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The effect of varying image processing parameters on the construction of the humidity field
Adv. Space Res. Vol. 12, No.7, pp. (7)453—(7)456, 1992 Printed in Great Britain. All rights reserved.
0273—1177/92 $15.00 Copyright © 1992 COSPAR
THE EFFECT OF VARYING IMAGE PROCESSING PARAMETERS ON THE CONSTRUCTION OF THE HUMIDITY FIELD P. Ko~Ikováand K. Hiavat)? Czech Hydrometeorological Institute, Na ~abatce 17, 143 06 Praha 4, Czechoslovakia
ABSTRACT A method of the AVHRR (NOAA satellites) data processing, which was prepared for utilization in objective analysis of the relative humidity for the limited area prediction of precipitation, is described and partly evaluated here. INTRODUCTION The impact of processed satellite picture data on the quality of weather prediction on a mesascale has been studied in several projects, but no final conclusions have been drawn. This is primarily because of the incomplete knowledge of the physically realistic relationship between the variables of the prognostic models and the results of picture processing. Certain cloud types and cloud formations can usually be recognized succesfully, but for exampple, the problem of transformation ~f the obtained classification characteristics into the relative humidity values is far from being an unambiguous solution. AVHHR DATA PROCESSING The AVHRR data from noon passes of the NOAA 11 satellite and TEMP and SYNOP conventional data from 1200 GMT main term of observations were used for this study. A combination of the processsing and its evaluation was prepared for the daily prognostic routine in order to refine the relative humidity input data and consequently improve the precipitation forecast /1/. The relative humidity serves as a prognostic variable in the mesomodel employing a grid with a horizontal spacing of 32—km, Fig. 1. Our experience with the AVHRR data processing and especially with the interpretation of the results on the one hand, and our a little limited computing facilities on the other, forced us to exchange the fully automated cluster analysis method, with a higher number of features and object classes, for an operational interactive variant with a simple pre—selection of object classes. Determination of classes is based on the interactive evaluation of histograms of single features. The histogram of B2 (B2 — albeda in channel 2 AVHRR,VIS) is evaluated for a whole cut—out.The picture cut—out contains the selected prognostic area with approximately four steps (e.g.4 x 32—km) added on the boundaries. The threshold value is determined from the histogram of B2 for the separation of “cloudy” areas. The histograms of the next features are then evaluated only for “cloudy” windows of the cut—out (52 ) threshold value). The histogram of T4 (the mean radiation temperature in channel 4—IR) and of d4(standard deviation) are then completed. The number and size of the important 14 intervals can be derived experimentally for each day, but in practice also values for longer time period can be used. The utilization of the classification table of the previous day seems to be profitable, with only a minimum correction for any extraordinary change in the weather conditions. In the interpretation,limitation of the number of classes to the most important T4 maxima is preferable. This usually separates the T4 values into five intervals (it seems to be very realistic from the paint of view of cloud physics), The histogram of ø’4 yields some outstanding maxima and (7)453
(7)454
P. Ko~Ikováand K.Hlavat~
thus a’4 is used as a feature. Usually only two intervals of ~‘4 are utilized, representing very homogeneous and less homogeneous cloud object. Once again, the histogram of s3 albedo of channel 3 (3.55—3.93 microns) is constructed and evaluated only for the picture windows with 52 ) threshold value. Really important differences ran be seen between the histogram representing the whole cut—out and its modification representing only cloudy windows. Another important peak originates when only cloudy objects are evaluated, Fig. 2,and new object classes can be postulated. Feature ~ (standard deviation of m3) has no particular trend and is not used for the subsequent classification. Finally, the classification in several object classes — oblong clusters, is completed.The oblong (or eliptical) clusters are determined by the intervals of the single features /3/. The classification is carried out only in the surroundings of the prognostic grid points. INTERPRETATION AND VERIFICATION OF THE RESULTS Fig. 4 presents the classification of the picture in Fig. 3, in the points of the grid in Fig. 1. We treated this classification as best, taking into account the adjustable parameters of the classification process /3/, for the following reasons: The method works quite well, on the basis of the usual visual comparison. For example, atmospheric fronts and conspicuous cloud formations are described