Application Of Simple Wind Model For Weather Modification Activity
In Indonesia*GAME International Project Office1 , Endarwin2( 1: HyARC, Nagoya University, 2: Meteorological And Geophysical Agency Indonesia , MGA Building
4th floor Jl. Angkasa I No. 2 Jakarta, Indonesia 10720. e-mail : endarwin@lycos.com )* GAME International Project Office (GIPO), HyARC, Nagoya University, Japan, 464-8601, Furo-cho,Chikusa Nagoya, Japan. e-mail : game@hyarc.nagoya-u.ac.jp
Abstract
Weather modification activity in Indonesia to overcome drought impact or to accelerate condensation toproduce rain, usually is worked every year especially in transition period between rainy season and dry season or theopposite.
One of the important factor that can support this activity by using of simple wind model that can give theinformation about the atmospheric condition especially about the possibility of growing cloudy area (cloudy potency)
and atmospheric instability in micro until meso scale area with analog analysis. By this model, the limitation of upper
wind data spreading as a consequence of limitation number of observation points can be overcome without spend highcost with good accuracy.
By this model, the meteorologist can give suggestion or decide where the flight engineer should spread the saltbrought from land to the air by an aircraft. So that the effectiveness and the result of expected will be better.
To develop this model is needed minimum number of upper air observation point that can coverage orrepresentatively for all area will be managed. In this event usually provided at least 4 points observation that located inirregular place of all area. From 4 points, can be increased become 5 or more, depend on the necessity by interpolationafter pass the scalar processing.
The interpolation method that will be used is Kriging Interpolation Method that can interpolate the scalar value
in irregular to irregular place or from irregular to regular place. In this chance will be develop the new value from
irregular to regular place in grid shape. That is from 4 or 5 points in irregular became 80 points in regular position.
So from this result the wind condition in the atmosphere can be viewed clearly and easier to be analyzed. And
finally determination of cloudy potency area and instability area base on wind pattern in every layer can be worked
accurately.
Keyword : weather modification, wind model analysis, Interpolation

1. Introduction
1.1. Background
When an aircraft in weather modification
activity will spread salt (NaCl) powder in the atmosphereto accelerate condensation as shown in figure 1, ofcourse it need good information about the destination
area that will be spread before it flies, in order to give
good result matching with planned.

?
Salt (NaCl)
Powderthe result of direct observation in ground or in theaircraft while it flies before spread the salt. By this way,
the cloudy potency area or instability area as thedestination
is rarely known first because it more
dominated by view in observation that can get cell cloudas the instability result than as an analysis result.
Besides that actually the observation ofatmosphere condition is also supported by pilot balloon(pibal) observation. By this way the approach detectionof atmospheric instability on the one place can be done.
But unfortunately the amount and distribution of pibal
observation points are still restricted compared withlarge area will be managed, whereas the increasing ofpibal observation point will need more cost.
To overcome these problems, in this chance
will be offered the method that can support the weather
modification activity by using application of simple
wind model. By this model all the limitedness data
distribution of observation and instability area detection
can be overcome without spend high cost but can give
good result.

1.2. Objective
Fig. 1 : Aircraft is spreading the salt on destination area
The destination area usually is decided base on

To give amenity, increase of amount and data
distribution especially for wind data and also improve
quality in analysis of atmosphere condition so that can

be yielded the information which is good for efficacy of
weather modification activity without spend a lot of
costly expense.

2. Method
2.1. Source of Data
To develop simple wind model in this event isrequired wind data in the form of direction and speed ofwind from some points that available in the area will be
manage, and usually available 4 points in irregular place.
And the wind data from these points is called data
source.
As shown in figure 2, there are 4 or 5 points
data source (black marked) and then will be increased
become 80 points to regular place in grid shape. The
location is around region of Bandung in West Java
Indonesia, with large area will be managed around 5000
km square.

2.3. Analysis
Analysis to model of this wind is done
analogously, where through this analysis is searched by
the possibility of existence of pattern of wind enabling
for the happening of updraft or instability.
The pattern of wind that enable for the updraft
condition so that yield cloud or precipitation is theconvergent or confluence (Trewartha & Horn, 1980), andthe opposite will not yield cloud but clear condition
relatively. Hence, finally at the picture of wind model
will be searched the pattern of that wind .
To facilitate determination result of analysis,hence can be used the map in figure 2 that divided in 4
region as shown in figure 3.

