Volume 2,
No. 10 Oktober 2024 p-ISSN 3032-3037
| e-ISSN 3031-5786
STUDY OF AIR STABILITY INDEX THRESHOLD VALUES IN CUMULONIMBUS AND
THUNDERSTORM CLOUDS AT HASANUDDIN METEOROLOGICAL STATION
Agusmin Hariansah1,
Jasruddin2, Agus Susanto3
Makassar State University
Email : agustuscepat@gmail.com1 , jasruddin@unm.ac.id2
Abstract
The purpose
of this study was to analyze the characteristics of the current air stability
index threshold value for Cumulonimbus (CB) and Thunderstorm (TS) cloud events
and to analyze the performance of the current air stability index threshold
value for CB and TS cloud events with previous research at Hasanuddin
Meteorological Station. The analysis of the characteristics of the air stability
index threshold value in this study used Showalter Index (SI), K Index (KI),
Total - Totals (TT) and Convective Available Potential Energy (CAPE) data for
the 2013-2022 period with the Sturges method to determine the class interval
obtained from the total frequency of events and then divided the class with the
largest number of events in the resulting value range. Furthermore, the
performance analysis of the current research air stability index threshold
value used 2023 data with the 2x2 contingency table verification test method.
The results of the analysis show that the characteristics of the air stability
index threshold value in this study in this case the SI, KI, TT and CAPE
indices for CB and TS events during 2013 to 2022 at Hasanuddin Meteorological
Station have changed or shifted to the manual or previous research. Changes or
shifts in current research occur more, especially in the lower threshold value
in the new range or threshold value with consecutive values, namely ≥
(-5), ≥ (13), ≥ (32), ≥ (1). This indicates that the
frequency of CB and TS cloud occurrences at the research location against the
indices used, namely SI, KI, TT and CAPE, still has quite an influence,
especially on the lower threshold value or initial potential when CB and TS
clouds form. Then related to the performance analysis of the air stability
index threshold value, this study was generally not significant when tested
using a verification test in the following year (2023). However, if reviewed
further regarding performance based on the time of occurrence, it is known that
the new threshold value produced in the current study is quite consistent with
previous research where the sensitivity of the index in predicting short-term
weather conditions during the day is better than at night.
Keywords: Threshold
Value, Air Stability Index, SI, KI, TT, CAPE, Cumulonimbus and Thunderstorm
Introduction
Indonesia is a country with a
very complex atmospheric dynamics because there are several factors that
influence it, including: solar activity, ENSO (El-Nino and Southern
Oscillation), DMI (Dipole Mode Index), MJO (Madden-Julian Oscillation), Monsoon
Winds and other local factors that interact with each other (Fatmasari
& Ariandi, 2014).
In general, the results of the
interaction of several factors cause the potential for bad weather. According
to (Hidayat,
2015), bad weather is an abnormal,
unusual weather event that can result in loss of life and property. One of the
bad weather conditions that often occurs is heavy rain and thunder storm.
Heavy rain occurs due to high
rainfall factors originating from convective clouds such as Cumulonimbus clouds
(Hidayah,
QA, Bimaprawira, AK, Yulitamora, NR, Nugraheni, IR, Deranadyan, 2019). While thunderstorms are a
mechanism for releasing electrical charges originating from convective clouds
which are characterized by lightning and thunder (Meilani,
Wahid A., 2014). Based on the definitions of
several researchers, it is generally stated that heavy rain and thunderstorms
are one form of events produced by convective clouds such as Cumulonimbus.
This very complex atmospheric
dynamic condition is a challenge especially for forecasters in making weather
forecasts. According to (Zakir,
Khotimah, & Sulistya, 2010) to predict the weather
requires parameters on a local scale or data analysis based on a single station
by utilizing data from upper air observations.
One of the uses of the results
of processing upper air observation data isuse of the
Air Stability Index. This is because the index is able to represent the
vertical conditions of the atmosphere that occur in a certain time span and
radius of the region (Veefkind
et al., 2012). Currently, the value that is
often used as the threshold for the Air Stability Index in the theory in the
book The Use of The Skew T, Log P diagram in Analysis and Forecasting cannot be
fully used as a basic value (Widadi
& Pratama, 2023). This is because the
characteristics of each region are different, so further research is needed to
determine the appropriate value in a region.