well /3/. But it is of little use for automated numerical mesoscale weather prediction. Another criterion can be,for example, comparison with the densest accessible conventional measurements, with SYNOP data. A brief comparison of SYNOP data on N (the total cloud amount) and single classification characters at SYNOP stations is given in /1/. Analysis of N from the SYNOP data is given in Fig. 5. This analysis is greatly influenced by the irregular distribution of the surface stations, but, nonetheles, a)a good coincidence of areas with N8/8 an special classes, and b)better results of the pattern recognition (regarding N) were obtained. The analysis of the height of the lower limit of the lowest cloud hh (SYNOP) and the brief statistics on the relationship between hh and the class characteristics at the stations are given in /3/.No important dependence can be observed in this case and almost nothing can be said about hh from the classification of the upper limit of the cloudiness. Comparison of the analyses of NMC (the medium cloud amount) and NCH (the high cloud amount) from SYNOP data, adapted slightly on the basis of cloud generi definitions (Atlas of Clouds) and of occurence of single object classes still do not yield convincing results. The sensitivity of the described picture processing method to the localization errors, to errors to the classification table, to the selection of the “window” size (and others) was studied experimentally. The results of the experiment are, of course, different. But only those differences, which will also influence the quality of the objective analysis of the input data or precipitation prediction are important. The class characters are transformed into the relative humidity values and these are included in the analytical process by the method described in /1/. The analysis of the relative humidity (for only one isobaric level) for the best classification from Fig. 4 is displayed in Fig. 6 (TEMP+SYNOP+AVHRR). The results of the analysis are discussed fully in /1/. CONCLUSI ONS The experiments can be summarized as follows: a) In spite of the precise picture localization (1 — 3 pixels), full coincidence of the SYNOP observations of N = (7) — 5/8 and of the processed AVHRR data at all the surface stations cannot be attained, especially for certain weather situations (with a strong advection, for example: we cannot guarantee the full time coincidence). Thus, the highest attainable precision of the picture localization is desirable. b) The representativeness of the description in the surroundings of a grid—point (a station) was studied for several sizes of picture windows: For 5 x S pixels, 8 x B pixels, 16 x 16, 32 x 32 and 3 x 3 (8 x 8) pixels, Fig. 1. For transformation into the relative humidity values, the classes in the special succession are found in single parts of the surroundings. This gives the best results for the 3 x 3(8 x B), slightly better than for the 16 x 16 pixels surroundings; the surroundings of 32 x 32 pixels is too smoothing for the grid—step of 32—km and the utilization of smaller surroundings S x 5 pixels is too time—consuming and of too little benefit f or weather phenomena studied.
The Effect of Varying Image Processing Parameters on the Humidity Field
(7)455
The process of the picture analysis is controlled by several parameters. The optimal adjustment requires some experimental work, especially regarding the character of the prognosis and of the area used for the prognosis. This is also true for the subsequent processes of the objective analysis and time integration of the prognostic model.Consequently, we selected a method which is closely connected with the reliability of the cloud analysis, even when the exact relationship between the relative humidity and cloudines is not known, mainly from the physical point of view. Considering the necessity of also experimentally adjusting the weighting functions and filtrations during the data analysis, we can completely neglect some small errors in the picture processing (square surroundings etc). Verification of the results through evaluation of the precipitation prediction is almost impossible without a special field experiment. The net of measuring stations with measurements carried out in conventional terms is not suitable for such detailed verification. But in spite of the above considerations, we believe that the described method of AVHRR data utilization exerts an unambigously positive influence on the quality of the objective analysis of the relative humidity for the grid with a spacing of 32—km, at least for the horizontal representation. The resulting analysis reflects better the field of cloudiness than that prepared with SYNOP and TEMP data alone. REFERENCES 1.
2. Sokol, P. Ko~fková and J. Krabec, Objective analysis of the relative humidity using enhanced surface observations of cloudiness and economically processed AVHRR data, Adv. Space ~es., 1991, to be published.
