A Weather Forecasting System using concept of SoftComputing: A new approach

Arvind SharmaPG Research Group (M.Tech. CSE)

SATI ,Vidisha(MP) IndiaMail:w

AbstractWeather forecasting and warnings are the major servicesprovided by the meteorological profession.. Manygovernment and private agencies are working on itsbehavior but still it is challenging and incomplete. Wepropose a new technique to construct the learning set ofimages, which represents the actual data. We relate thisdata to the forthcoming weather events based on theirprevious records and history or whatever recognized by oursystem. Our work presents a new approach where the dataexplanation is performed with soft computing technique i.e.a neuro-fuzzy system is used to predict meteorologicalposition on the basis of measurements by a weather systemdesigned by us. This model can help us in making forecastof different weather conditions like rain and thunderstorm,sunshine and dry day, and perhaps a cloudy weather i.e.purpose of this model is to represent a warning system forlikely adverse conditions.

Our model is designed with a concept of AdaptiveForecasting Model(AFD) in the mind. The simple meaningof this term is that our model has potential to capture thecomplex relationships between many factors that contributeto certain weather conditions. The results are comparedwith actual working of meteorological department andthese results confirm that our model which is based on softcomputing have the potential for successful application toweather forecasting.

We considered atmospheric pressure a primary keyparameter and atmospheric temperature and relativehumidity secondary type. We will, however, examinetemperature as signature of weather conditions in sometypical cases where we could observe the effect oftemperature. As an example, the estimations produced bythe proposed methodology were applied on differentweather forecasting data provided by the Gwaliormeteorology center to make the result more practical andbelievable. The forecasts are always current are based onneuro-fuzzy[2] systems that utilize the concept of softcomputing. Real time processing of weather data indicatethat the neuro-fuzzy based weather forecast have shownimprovement not only over guidance forecasts from

Prof. Manish ManoriaDeptt. Of CSE

SATI ,Vidisha(MP) India

numerical models, but over official local weather serviceforecasts as well.

Key Terms: Soft computing,Neuro-Fuzzysystem,Adaptive Forecasting Model etc.1.INTRODUCTION

A method of short term weather forecasting is presented,each sample, which is recorded by our system consists of adate information combined with meteorological data fromthe last 16 or 24 hours gathered at the meteorologicalstation in Gwalior, India. The prediction goal is next day'sweather conditions and warnings, we find the analogous setof meteo events [3]and, taking into account neighbor data,extract suitable information , which may be interpreted asforecast.

The most convenient tool for search for the associativeanalogies between different items of the image is neuro-fuzzy system . This system, after appropriate training, isable to produce weather preconditions almostinstantaneously . On the other hand fuzzy system provideapproximate solutions based on the different category ofmembership.

Current experiment requires two basic atmosphericparameters to co-relate the atmospheric physical conditionsto likely weather outcome . These parameters are -

(i) Atmospheric pressure(ii) Atmospheric temperature(iii) Relative humidity direction(iv) Wind velocity and wind direction

Atmospheric pressure in day and night also changes as timeprogresses and atmospheric temperature changes due toheat from sun rays, and also due to effect of wind andclouds. We may also find that atmospheric pressurepatterns are global in nature and locally part of it isexperienced. The sea surface[I]s produces large amount ofwater vapors due to increased temperature and the earthsurface generates low pressure due to hot gases. We alsofind that wind starts blowing from sea to earth as day heatsup and perhaps a reverse pattern when night cools theearth.

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At gwalior city we are from seashore and are placed at 212m above mean sea surface. As height from sea increases,the atmospheric pressure starts decreasing at about 14 kpa/ Km. Mean atmospheric pressure at sea cost in normalcondition is about 110 Kpa or 1 bar(one atmosphericpressure level ). Changes in atmospheric pressure due toweather conditions are only on mbar scale. Hence,measuring instrument required for the experiment need tohave greater than .1% accuracy. Such instruments are very

expensive and are available only in few reputed researchlabs in India. Present experiments were conducted using a

highly sensitive pressure sensor and temperature sensor atgwalior city.

2. IMPLEMENTATION

To summarize the working of our system we can categorizethe major components[4] in following manner-

1) Data recording scheme2) Parameter selection and user

requirement definition3) Neuro-fuzzy training and prediction scheme4) Generating weather forecasting system

1.Data recording scheme

The experimental setup consists of highly sensitiveatmospheric pressure sensor and atmospheric temperaturesensor . We have linked the sensors to the data loggingcomputer using an interface cable to the parallel portLPT 1. Data acquisition software records data in real time ata fixed time intervals of 4 seconds.

Data precision for pressure sensor is 1 mbar and fortemperature sensor is 0.10 c. System has capability torecord the data continuously for several days without breakin data recording as shown in figure 1.

2.Parameter selection and user's requirement definitionWe considered atmospheric pressure as a primaryparameter and atmospheric temperature and relativehumidity secondary type. Other parameters are alsoconsidered like wind direction and wind velocity.

