Applications Of A Satellite-Based Objective Overshooting Convective Cloud Top Detection Product
Convective cloud distribution monitoring using Cloud ... · One of the research instrument that can...
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www.meteconferences.org The 9 th Asia-Oceania Meteorological Satellite User’s Conference Convective cloud distribution monitoring using Cloud Convective Overlays and Red-Green-Blue Convective Storms methods on Himawari-8 satellite imagery on the Eastern Coast of North Sumatra Immanuel Jhonson Arizona Saragih 1, *, Adelina Lumban Gaol 2 , Aries Kristianto 3 , and Andersen Lasbayakin Panjaitan 4 1,2,3 Indonesian School of Meteorology Climatology and Geophysics (STMKG), South Tangerang 4 Indonesian Agency for Meteorology Climatology and Geophysics (BMKG), Jakarta * email: [email protected] Abstract The Eastern Coast of North Sumatra which was directly adjacent to the Malacca Strait was an area with a high frequency of heavy rain events. This indicates the high intensity of convective activity in the Eastern Coast of North Sumatra, so that the information of convective cloud distribution is needed to analysis and forecast the rain events. Convective cloud distribution can be identified using Cloud Convective Overlays (CCO) and Red-Green- Blue Convective Storms (RGB-CS) methods. The CCO method is an overlay technique of two types of algorithms by utilizing differences in the emissivity of two infrared channels or Brightness Temperature Different (BTD), namely Split Windows [SW = BTD (IR1-I2)] and Dual Channel Difference [DCD = BTD (IR1-WV)]. The SW algorithm is used to calculate the amount of water vapor in the atmosphere as an indicator of convective cloud grouping. The DCD algorithm is used to detect the height of clouds reaching the tropopause layer. The RGB-CS method is a multispectral method that displays satellite imagery with three specific algorithms to represent red, green, and blue color composites using 6 channels, the 3 rd channel (0.64 μm); 5 th channel (1.6 μm); 7 th channel (3.7 μm); 8 th channel (6.2 μm); 10 th channel (7.3 μm); 13rd channel (10.4 μm); and 15 th channel (12.4 μm). Case studies were carried out on heavy rain events in 15 th September 2018. The results of the study show that in the heavy rain events, the CCO and RGB-CS methods can clearly capture convective cloud patterns. The RGB-CS method can produce more real color contrast and 'natural look' images by maintaining the original texture of the cloud so that it is easier to observe and analysis the classification of convective clouds that have the potential to produce the extreme rainfall. Introduction A B Data and methods This study was conducted in the East Coast region of North Sumatra, which is at coordinates 0 o -6 o N and 96 o - 102 o E (Figure 1). The data used are measured rainfall data from KNO and BLW, as well as Himawari-8 satellite image data from the Sub-Section of Satellite Image Management - BMKG. Results and discussion Conclutions References Based on convective distribution monitoring using RGB-CS and CCO methods shows that extreme rain on the East Coast of North Sumatra on 15 th September caused by the convective cloud (CB) clusters that grow and develop very intense. The linear pattern of the top cloud temperature and the rainfall graphs can be used as a reference in the making of early warnings of heavy rain. Both RGB-CS and CCO methods show relatively similar convective distribution patterns, but in the RGB-CS method the convective area looks more real and can be classified and used to forecast the growth and movement of clouds based on the visible color-differences. The high level of absorption of solar radiation causes high convective activity in the tropics. The high convective activity results in high rainfall (Tjasyono, 2004). The high intensity of rainfall on the East Coast of North Sumatra is an important thing to study because it has a significant impact on community activities, e.g. can trigger the flood occurrence. One of the research instrument that can be used to assess the occurrence of heavy rainfall is satellite imagery. Satellite image data can be processed and analyzed to find out the convective evolution stage that causes rain in an area (Saragih, et. al., 2017). Convective distribution analysis can be identified using satellite imagery data subjectively and objectively. The subjective method is to directly observe rainfall through satellite imagery. Objective method is a method that is carried out by processing satellite data into satellite imagery products by utilizing the characteristics of several satellite-image channels. Subjective interpretation has many shortcomings, one of which is a low and inconsistent level of accuracy. On 15 th September 2018, there was heavy rain in the East Coast region of North Sumatra. Observation data from Kualanamu Meteorological Station (KNO) and Belawan Maritime Meteorological Station (BLW) show that the measured rainfall