2.
H. Setvàk and K. Hlavat~, The calculation of the spectral reflectance in the range of the AVHRR channel 3 of NOAA satellites and its application severe storm studies, in: Utihzation of satellite measurements in modelling and prediction of atmopheric phenomena, ed.P.Ko~fkovâ, IPA CAB, Praha 198B,p.112.
3.
P. Koelkova, On the problem—oriented selection of features for an automated AVHRR picture processing, in: Satellite meteorological symposium, ed. G. Major, MAV, Visegrad 1990, in press.
--
-
1
-
-
46
Fig. 1.
Prognostic area,
I
46x41 grid points, with a spacing of 32—km, surroundings for the I
classification depicted.
I
~16
pixels
32km “.)‘3xepixels I
P. Kol~1kováand K. fflavat~r
(7)456
8.
.
.4 CCC 69 C9A 96660 0C0960644, CICC 0940 09 99*0CC 005666 2CCOLC CCJ000 40 C C CC8 OOFC#S0&2069C5C0 0.200 6660CC CCCC 909C7050866C6000 CE .2 CCC COG C 8 0 4 80 73D~8841C48Q C JOOIIIJJC C 660 CCCII 410 CII 77706 28609CC! A JOlt 0.2 0 A JO CCCIII CCCI 48 84 44656900 68 C800IC 90 C 000CC CCC 9* *6 26 686a59G AS 9500EC 9*00 CCI 0 0 066*0 66 86 5866900 09 AO5ECC 6690 94000 CCC EA448 98 94 560 61 0 C9 6668*0 9669 80000 0800 EAAAA 96 #2 766 60.21 CC 8676* 66.’ E990 F El 04 *6 9* 97 7 9OCCIX 9, C7608A *0 6 69CC 3 0408 CA 5* 7* 9 4 60 Ar C87776 56 8 74 A S 8 CA *4 7* A 6 6000 46 90*56 6 6005 A 8 6 57 95 89 0 4 020 7 ACCA9 *7 6 6677 0 0 6 69 42 89 C 2 60 76 779656 *4 4 * 0 6 66 44 36 6 00 88 977547 89 6 A * 6 66 44 86 2 44 77777 *8 8 8 6 A 6 6 29 66 4 0 2 79 C 00 0 8 38 6 8 82 29 4 46 6* 56 6 56 0 84 08 83 66 5 66 0 *7 4 48 69 86 63 C9 A 88 8 44 46 26 84 9 0 4 60 B6 06 0 CE A 80 6 48 66 8 46 8 0 5 84 9 A6 02 6 CC 06 0 8 43 73 4 64484 84 6 44 4 38 06 6 09 84 9 44 84 8 8446 4 5 44 49 0 6 6 69* 00 8 44 04 2 8444 44 44 2C 90 0 CA 44 0 04 42 6 88 4 4 4 44 8 00266 C 0* 88 2 4 80 40 0 78 4 4 84 099 06800 0 AC 8884 8480 8000 88 8 A 44 00 1 6600 C 80 084 7488 8888 79 8 4 44 80 8953 0 0 1 C 08477 70 *758 0 8 6 66 000 8 6 CC A 5570*0 70 9777 06 3 * 58 0 800 626 * 098940C C 0*8. 0 066 88 0 • AA*A 87 9 0 6A 80* 6 66 0 6** 86 710C59589IJ 8 8000 II 0 A AC9 21 ..59 B5A0JCP901CHX .6 14 I 1 I 0590 * IJIII 6 0 0 10 0 ...9*A79 ICDJJ 0 8069C 0 14 ....00 00.600 6620JCC 0006 ...5 9C 00 006 0066090914 0006 006018 6006995 CCC6 •
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0866 864 0826 2680 8 62 1 08
Fig. 3. 15.9.1989, NOAA 11, prognostic area and its nearest proximity.