Research related to
determining the threshold value of the Air Stability Index has been carried out
especially in several regions of Indonesia (Putra
et al., 2022). In general, research related
to the upper air is more concentrated in areas or regions that carry out upper
air observations. The Meteorology, Climatology and Geophysics Agency (BMKG) is
one of the government agencies that conducts upper air observations using Pilot
Balloons and Radiosondes which are carried out directly by the Technical
Implementation Unit (UPT) in the region.
Research related to the study
of the Air Stability Index threshold has been conducted by (Bangsawan,
2015) with the research location in
Makassar using data from the Hasanuddin Meteorological Station to see the
Showalter Index (SI) and K Index (KI) with the research results obtained
respectively in the range of - 0.1 - 3.0 and 30.1 - 38.0. While the research
conducted by (Hariansah,
A., 2016) at the same location but
using the Convective Available Potential Energy (CAPE) Index and Total-Totals
(TT) obtained values respectively in the range of ≥ 500 and ≥ 40.
The index values obtained from
previous researchers are the basis for this study to conduct further studies on
the threshold values of the air stability index, especially on SI, KI, CAPE and
TT in Cumulonimbus (CB) and Thunderstorm (TS) cloud events over the past 10
years (2013 to 2022) at Hasanuddin Meteorological Station. The addition of SI
and KI indices in this study was carried out to determine the stable conditions
or not of the middle atmosphere and how much potential there is for CB clouds
to arise around the research area. While the TT and CAPE indices are each used
to identify the opportunities for CB and TS clouds to occur and the amount of
energy available in the air column to lift air parcels in this case in
Cumulonimbus clouds.
Research Methods
This research is a type of qualitative
descriptive research, namely research that is included in the type of
qualitative research. The purpose of this research is to reveal facts,
circumstances, phenomena, variables and conditions that occur when the research
is running and present them as they are. Qualitative descriptive research
interprets and narrates data related to the current situation.
The data used in
this study is data sourced directly from the Hasanuddin Meteorological Station
UPT office, which includes:
1. Air Stability Index data for the last 10 (ten) years
(2013 - 2022) at observations at 00 UTC and 12 UTC. The data is used to obtain
the SI, KI, TT and CAPE Index values.
same time period, namely from 2013 - 2022. This data is
used to search for Cumulonimbus (CB) and Thunderstorm (TS) cloud events.
Air Stability Index Data
The Air
Stability Index data used in this study are the Showalter Index, K Index, TT
Index and CAPE for the last 10 years (2013 - 2022). Currently, processing to
obtain Air Stability Index data is still being carried out at the Hasanuddin
Meteorological Station office because the latest application can only operate
on special devices from the office that carries out Pilot Balloon and
Radiosonde observation activities.
2. Surface Weather Observation Data
Synoptic data processing is done by searching
for Cumulonimbus cloud events accompanied by rain, thunderstorms or not on the
Me.45 and Me.48 forms. On the Me.45 form, it is done by looking at the low
cloud type code (8NhClCmCh) (Table 3.1). While on the Me.48 form as an
attachment to see the current weather conditions (present weather) and SPECI
which shows more clearly the time of occurrence of Cumulonimbus clouds with or
without thunderstorms.
Table 1 Low Cloud Type Codes in Me.45 (Table
(a)) and Present Weather Codes in Me.48 (Table (b)) (Source: Manual on Codes
WMO-No. 306)
Table (a)
|
Cl |
Type of low clouds |
|
3 |
Cumulonimbus without anvil, accompanied/not accompanied by cumulus sc/stratus. Cumulonimbus is based on cumulus, stratocumulus or stratus |
|
9 |
Cumulonimbus is based on cumulus, stratocumulus or stratus |
Table (b)
|
wow |
Current Weather |
|
13 |
lightning visible, no thunder heard |
|
17 |
thunderstorm, but no precipitation at the
time of observation |
|
29 |
thunderstorm (with or without
precipitation) |
|
91 |
slight rain at time observation
(thunderstorm during the preceding hour) |
|
92 |
moderate rain at time observation
(thunderstorm during the preceding hour) |
|
95 |
thunderstorm, slight or moderate, without
hail, but with rain at time of observation |
|
97 |
thunderstorm, heavy, without hail, but with
rain at time of observation |
In this sub- chapter , we will explain more clearly the data processing
techniques in the research that will be carried out with the following
framework:
1. Upper air observations are carried out using Radiosondes
to measure air temperature, relative humidity and wind direction and speed at
certain altitude levels.