was 40 mm/day and 16 mm/day respectively. The rainfall peak occurs at night, ie at 12.00 - 15.00 UTC. The occurrence of rain at night allows a more significant impact due to the unpreparedness of the community, so early warning is needed. This study was conducted to determine the accuracy of the CCO and RGB- CS methods in identifying convective cloud cover on the East Coast region of North Sumatra so that it can be used as a reference in making extreme weather early warning. Figure 2. Graphs of measured rainfall at KNO and BLW weather station on 15 th September 2018 Figure 4. Convective distribution by RGB-CS (a,b) and CCO (c,d) methods on the growth (a,c) and mature (b,d) stage of convective cloud evolution on 15 th September 2018; the ‘black dot’ is the location of the KNO In general, the convective distribution of the CCO and RGB- CS methods shows the relatively-same pattern. However, the CCO method only shows the convective cloud cover while the RGB-CS can show convection area and convective cloud concentration distribution. The RGB-CS method can be used as a convective cloud type classification analysis indicator based on its color-difference. At Figure 4.a, it can be seen that there is a yellowish color that shows the areas with strong convection and updraft processes. The yellowish color is a combination of red and green colors. The ‘Red’ indicates a thick cloud, while the ‘Green’ indicates the presence of small ice particles in the cloud. Based on Figure 2 it is known that rain occurs from 09.00 - 12.00 UTC and ends at 06.00 - 09.00 UTC. Rainfall peaks in KNO and BLW occurred at 12.00 - 15.00 UTC, ie. rainfall value of around 21 mm/3-hour and 10 mm/3-hour respectively. The similarity of rainfall patterns and the distance between KNO and BLW which are relatively far away indicate that the convective clouds that cause the rain have a relatively broad cover. Saragih, I. J. A., Kristianto, A., 2017, Kajian dinamika atmosfer saat kejadian hujan lebat di wilayah Pesisir Timur Sumatera Utara menggunakan model WRF- ARW dan citra satelit Himawari-8, Unnes Physics Journal, 6(1), 25-30. Syaifullah, M. D., & Nuryanto, S., 2016, Pemanfaatan data satelit GMS multi kanal untuk kegiatan Teknologi Modifikasi Cuaca, Jurnal Sains & Teknologi Modifikasi Cuaca, 17(2), 47-55 Tjasyono, B., 2004, Klimatologi, Institut Teknologi Bandung, Bandung Meteorology Department - Indonesian School of Meteorology Climatology and Geophysics (STMKG) Satellite imagery data used is VS channel data (VIS 0.6 μm), N2 (NIR 1.6 μm), I4 (IR4 3.9 μm), WV (WV 6.2 μm), W3 (WV3 7.3 μm), IR (IR1 10.4 μm ), I2 (IR2 12.4 μm). The study time was a case study of heavy rain events on 15 th September 2018. The CCO method uses two algorithms with the use of the IR channel, the I2 channel, and the WV channel. The first algorithm uses the Split Window (SW) method which utilizes two channels that are atmospheric windows (transparent radiation to atmospheric gases) with a spectrum of different wavelengths. This algorithm is used to distinguish between Cumulonimbus clouds (CB) and thin Cirrus (Ci) clouds. Small difference values show high and thick clouds (CB) while large difference values indicate the absence of thin Ci clouds or clouds (Syaifullah et. al., 2016). SW = BTD [IR-I2] < 2 The second algorithm is the Dual Channel Difference (DCD) method by utilizing two different channels, namely atmospheric windows and atmospheric bands (radiation absorbed by atmospheric gases). The second algorithm serves to distinguish between CB clouds and low clouds. Small difference values show thick clouds (CB), while large difference values show low and thin clouds (Cumulus/CU) (Syaifullah et. al., 2016). DCD = BTD [IR-WV] < 3 Figure 1. Map of the study area; BLW = Belawan, KNO = Kualanamu, SPL = Sampali Malacca Strait The time series graph of the cloud peak temperature in Figure 3 represents the stages of convective cloud development around the KNO. Based on the cloud peak temperature analysis, three stages of convective cloud evolution can be identified, namely the growth stage (10.00 - 13.00 UTC), the mature stage (13.00 - 19.00 UTC), and the dissipation stage (19.00 - 20.00 UTC). Based on the value of the lowest cloud peak temperature, this convective cloud is included as a CB with the top cloud temperature value less than -70 o C. Analyse from the duration of its development, this convective cloud has a life-time that is relatively longer than its normal time, which is normally about 3-4 hours. When compared to the measured rainfall graph, the time-series graph of the top cloud temperature is related to graph of rainfall. Figure 3. Graphs of top cloud temperature from the IR channel of Himawari-8 on 15th September 2018 a d c b Acknowledgements The author would like to thank the 9 th AOMSUC committee. The author also would like to thank the Sub- Section of Satellite Image Management - BMKG and STMKG Meteorology Laboratory for the availability of data and facilities used in this study.