66
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44
58
54
00 8 6* AC 6 99.Z50E6.FJCC SAC’6A69 6 8 *4 00 86*0 *0 6 365860FF C 66* 9 9 44 II 458 46 V000A0 08 0.060 ‘30 * A 96 AC 0 50 0* 6265 5’0F6 80 01 E’3.A *0 A C A 47 6 *977 CA A’ 62964 9*0 6 79A9N7 C 19 0 79602* 08.6 0 9926,, 9 CC 06 * 96 70990* 668 644456 2 06 74 7677 CI 4C9A 4~65648 62 II 68 77*3.8 809099 666 0 882~ 06 40 8($#7 ~/.\6 648 099*A 86062 280 4466 84 8877 6 4 600 857877 00 04 28-.67 5 68 80 9488 ~JSO86 877*66 6564.24.4666 4 68 4 8268 06677 6 .4—2 6660 0 08 0~~0 88 0 4 2 950 6 668 4 9606 66 4 9.7 4 a s6 4 47863 66 08088/4 48 8A6C 0666 8408 4 900 26 880 \6~8 40 08 08 4444 444 44 C 5 60 644 8~ 8 44 44 88 444 44 64 IC 9 66 840~~, 44 44 8 08 444 44 66 C 0 606 80&~.448 64 0 07 484 84 87 600 0 6 864 68 0 57 0088 #40 6 70 000 0 0075 15 7 9* 8886 4288 C 00 00 6 7 0*64 6 7 0 0866 076 06 9 60 2608 .94 6675 66 8007 990 CS 6 064656.2 CA 5 0686 COO Coo 066 CD ..5A6467711C99 69 IC 5 0 50 016600 ...00 8749311 CC 9 C 0 6 081 • .956059.2 6.2 0 I 02000601 • . 405 JO .2.2 C 6060 .599 5 .21*1 .2 44 600000
~ 4. The classification for the prognostic area, 8x8 pixel window used around grid points.
Fig. 5. Total cloud cover N (analyzed), the isolines of 7/8, 8/8 overlapped with the classification in grid points.
*0
~
C,
Fig. 6.
Analyzed field
of the relative humidity, 700—hPa,
~q.
o
TEMP+SYNOP+AVHRR data.
0273—1177/92 $15.00 Copyright © 1992 COSPAR
THE EFFECT OF VARYING IMAGE PROCESSING PARAMETERS ON THE CONSTRUCTION OF THE HUMIDITY FIELD P. Ko~Ikováand K. Hiavat)? Czech Hydrometeorological Institute, Na ~abatce 17, 143 06 Praha 4, Czechoslovakia
ABSTRACT A method of the AVHRR (NOAA satellites) data processing, which was prepared for utilization in objective analysis of the relative humidity for the limited area prediction of precipitation, is described and partly evaluated here. INTRODUCTION The impact of processed satellite picture data on the quality of weather prediction on a mesascale has been studied in several projects, but no final conclusions have been drawn. This is primarily because of the incomplete knowledge of the physically realistic relationship between the variables of the prognostic models and the results of picture processing. Certain cloud types and cloud formations can usually be recognized succesfully, but for exampple, the problem of transformation ~f the obtained classification characteristics into the relative humidity values is far from being an unambiguous solution. AVHHR DATA PROCESSING The AVHRR data from noon passes of the NOAA 11 satellite and TEMP and SYNOP conventional data from 1200 GMT main term of observations were used for this study. A combination of the processsing and its evaluation was prepared for the daily prognostic routine in order to refine the relative humidity input data and consequently improve the precipitation forecast /1/. The relative humidity serves as a prognostic variable in the mesomodel employing a grid with a horizontal spacing of 32—km, Fig. 1. Our experience with the AVHRR data processing and especially with the interpretation of the results on the one hand, and our a little limited computing facilities on the other, forced us to exchange the fully automated cluster analysis method, with a higher number of features and object classes, for an operational interactive variant with a simple pre—selection of object classes. Determination of classes is based on the interactive evaluation of histograms of single features. The histogram of B2 (B2 — albeda in channel 2 AVHRR,VIS) is evaluated for a whole cut—out.The picture cut—out contains the selected prognostic area with approximately four steps (e.g.4 x 32—km) added on the boundaries. The threshold value is determined from the histogram of B2 for the separation of “cloudy” areas. The histograms of the next features are then evaluated only for “cloudy” windows of the cut—out (52 ) threshold value). The histogram of T4 (the mean radiation temperature in channel 4—IR) and of d4(standard deviation) are then completed. The number and size of the important 14 intervals can be derived experimentally for each day, but in practice also values for longer time period can be used. The utilization of the classification table of the previous day seems to be profitable, with only a minimum correction for any extraordinary change in the weather conditions. In the interpretation,limitation of the number of classes to the most important T4 maxima is preferable. This usually separates the T4 values into five intervals (it seems to be very realistic from the paint of view of cloud physics), The histogram of ø’4 yields some outstanding maxima and (7)453