2. Output data obtained from Radiosonde observations in
code format which is then processed using the RAOB Application.
3. Collecting Air Stability Index data to be used, namely
Showalter Index, K Index, Total-Totals Index and CAPE.
4. Grouping the conditions of CB and TS cloud events from
the daily actual weather report (present weather) against the predetermined
stability index.
5. Determine the threshold for the SI Index, K Index, TT
Index and CAPE parameters for CB and TS events using the frequency distribution
method based on Sturges' rules (Sugiyono, 2017).
a.
Determine the Number of
Classes.
k = 1 + 3.322 log n (1)
Description:
k = number of classes
n =
number of data
b.Determine
the interval of each class
Range (
R ) = X n – X 1 (2)
Description:
R = area of distribution ( Range )
X n = highest
observed value
X 1 =
lowest observed value
Class Interval =
6. Conduct a
comparison of the new threshold with the manual and previous research on the
specified weather conditions.
7. Perform
verification.
8. Conduct
interpretation and analysis of atmospheric dynamics.
Analysis
Using Verification Method
Analysis using the verification method is
needed to determine the consistency of the values obtained. The first data
analysis flow is to compare the results of the threshold value with previous
studies by conducting trials or applications on new thresholds of the SI index
parameters, K index, TT index and CAPE index against severe weather events
during 2013 to 2022. The next step is to verify using the same data in 2023 to
see the performance of the resulting threshold using Table 3.2 with a 2 x 2
format to see the statistical values obtained. One of them used in this study
is the accuracy value (Gustari, Hadi, Hadi, &
Renggono, 2012).
Table 2 Contingency
Table for Verification
|
|
|
Observation |
|
|
|
|
Yes |
No |
|
Forecast |
Yes |
Hits |
False Alarm |
|
No |
Misses |
Correct Negatives |
|
Accuracy is the part of the forecast that is correct overall. The accuracy value is between 0 and 1. The best forecast is when the accuracy approaches the value 1. Accuracy can be calculated using the following equation (3.4):
(4)
Interpretation
Interpretation is done with the aim of understanding and
explaining the meaning of information or data that has been collected or
analyzed. In addition, Interpretation also involves the process of extracting
information contained in the data, identifying patterns or trends and making
conclusions that can be used to make decisions or provide deeper insights.
will later become the basis for information or data
obtained to make decisions regarding the performance of the results of the
study before and after as well as various things that need to be revealed in
this research.
Atmospheric
Dynamics Analysis
Further analysis of the characteristics of atmospheric
dynamics on the resulting index values is quite important information in this
study. The purpose of the atmospheric dynamics analysis is to see how much
influence the resulting index values have on the occurrence of Cumulonimbus
clouds and Thunderstorms.
Results and Discussion
Research result
Results of Air Stability Index
Processing
The air stability index data obtained
in this study is the result of data processing from Radiosonde observations through
the RAOB application and has been adjusted based on the observation time
against the specified weather event conditions, namely the Cumulonimbus (CB)
and Thunderstorm (TS) cloud events.
a. SI Index for Cumulonimbus Cloud
(CB) Occurrence
Table 3 SI Index of CB Cloud Events
|
Explanation |
Symbol |
Results |
|
Amount of Data |
N |
1389 |
|
The highest score |
MAX |
16.06 |
|
Lowest value |
MIN |
-12.55 |
|
Range |
R |
28.61 |
|
Number of Classes |
K |
11 |
|
Class Width |
P |
3 |
|
Class |
BB |
BA |
Freq |
|
1 |
-13 |
-10 |
1 |
|
2 |
-9 |
-6 |
0 |
|
3 |
-5 |
-2 |
47 |
|
4 |
-1 |
2 |
1083 |
|
5 |
3 |
6 |
238 |
|
6 |
7 |
10 |
17 |
|
7 |
11 |
14 |
2 |
|
8 |
15 |
18 |
1 |
Meanwhile, Table 4 uses 1118 SI
Index data for Thunderstorm (TS) events, and the highest SI index value
is 20.18 and the lowest value is -4.38. The highest frequency of occurrence of
the SI Index for TS cloud events is known to occur in the range of values (-1
to 1) of 648 events.
B.