Fig. 3 : Dividing of scope area to facilitate result ofanalysis in four region

Fig. 2 : Location of observation points as data source
In figure 2, also can be seen there are someriver and dam or barrage that representing goals area of
this event. Where the flow and debit of water in thosegoals area usually will decrease in dry season and in theend can give bad impact to many people life.

By this way, every result of analysis can be shown moreexactly, in region I, II, III or IV, besides by thecoordinate.

3. Result2.2. Data Processing

After conducted by interpolation process fromevery existing data source hence obtainable in the end
picture from expected wind model.
Some examples of wind model picture can beseen in figure 4, 5, 6, 7 below.

After get source data the next step is process thedata by interpolation. The method of interpolation
would be used is Kriging method. This method has
assumption that the parameter being interpolated can betreated as regionalized variable. A regionalized variable
is intermediate between a truly random variable and acompletely deterministic variable in that it varies in acontinues manner from one location to the next andtherefore points that are near each other have a certain
degree of spatial correlation, but points that are widely
separated are statistically independent (Davis, 1986).
So, all new value in regular position base on theweight factor of the all data source in every irregular
position, and the sum of all weight factor (W1 ,W2 , ……
, Wn ) is equal to 1 for each new value in each position.
Because wind is vector, so before processedmust be separated between the value of direction andvalue of speed, and finally will be got 2 scalar values.
So, that will be processed is the scalar value of the wind.

Fig. 4: Picture model of wind that shown convergence
pattern especially in region II and III

And all the pictures represent example of wind
pattern which is possible formed at the time ofperception conducted such as those which have been
explained previously at section 2.3.

4. Discussion

Fig. 5: Picture model of wind that shown confluence
pattern especially in region III, IV and convergence
pattern in border of region I and II

Base on the results in section 3, wind model
which is there are in figure 4, 5, 6 and 7 can give clear
picture to the meteorologist about atmosphere condition
on area that will be managed.
All of picture in section 3 are the pictures ofwind condition in same level that is 5000 feet but indifferent time (different day). From those picture can bedetermined the possibility area of updraft or instability
like convergence or confluence and the opposite can beshown in each region in more precise, so that can be
determined when and where the aircraft spread the salt .
By this way of course this effort is hoped more effectiveand efficient.
Ordinary resistance in making and conducting
analysis by this model is difficulty to get good data fromevery observation point (data source) in each level ofpibal observation. For example, sometimes in oneobservation point there is not data (missing) in one ormore height level of observation whereas in the otherpoint is available. To overcome this problem, of course
the observation method by pibal should be changed byanother way that more good like use radiosonde.
But fortunately so far, although there islimitedness to get source data completely but the windmodel which is resulted, still can good contribution tothis activity.

5. Conclusion

Fig. 6: Picture model of wind that shown divergence
pattern especially in region I, IV and convergence
pattern in region II and III

The limitedness of amount and distribution ofupper air data observation at every level in weather
modification activity can be overcome by simple windmodel, even this wind model can give more benefit andsupport for success this event.
To improve wind model quality is needed gooddata source as input. So that the method of upper airobservation in this event if possible should be improved
too.

References
Trewartha, G.T and Horn, L.H, An introduction to Climate,
Mc Graw-Hill Company, 5th edition, 1980.
Davis, J.C, Statistic and data analysis in Geology, John Wiley& Sons Inc, 2nd edition, 1986.
Dorsel, D and La Breche, T, Kriging, Enviromental Samplingand Monitoring, Virginia, 1997.
Anonymous, Kriging, Enviromental Modeling System Inc,1997
Endarwin, Overcoming limitation of rainfall data distribution
on micro scale area by simple interpolation, J.
Meteor and Geoph, 5, 1411-3082, 2004
Endarwin, Utilization of simple wind model to analysis
atmosphere condition on meso scale area, Proceedingof seminar for 54th Meteorological day, 2004

Fig. 7: Picture model of wind that shown diffluent
pattern especially in region II, III and IV