(7)454
P. Ko~Ikováand K.Hlavat~
thus a’4 is used as a feature. Usually only two intervals of ~‘4 are utilized, representing very homogeneous and less homogeneous cloud object. Once again, the histogram of s3 albedo of channel 3 (3.55—3.93 microns) is constructed and evaluated only for the picture windows with 52 ) threshold value. Really important differences ran be seen between the histogram representing the whole cut—out and its modification representing only cloudy windows. Another important peak originates when only cloudy objects are evaluated, Fig. 2,and new object classes can be postulated. Feature ~ (standard deviation of m3) has no particular trend and is not used for the subsequent classification. Finally, the classification in several object classes — oblong clusters, is completed.The oblong (or eliptical) clusters are determined by the intervals of the single features /3/. The classification is carried out only in the surroundings of the prognostic grid points. INTERPRETATION AND VERIFICATION OF THE RESULTS Fig. 4 presents the classification of the picture in Fig. 3, in the points of the grid in Fig. 1. We treated this classification as best, taking into account the adjustable parameters of the classification process /3/, for the following reasons: The method works quite well, on the basis of the usual visual comparison. For example, atmospheric fronts and conspicuous cloud formations are described well /3/. But it is of little use for automated numerical mesoscale weather prediction. Another criterion can be,for example, comparison with the densest accessible conventional measurements, with SYNOP data. A brief comparison of SYNOP data on N (the total cloud amount) and single classification characters at SYNOP stations is given in /1/. Analysis of N from the SYNOP data is given in Fig. 5. This analysis is greatly influenced by the irregular distribution of the surface stations, but, nonetheles, a)a good coincidence of areas with N8/8 an special classes, and b)better results of the pattern recognition (regarding N) were obtained. The analysis of the height of the lower limit of the lowest cloud hh (SYNOP) and the brief statistics on the relationship between hh and the class characteristics at the stations are given in /3/.No important dependence can be observed in this case and almost nothing can be said about hh from the classification of the upper limit of the cloudiness. Comparison of the analyses of NMC (the medium cloud amount) and NCH (the high cloud amount) from SYNOP data, adapted slightly on the basis of cloud generi definitions (Atlas of Clouds) and of occurence of single object classes still do not yield convincing results. The sensitivity of the described picture processing method to the localization errors, to errors to the classification table, to the selection of the “window” size (and others) was studied experimentally. The results of the experiment are, of course, different. But only those differences, which will also influence the quality of the objective analysis of the input data or precipitation prediction are important. The class characters are transformed into the relative humidity values and these are included in the analytical process by the method described in /1/. The analysis of the relative humidity (for only one isobaric level) for the best classification from Fig. 4 is displayed in Fig. 6 (TEMP+SYNOP+AVHRR). The results of the analysis are discussed fully in /1/. CONCLUSI ONS The experiments can be summarized as follows: a) In spite of the precise picture localization (1 — 3 pixels), full coincidence of the SYNOP observations of N = (7) — 5/8 and of the processed AVHRR data at all the surface stations cannot be attained, especially for certain weather situations (with a strong advection, for example: we cannot guarantee the full time coincidence). Thus, the highest attainable precision of the picture localization is desirable. b) The representativeness of the description in the surroundings of a grid—point (a station) was studied for several sizes of picture windows: For 5 x S pixels, 8 x B pixels, 16 x 16, 32 x 32 and 3 x 3 (8 x 8) pixels, Fig. 1. For transformation into the relative humidity values, the classes in the special succession are found in single parts of the surroundings. This gives the best results for the 3 x 3(8 x B), slightly better than for the 16 x 16 pixels surroundings; the surroundings of 32 x 32 pixels is too smoothing for the grid—step of 32—km and the utilization of smaller surroundings S x 5 pixels is too time—consuming and of too little benefit f or weather phenomena studied.