K Index for Cumulonimbus Cloud (CB) Events
Table 5 K
Index Against CB Cloud Events
Furthermore, in Table 5, it is
known that there are 1389 K index data used to identify Cumulonimbus Cloud (CB) events . From these data, the highest K index
value is obtained at 43.6 and the lowest value is obtained at -5.9. The highest
frequency of occurrence of the K index against CB cloud events is known to
occur in the range of values (34 to 38) of 747 events
K Index
against Thunderstorm (TS) Events
Table 6 K Index Against TS Events
|
Explanation |
Symbol |
Results |
|
Amount of Data |
N |
1118 |
|
The highest score |
MAX |
42.5 |
|
Lowest value |
MIN |
-56.9 |
|
Range |
R |
99.40 |
|
Number of Classes |
K |
11 |
|
Class Width |
P |
9 |
|
Class |
BB |
BA |
Freq |
|
1 |
-57 |
-48 |
1 |
|
2 |
-47 |
-38 |
0 |
|
3 |
-37 |
-28 |
0 |
|
4 |
-27 |
-18 |
0 |
|
5 |
-17 |
-8 |
0 |
|
6 |
-7 |
2 |
1 |
|
7 |
3 |
12 |
4 |
|
8 |
13 |
22 |
35 |
|
9 |
23 |
32 |
303 |
|
10 |
33 |
42 |
771 |
|
11 |
43 |
52 |
3 |
Based on Table 6 above, it can
also be seen and known that the total number of incident data on the K Index
used to identify Thunderstorm (TS) events is 1118 data. Then from the
total incident data, the highest K index value is obtained, which is 42.5 and
the lowest value is obtained, which is -56.9. The highest frequency of
occurrence on the K Index against TS events is known to occur in the range of
values (33 to 42) of 771 events.
f. TT Index for Cumulonimbus Cloud (CB) Events
Table 7 TT Index Against CB Cloud Events
Based
on Table 6 above, it can also be seen and known that the total number of
incident data on the K Index used to identify Thunderstorm (TS) events
is 1118 data. Then from the total incident data, the highest K index value is
obtained, which is 42.5 and the lowest value is obtained, which is -56.9. The
highest frequency of occurrence on the K Index against TS events is known to
occur in the range of values (33 to 42) of 771 events.
TT Index for Cumulonimbus
Cloud (CB) Events
Table 7 TT Index Against CB Cloud Events
In Table 7, the number of TT Index
data used for Cumulonimbus (CB) cloud events is 1389 data. The results of the analysis show the highest TT Index value
of 83.4 and the lowest value obtained is 26.46. The highest frequency of occurrence
of the TT Index for TS cloud events is known to occur in the range of values
(38 to 43) of 710 events.
TT Index against Thunderstorm (TS) Events
Table 8 TT Index Against TS Incidents
|
Explanation |
Symbol |
Results |
|
Amount of Data |
N |
1118 |
|
The highest score |
MAX |
50.3 |
|
Lowest value |
MIN |
-3.6 |
|
Range |
R |
53.90 |
|
Number of Classes |
K |
11 |
|
Class Width |
P |
5 |
|
Class |
BB |
BA |
Freq |
|
1 |
-4 |
1 |
1 |
|
2 |
2 |
7 |
0 |
|
3 |
8 |
13 |
0 |
|
4 |
14 |
19 |
0 |
|
5 |
20 |
25 |
0 |
|
6 |
26 |
31 |
2 |
|
7 |
32 |
37 |
14 |
|
8 |
38 |
43 |
550 |
|
9 |
44 |
49 |
547 |
|
10 |
50 |
55 |
4 |
Meanwhile,
in Table 8, it can be seen that the number of TT Index data used for Thunderstorm
(TS) events is 1118 data where the highest TT Index value is 50.3 and the
lowest value is -3.6. The highest frequency of TT Index occurrences for TS
events is known to occur in the range of values (38 to 43) of 550 events.
