,G.,M.Tanaka and70,p282. S..Asano,Radiative transfer in water clouds in the Infrared re— gion.J.Atm. Sciences. .27,19 5. Lavanant,L. ,AVHRR/HIRS coupling.in:Sth Int.TOVS Study Conf.1989,p229.