The Effect of Varying Image Processing Parameters on the Humidity Field
(7)455
The process of the picture analysis is controlled by several parameters. The optimal adjustment requires some experimental work, especially regarding the character of the prognosis and of the area used for the prognosis. This is also true for the subsequent processes of the objective analysis and time integration of the prognostic model.Consequently, we selected a method which is closely connected with the reliability of the cloud analysis, even when the exact relationship between the relative humidity and cloudines is not known, mainly from the physical point of view. Considering the necessity of also experimentally adjusting the weighting functions and filtrations during the data analysis, we can completely neglect some small errors in the picture processing (square surroundings etc). Verification of the results through evaluation of the precipitation prediction is almost impossible without a special field experiment. The net of measuring stations with measurements carried out in conventional terms is not suitable for such detailed verification. But in spite of the above considerations, we believe that the described method of AVHRR data utilization exerts an unambigously positive influence on the quality of the objective analysis of the relative humidity for the grid with a spacing of 32—km, at least for the horizontal representation. The resulting analysis reflects better the field of cloudiness than that prepared with SYNOP and TEMP data alone. REFERENCES 1.
2. Sokol, P. Ko~fková and J. Krabec, Objective analysis of the relative humidity using enhanced surface observations of cloudiness and economically processed AVHRR data, Adv. Space ~es., 1991, to be published.
2.
H. Setvàk and K. Hlavat~, The calculation of the spectral reflectance in the range of the AVHRR channel 3 of NOAA satellites and its application severe storm studies, in: Utihzation of satellite measurements in modelling and prediction of atmopheric phenomena, ed.P.Ko~fkovâ, IPA CAB, Praha 198B,p.112.
3.
P. Koelkova, On the problem—oriented selection of features for an automated AVHRR picture processing, in: Satellite meteorological symposium, ed. G. Major, MAV, Visegrad 1990, in press.
--
-
1
-
-
46
Fig. 1.
Prognostic area,
I
46x41 grid points, with a spacing of 32—km, surroundings for the I
classification depicted.
I
~16
pixels
32km “.)‘3xepixels I
P. Kol~1kováand K. fflavat~r
(7)456
8.
.