CAPE Index for Cumulonimbus Cloud (CB) Events
Table 9 CAPE Index Against CB Cloud Events
|
Explanation |
Symbol |
Results |
|
Amount of Data |
N |
1389 |
|
The highest score |
MAX |
11829.77 |
|
Lowest value |
MIN |
0 |
|
Range |
R |
11829.77 |
|
Number of Classes |
K |
11 |
|
Class Width |
P |
1040 |
|
Class |
BB |
BA |
Freq |
|
1 |
0 |
1040 |
1143 |
|
2 |
1041 |
2081 |
214 |
|
3 |
2082 |
3122 |
28 |
|
4 |
3123 |
4163 |
2 |
|
5 |
4164 |
5204 |
0 |
|
6 |
5205 |
6245 |
1 |
|
7 |
6246 |
7286 |
0 |
|
8 |
7287 |
8327 |
0 |
|
9 |
8328 |
9368 |
0 |
|
10 |
9369 |
10409 |
0 |
|
11 |
10410 |
11450 |
0 |
|
12 |
11451 |
12491 |
1 |
Then
in Table 9 the number of CAPE Index data used for Cumulonimbus (CB) cloud events is 1389 data. The results of the analysis
show that the highest CAPE index value is 11829.77 and the lowest value
obtained is 0. The highest frequency of CAPE Index occurrence for CB cloud
events is known to occur in the range of values (0 to 1040) of 1143 events
CAPE Index for Thunderstorm Events (TS )
Table 10 CAPE Index Against TS Events
|
Class |
BB |
BA |
Freq |
|
1 |
0 |
309 |
521 |
|
2 |
310 |
619 |
238 |
|
3 |
620 |
929 |
138 |
|
4 |
930 |
1239 |
83 |
|
5 |
1240 |
1549 |
64 |
|
6 |
1550 |
1859 |
33 |
|
7 |
1860 |
2169 |
17 |
|
8 |
2170 |
2479 |
15 |
|
9 |
2480 |
2789 |
5 |
|
10 |
2790 |
3099 |
2 |
|
11 |
3100 |
3409 |
1 |
|
12 |
3410 |
3719 |
1 |
|
Explanation |
Symbol |
Results |
|
Amount
of Data |
N |
1118 |
|
The
highest score |
MAX |
3,419.86 |
|
Lowest
value |
MIN |
0 |
|
Range |
R |
3,419.86 |
|
Number
of Classes |
K |
11 |
|
Class
Width |
P |
309 |
Furthermore, in Table 10, the
number of CAPE Index data used for Thunderstorm (TS) events is 1118
data, so the highest SI index value is 3419.86 and the lowest value is 0. The
highest frequency of CAPE Index occurrence for TS events is known to occur in
the range of values (0 to 309) of 521 occurrences.
The air stability index against
weather conditions in CB and TS events has produced index values that represent
the range of values at which the weather event in question can begin to occur.
The shift in the values produced will certainly be one of the considerations
that there are other external factors that can affect weather conditions,
especially in the research area.
Discussion
Studies related to the threshold value of the
air stability index, especially in Indonesia itself, have been conducted in
several regions such as Jakarta, Surabaya, Kupang,
Medan, Manado, Biak and Makassar. This was done by several researchers
considering that the threshold value of the index that is currently the
reference has not been fully able to represent the number of frequencies that
always occur from the index value produced when processing data from Radiosonde
observations because there are several external factors that are quite
influential so that ongoing research is needed, especially in research related
to the threshold value of the air stability index in each region, especially in
Indonesia.
In this discussion, the researcher tries to
conduct further research by looking at the performance of several previous
studies and current research on the guidebook which is still a general
reference as a measuring point for the potential for bad weather events in this
case related to the formation of Cumulonimbus clouds and Thunderstorms.
The results of this study indicate that in
general there are changes that occur between the guideline for threshold values
in the current study and previous studies. Changes or shifts occur more in the
lower threshold values and upper threshold values. This is certainly very
possible because the measurement of the upper air through Radiosonde which is
carried out continuously every day also produces different data depending on
the atmospheric conditions at that time.
In previous research conducted by (Bangsawan, 2015), it was found that the shift in values towards
the guidebook was already visible with the threshold values on the SI and KI
indices, which were respectively in the range (-0.1 ) to (3.0) and (30.1) to
(38.0). While in the guidebook, the threshold values on the SI and KI indices
were known, which were respectively in the range (-3) to (-6) and (31) to (40).
Then the current research was conducted to see the consistency of the threshold
values produced in the previous research but by adding the latest data in the
following year, namely until 2022.
The results of the study revealed that current
research also tends to experience changes or shifts where the values produced
in the SI and KI indices are respectively at values (-5) to (7) and (13) to
(43). This also shows that the pattern of events based on the SI and KI indices
for the formation of cumulonimbus clouds and thunderstorms has shifted,
especially in the lower and upper threshold values.