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The 9th Asia-Oceania Meteorological Satellite User’s Conference
Convective cloud distribution monitoring using Cloud Convective Overlays
and Red-Green-Blue Convective Storms methods on Himawari-8 satellite imagery
on the Eastern Coast of North Sumatra Immanuel Jhonson Arizona Saragih1,*, Adelina Lumban Gaol2, Aries Kristianto3, and Andersen Lasbayakin Panjaitan4
1,2,3 Indonesian School of Meteorology Climatology and Geophysics (STMKG), South Tangerang4 Indonesian Agency for Meteorology Climatology and Geophysics (BMKG), Jakarta* email: [email protected]
Abstract
The Eastern Coast of North Sumatra which was directlyadjacent to the Malacca Strait was an area with a high
frequency of heavy rain events. This indicates the highintensity of convective activity in the Eastern Coast of
North Sumatra, so that the information of convective
cloud distribution is needed to analysis and forecast therain events. Convective cloud distribution can be identified
using Cloud Convective Overlays (CCO) and Red-Green-Blue Convective Storms (RGB-CS) methods. The CCO
method is an overlay technique of two types ofalgorithms by utilizing differences in the emissivity of twoinfrared channels or Brightness Temperature Different(BTD), namely Split Windows [SW = BTD (IR1-I2)] and DualChannel Difference [DCD = BTD (IR1-WV)]. The SWalgorithm is used to calculate the amount of water vapor inthe atmosphere as an indicator of convective cloudgrouping. The DCD algorithm is used to detect the heightof clouds reaching the tropopause layer. The RGB-CS
method is a multispectral method that displays satelliteimagery with three specific algorithms to represent red,green, and blue color composites using 6 channels, the 3rd
channel (0.64 μm); 5th channel (1.6 μm); 7th channel (3.7μm); 8th channel (6.2 μm); 10th channel (7.3 μm); 13rdchannel (10.4 μm); and 15th channel (12.4 μm). Casestudies were carried out on heavy rain events in 15th
September 2018. The results of the study show that in theheavy rain events, the CCO and RGB-CS methods can
clearly capture convective cloud patterns. The RGB-CSmethod can produce more real color contrast and 'naturallook' images by maintaining the original texture of thecloud so that it is easier to observe and analysis theclassification of convective clouds that have the potentialto produce the extreme rainfall.
Introduction
A
B
Data and methodsThis study was conducted in the East Coast region ofNorth Sumatra, which is at coordinates 0o - 6o N and 96o -
102o E (Figure 1). The data used are measured rainfalldata from KNO and BLW, as well as Himawari-8 satelliteimage data from the Sub-Section of Satellite ImageManagement - BMKG.
Results and discussion
Conclutions
References
Based on convective distribution monitoring using RGB-CSand CCO methods shows that extreme rain on the EastCoast of North Sumatra on 15th September caused by the
convective cloud (CB) clusters that grow and developvery intense. The linear pattern of the top cloudtemperature and the rainfall graphs can be used as areference in the making of early warnings of heavy rain.Both RGB-CS and CCO methods show relatively similar
convective distribution patterns, but in the RGB-CS
method the convective area looks more real and can beclassified and used to forecast the growth and movementof clouds based on the visible color-differences.
The high level of absorption of solar radiation causes high
convective activity in the tropics. The high convectiveactivity results in high rainfall (Tjasyono, 2004). The highintensity of rainfall on the East Coast of North Sumatra isan important thing to study because it has a significantimpact on community activities, e.g. can trigger the floodoccurrence. One of the research instrument that can be
used to assess the occurrence of heavy rainfall is satelliteimagery. Satellite image data can be processed andanalyzed to find out the convective evolution stage thatcauses rain in an area (Saragih, et. al., 2017).
Convective distribution analysis can be identified using
satellite imagery data subjectively and objectively. Thesubjective method is to directly observe rainfall throughsatellite imagery. Objective method is a method that iscarried out by processing satellite data into satelliteimagery products by utilizing the characteristics of severalsatellite-image channels. Subjective interpretation hasmany shortcomings, one of which is a low and inconsistentlevel of accuracy.
On 15th September 2018, there was heavy rain in the EastCoast region of North Sumatra. Observation data fromKualanamu Meteorological Station (KNO) and BelawanMaritime Meteorological Station (BLW) show that the
measured rainfall was 40 mm/day and 16 mm/dayrespectively. The rainfall peak occurs at night, ie at 12.00 -15.00 UTC. The occurrence of rain at night allows a moresignificant impact due to the unpreparedness of thecommunity, so early warning is needed. This study wasconducted to determine the accuracy of the CCO and RGB-
CS methods in identifying convective cloud cover on theEast Coast region of North Sumatra so that it can be usedas a reference in making extreme weather early warning.