Atmospheric pressure changes at any given place on earthare minute, hence, a very high sensitivity of pressure sensor

is essential. Atmospheric pressure[3] also changes withaltitude and hence base atmospheric pressure differs fromplace to place depending on the altitude of the place frommean sea level.User requirement involves the collect ion of different inputvalues and variables; including the selection of one or more

forecasting measurements (such as the temperature,pressure, humidity and precipitation etc.), the forecastingrange (as next day or next N days forecast), parameterssuch as the local forecast and distance of altitude from thesea level, and no. of samples per recording and interval of

3. Neuro - fuzzy training and prediction schemeHaving collected and preprocessed all of the relevantweather information, our system starts the appropriatenetwork training and forecasting, which is based on theback propagation in a neuro-fuzzy network as shown infigure2. Table I shows the different categories defined forthe fuzzification of the weather conditions.

Table I

Weather categories

In our experiments , the fuzzy data for predicting theoccurrence of either rain or not rain ,bad weather ,goodweather are based on the above given table I.

While local pressure is measured in absolute scale , forsimplicity of understanding of weather conditions pressure

at sea level is computer with altitude information as

correction factor to predict weather .

The following conditions are to be analyzed --

1. Constant low pressure (Bad weather)

2. Constant high pressure (Good weather)

3. Negative slope(Good to bad weather)

4. Positive slope (Bad to good weather)

5. Dual Slope (Variable condition of weather)

4.Generating Weather prediction

From recorded pressure data the generated weather forecastunder stable low-pressure condition was rainy weather. Asonly two hours record was used for testing, we did notexpect great results from it. Pressure changes recorded atfixed pressure rate of -0.564 mbar/h. It is very small changeand is considered to be constant. This is given in Fig.3

recording etc.

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S.No. Pressure Weather CategoryChange

1 dp>+ 2.5 Intermediate high pressurembar system ,not stable(Very Good

weather)2 - Stable weather

0.5mb/h<dp/dt conditions(Good weather)<0.5 mb/h

3 -2.5mb/h Rain indicator (Bad weather)<dp/dt< -0.5mb/h

4 dp < -2.5 Intermediate low pressuremb/h Thuderstorm, not Stable(Very

bad weather)

3. Experimental results and discussions

From conducted experiments we find the following keychanges in the atmospheric pressure signature that can berelated to dynamic states of atmospheric conditions and formeaningful short duration weather prediction.

We are noticed the following key features inatmospheric pressure patterns that were related tothe weather conditions and indicated trend of thefuture weather of the place. These conditions were:

* Stable day-night pressure gradient - indicator ofstable weather -sun shine

* Sudden pressure fall - indication of likely thunderstorm

* Sudden pressure rise - indicator for windy day

* Change in pressure slope - change of weatherstate in either way

These trends were actually found in the recorded data andthe selection of August month for the experiments wasbenefiting, as there were almost all types of signaturespresent in the atmospheric pressure indicating weatherchanges. These are-

i. Normal daily pressure gradientsa. Midnight to midday patternb. Midday to midnight pattern

ii. Sharp changes in atmospheric pressure meansweather change

iii. Step functions in pressure trendiv. Rain & temperature dip

Experiments were carried out on different locations of thedifferent city. The performance of the experiments fromstations having short difference from the sea level in theform of altitude is the best. These observations were carriedout on the time series basis. The results for a single stationor multiple stations do not produce a large difference inperformance. The best result that is achieved by our modelis due to the availability of a large amount of input data forthe model to select the right variables[2]. Thus the modelhas a greater chance of producing better prediction results.It is found and can be deduced that the correlation betweenthe data at time t and t+1 is high; therefore, it is easier tobuild a successful model.

5.Conclusion

In this paper, we introduce an innovative, intelligent softcomputing based platform. Through the implementation ofthis system, we illustrate how an intelligent system can beefficiently integrated with a neuro-Fuzzy prediction modelto implement an online weather information retrieval,analysis, and prediction system by using electronic sensors.

Data recording used at 4 samples per second [2]wasadequate to see minute changes in atmospheric pressureand temperature trends . Perhaps sampling at every minuteinterval might have been all right as atmospheric conditionsdo not change very fast. At some places in bad weather,atmospheric conditions perhaps can change faster, hence,the instrument used for data recording did not miss anysuch signature and we find no abrupt changes.

Perhaps increasing parameters for weather modeling mayhelp in predicting weather changes to greater extend incomparison with than simple model used in present case.

COMPUTER

ATMPSPHERICPRESSUREANDTEMPERATURESENSORSI4

INTERFACECABLE

Figure 1: Schematic diagram of data acquisition system for measurement of atmospheric pressure and temperature.

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4.Model performance

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Fig2. Schematic diagram for the Neuro-fuzzy network for weather forecasting using the following data: Relative.I *- -1ZT/T -\ l l 'I- * -11r11, r-I I 1-v1 \o A I * T/A-\

References

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2. Ochiai K; Suzuki, H; Tokunaga ,Y " Snowfall and rainfallforecasting from the images of weather radar with artificial neuralnetworks" Neural networks for Signal processing [1996] VI.Proceedings of the 1996 IEEE Signal Processing Society workshop ,4-6sep. 1998 page9s) 473-481

3. McCullagh ,J; Bluff,K; Ebert,E; " A Neural Network Model forrainfall estimation" Second New Zeland international two streamconference on 20-23 Nov. 1995 page(s): 389-392

4. Roe ,K; John,H. Nelson " High Resolution Numerical WeatherForecasting to aid AMOS " User group conference 2003 proceedings,2003 page(s)275-280

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