Furthermore, the
same study but using other air stability indices, namely the TT and CAPE
indices conducted by (Hariansah,
A., 2016) was also found to have experienced changes or
shifts where the study produced threshold values for the TT and CAPE indices of
≥ 40 and ≥ 500 respectively. Meanwhile, in the current study, there
was a shift again but tended to experience a shift or lower threshold value
where more incidents occurred in the lower limit value range of the CAPE index,
namely (≥1).
When compared to the manual, the threshold
values for the TT and CAPE indices, especially at the lower limit where the
formation of cumulonimbus clouds and thunderstorms begins, are at TT ≥ 44
and CAPE ≥ 1000 respectively. Thus, the shift in the lower threshold
formed in the current study illustrates that the frequency of occurrence,
especially for the TT and CAPE indices, does not require large energy in the
formation of Cumulonimbus (CB) and Thunderstorm (TS) clouds.
The range of values produced by previous
research and current research has a fairly wide class because the index value
produced during processing in the RAOB application also has a fairly wide range
of values, both too low and too high compared to the average index value of the
manual. It should be noted that the RAOB application has quite complex settings
or arrangements so that it can increase the accuracy value of an index produced
by taking into account other external factors so that this study tends to look
more at the justification of the initial value (lower limit) which is obtained
through the air stability index which is able to represent atmospheric
conditions that are predicted to cause Cumulonimbus clouds and Thunderstorms.
In addition, the comparison of the criteria or
classification values of an index sourced from the manual against previous
research and current research has not been known about how the manual measures
an index against the intensity of events, especially CB and TS cloud events
because observations of CB or TS clouds are only done visually and reported
only as far as their existence. Meanwhile, to find out whether the CB or TS is
included in the weak, moderate, or strong category based on the range of values
produced by previous research or current research does not yet have a strong
basis so that in general it only looks at the initial value (lower limit) of
the index of how many CB and TS events can be formed.
Furthermore, verification was carried out on the
manual, the results of previous studies and current research to see how the
performance of the four indices, namely SI, KI, TT and CAPE, was against CB and
TS events using 2023 data. The verification results showed that the threshold
value produced in the current study was not significant enough because the data
used was still very little so as to determine the results of the performance of
each index, namely SI, KI, TT and CAPE, against CB and TS events. However, if
reviewed further based on the time of the incident, it is known that the best
performance was produced during the incident during the day compared to at
night and this is consistent with the results of previous studies which stated
that the sensitivity performance of the air stability index was quite good
during the day.
In physics, the formation of convective clouds
during the day occurs faster due to increased heating of the earth's surface by
solar radiation. In general, a combination of other physical influences ranging
from surface heating, convection, atmospheric instability, and local factors
such as surface type and air humidity are needed, making the formation of
convective clouds occur faster during the day and all are interrelated. This
process usually produces cumulus clouds that can develop further into cumulonimbus
clouds where if atmospheric conditions are favorable, they can cause
thunderstorms.
Conclusion
Based on the
results and discussions that have been described, it can be concluded that: 1. The
characteristics of the Air Stability Index threshold value in the current study
in terms of the SI, KI, TT and CAPE indices against cumulonimbus (CB) and
thunderstorm (TS) events during 2013 to 2022 (10 years) at the Hasanuddin
Meteorological Station have experienced changes or shifts to the manual or
previous studies. Changes or shifts occur more, especially in the lower
threshold value in the new range or threshold value with consecutive values,
namely ≥ (-5), ≥ (13), ≥ (32), ≥ (1). This indicates
that the frequency of CB and TS cloud occurrences at the research location
against the indices used, namely SI, KI, TT and CAPE, still has quite an
influence, especially on the lower threshold value or the initial value when CB
and TS events begin. 2. The performance of the current research Air Stability
Index threshold value is generally not significant when tested using a
verification test in the following year (2023). However, if reviewed further
regarding the performance based on the time of the incident, it is known that
the new threshold value produced in the current study is quite consistent with
previous studies where the sensitivity of the index during the day is better at
predicting short-term weather conditions than at night.
The sensitivity of
the air stability index during the day is better than at night due to more
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Copyright holder: Agusmin
Hariansah, Jasruddin,
Agus Susanto (2024) |
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First publication right: Advances in Social Humanities Research |
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