Figure 2. Graphs of measured rainfall at KNO and BLW weather station on 15th September 2018
Figure 4. Convective distribution by RGB-CS (a,b) and CCO (c,d)methods on the growth (a,c) and mature (b,d) stage ofconvective cloud evolution on 15th September 2018; the‘black dot’ is the location of the KNO
In general, the convective distribution of the CCO and RGB-
CS methods shows the relatively-same pattern. However,the CCO method only shows the convective cloud coverwhile the RGB-CS can show convection area and convectivecloud concentration distribution. The RGB-CS method canbe used as a convective cloud type classification analysisindicator based on its color-difference. At Figure 4.a, it can
be seen that there is a yellowish color that shows theareas with strong convection and updraft processes. Theyellowish color is a combination of red and green colors.The ‘Red’ indicates a thick cloud, while the ‘Green’indicates the presence of small ice particles in the cloud.
Based on Figure 2 it is known that rain occurs from 09.00 -12.00 UTC and ends at 06.00 - 09.00 UTC. Rainfall peaks in
KNO and BLW occurred at 12.00 - 15.00 UTC, ie. rainfall
value of around 21 mm/3-hour and 10 mm/3-hourrespectively. The similarity of rainfall patterns and thedistance between KNO and BLW which are relatively faraway indicate that the convective clouds that cause therain have a relatively broad cover.
Saragih, I. J. A., Kristianto, A., 2017, Kajian dinamikaatmosfer saat kejadian hujan lebat di wilayah PesisirTimur Sumatera Utara menggunakan model WRF-ARW dan citra satelit Himawari-8, Unnes PhysicsJournal, 6(1), 25-30.
Syaifullah, M. D., & Nuryanto, S., 2016, Pemanfaatan datasatelit GMS multi kanal untuk kegiatan TeknologiModifikasi Cuaca, Jurnal Sains & Teknologi ModifikasiCuaca, 17(2), 47-55
Tjasyono, B., 2004, Klimatologi, Institut TeknologiBandung, Bandung
Meteorology Department - Indonesian School of Meteorology Climatology and Geophysics (STMKG)
Satellite imagery data used is VS channel data (VIS 0.6 µm),N2 (NIR 1.6 µm), I4 (IR4 3.9 µm), WV (WV 6.2 µm), W3(WV3 7.3 µm), IR (IR1 10.4 µm ), I2 (IR2 12.4 µm). Thestudy time was a case study of heavy rain events on 15th
September 2018.
The CCO method uses two algorithms with the use of theIR channel, the I2 channel, and the WV channel. The firstalgorithm uses the Split Window (SW) method whichutilizes two channels that are atmospheric windows(transparent radiation to atmospheric gases) with aspectrum of different wavelengths. This algorithm is usedto distinguish between Cumulonimbus clouds (CB) and thinCirrus (Ci) clouds. Small difference values show high andthick clouds (CB) while large difference values indicate theabsence of thin Ci clouds or clouds (Syaifullah et. al., 2016).
SW = BTD [IR-I2] < 2
The second algorithm is the Dual Channel Difference (DCD)method by utilizing two different channels, namelyatmospheric windows and atmospheric bands (radiationabsorbed by atmospheric gases). The second algorithmserves to distinguish between CB clouds and low clouds.Small difference values show thick clouds (CB), while largedifference values show low and thin clouds (Cumulus/CU)(Syaifullah et. al., 2016).
DCD = BTD [IR-WV] < 3
Figure 1.Map of the study area; BLW = Belawan, KNO = Kualanamu, SPL = Sampali
Malacca Strait
The time series graph of the cloud peak temperature inFigure 3 represents the stages of convective clouddevelopment around the KNO. Based on the cloud peak
temperature analysis, three stages of convective cloudevolution can be identified, namely the growth stage(10.00 - 13.00 UTC), the mature stage (13.00 - 19.00 UTC),and the dissipation stage (19.00 - 20.00 UTC).
Based on the value of the lowest cloud peak temperature,
this convective cloud is included as a CB with the topcloud temperature value less than -70oC. Analyse from theduration of its development, this convective cloud has alife-time that is relatively longer than its normal time,which is normally about 3-4 hours. When compared to themeasured rainfall graph, the time-series graph of the topcloud temperature is related to graph of rainfall.
Figure 3. Graphs of top cloud temperature from the IR channel of Himawari-8 on 15th September 2018
a
d
c
b
Acknowledgements
The author would like to thank the 9th AOMSUCcommittee. The author also would like to thank the Sub-Section of Satellite Image Management - BMKG andSTMKG Meteorology Laboratory for the availability of dataand facilities used in this study.