.4 CCC 69 C9A 96660 0C0960644, CICC 0940 09 99*0CC 005666 2CCOLC CCJ000 40 C C CC8 OOFC#S0&2069C5C0 0.200 6660CC CCCC 909C7050866C6000 CE .2 CCC COG C 8 0 4 80 73D~8841C48Q C JOOIIIJJC C 660 CCCII 410 CII 77706 28609CC! A JOlt 0.2 0 A JO CCCIII CCCI 48 84 44656900 68 C800IC 90 C 000CC CCC 9* *6 26 686a59G AS 9500EC 9*00 CCI 0 0 066*0 66 86 5866900 09 AO5ECC 6690 94000 CCC EA448 98 94 560 61 0 C9 6668*0 9669 80000 0800 EAAAA 96 #2 766 60.21 CC 8676* 66.’ E990 F El 04 *6 9* 97 7 9OCCIX 9, C7608A *0 6 69CC 3 0408 CA 5* 7* 9 4 60 Ar C87776 56 8 74 A S 8 CA *4 7* A 6 6000 46 90*56 6 6005 A 8 6 57 95 89 0 4 020 7 ACCA9 *7 6 6677 0 0 6 69 42 89 C 2 60 76 779656 *4 4 * 0 6 66 44 36 6 00 88 977547 89 6 A * 6 66 44 86 2 44 77777 *8 8 8 6 A 6 6 29 66 4 0 2 79 C 00 0 8 38 6 8 82 29 4 46 6* 56 6 56 0 84 08 83 66 5 66 0 *7 4 48 69 86 63 C9 A 88 8 44 46 26 84 9 0 4 60 B6 06 0 CE A 80 6 48 66 8 46 8 0 5 84 9 A6 02 6 CC 06 0 8 43 73 4 64484 84 6 44 4 38 06 6 09 84 9 44 84 8 8446 4 5 44 49 0 6 6 69* 00 8 44 04 2 8444 44 44 2C 90 0 CA 44 0 04 42 6 88 4 4 4 44 8 00266 C 0* 88 2 4 80 40 0 78 4 4 84 099 06800 0 AC 8884 8480 8000 88 8 A 44 00 1 6600 C 80 084 7488 8888 79 8 4 44 80 8953 0 0 1 C 08477 70 *758 0 8 6 66 000 8 6 CC A 5570*0 70 9777 06 3 * 58 0 800 626 * 098940C C 0*8. 0 066 88 0 • AA*A 87 9 0 6A 80* 6 66 0 6** 86 710C59589IJ 8 8000 II 0 A AC9 21 ..59 B5A0JCP901CHX .6 14 I 1 I 0590 * IJIII 6 0 0 10 0 ...9*A79 ICDJJ 0 8069C 0 14 ....00 00.600 6620JCC 0006 ...5 9C 00 006 0066090914 0006 006018 6006995 CCC6 •
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0866 864 0826 2680 8 62 1 08
Fig. 3. 15.9.1989, NOAA 11, prognostic area and its nearest proximity.
66
7 6
44
58
54
00 8 6* AC 6 99.Z50E6.FJCC SAC’6A69 6 8 *4 00 86*0 *0 6 365860FF C 66* 9 9 44 II 458 46 V000A0 08 0.060 ‘30 * A 96 AC 0 50 0* 6265 5’0F6 80 01 E’3.A *0 A C A 47 6 *977 CA A’ 62964 9*0 6 79A9N7 C 19 0 79602* 08.6 0 9926,, 9 CC 06 * 96 70990* 668 644456 2 06 74 7677 CI 4C9A 4~65648 62 II 68 77*3.8 809099 666 0 882~ 06 40 8($#7 ~/.\6 648 099*A 86062 280 4466 84 8877 6 4 600 857877 00 04 28-.67 5 68 80 9488 ~JSO86 877*66 6564.24.4666 4 68 4 8268 06677 6 .4—2 6660 0 08 0~~0 88 0 4 2 950 6 668 4 9606 66 4 9.7 4 a s6 4 47863 66 08088/4 48 8A6C 0666 8408 4 900 26 880 \6~8 40 08 08 4444 444 44 C 5 60 644 8~ 8 44 44 88 444 44 64 IC 9 66 840~~, 44 44 8 08 444 44 66 C 0 606 80&~.448 64 0 07 484 84 87 600 0 6 864 68 0 57 0088 #40 6 70 000 0 0075 15 7 9* 8886 4288 C 00 00 6 7 0*64 6 7 0 0866 076 06 9 60 2608 .94 6675 66 8007 990 CS 6 064656.2 CA 5 0686 COO Coo 066 CD ..5A6467711C99 69 IC 5 0 50 016600 ...00 8749311 CC 9 C 0 6 081 • .956059.2 6.2 0 I 02000601 • . 405 JO .2.2 C 6060 .599 5 .21*1 .2 44 600000
~ 4. The classification for the prognostic area, 8x8 pixel window used around grid points.
Fig. 5. Total cloud cover N (analyzed), the isolines of 7/8, 8/8 overlapped with the classification in grid points.
*0
~
C,
Fig. 6.
Analyzed field
of the relative humidity, 700—hPa,
~q.
o
TEMP+SYNOP+AVHRR data.