Thunderstorm Detection and its Equivalent Knowledge Representation with Wavelength Based Image Segmentation

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1 Thunderstorm Detection and its Equivalent Knowledge Representation with Wavelength Based Image Segmentation Miss Vasanti Y. Gaud M.E student Department of Information Technology Prof Ram Meghe Institute of technology & Research, Badnera-Amravati (INDIA) Prof. Mr. S. S. Kulkarni Associate professor Department of Information Technology Prof Ram Meghe Institute of technology & Research, Badnera-Amravati (INDIA) Abstract Thunderstorm is a vicious, climatic disturbance that is associated with heavy rains, lightening, thunders, thick clouds and gusty surface winds. Thunderstorms take place when a layer of warm and moist air rises to a larger extent, and updrafts to the cooler regions of the atmosphere. The updraft that contains moisture condenses in order to form massive cumulonimbus clouds and eventually leads to the formation of precipitation. Columns of frozen air then sink earthward, striking the ground with strong downdrafts and horizontal winds. Meanwhile, electrical charges mount up on cloud particles and causes lightning. This further heats the air in a fierce manner by which shock waves are produced, resulting in thunder. Thunderstorms were predicted based on the severity of the sounds of the thunder, statistical test and graphing were the other parameters used for the prediction purpose. In this, many of the researchers proposed various methodologies like STP model, MOM model, CG model, LM model, QKP model, DBD model and so on for the detection. The research work adopted clustering and wavelet transform in order to improve the thunderstorm prediction rate. The study carried on the thunderstorm prediction using clustering and wavelet techniques resulting with higher accuracy. Keywords Clustering, wavelet transform, Image processing, Thunderstorm. I. Introduction: A climatic disturbance that is associated with heavy rains, lightening, thunders, and thick gusty and clouded surface wind is known as thunderstorm. Thunderstorms take place when a layer of warm and moist air rises to a larger extent, and other gases to the cooler regions of the atmosphere. The other gas that contains moisture condenses in order to form large mass cumulonimbus clouds and lead eventually to the formation of precipitation. Columns of frozen air then sink earthward, striking the ground with strong downward air and horizontal winds. Meanwhile, electrical charges rise up on cloud particles and causes

2 lightning. This further heats the air in a fierce manner by which shock waves are produced, resulting in thunder. Cloud lightning frequently occurs as part of the thunderstorm phenomena, which on rigor becomes dangerous to the property, wildlife and population across the world to a major extent. One of the most significant lightning hazards is to the wildfires, as they can even burn the ground surfaces. The behaviour of thunderstorms is subjected to the experience of the forecasters and the analysis of numerical. Thunderstorms prediction based on the harshness of the sounds of the thunder, analytical test and graphing were the other parameters used for the prediction purpose. Thunderstorms are generally formed in any geographic location. The satellite images obtained from Indian Meteorological Department, which predict whether the cloud images produce thunderstorms or not. The input image of the cloud is been taken by the satellite for the experimentation. The image taken by the satellite is the input image. The rapid upward movement of warm shows the result of thunderstorm, moist air, this can be represented in 3 stages named as the developing, maturity, and dissolving stage. The developing stage is when, the storm starts strengthening in this the warm, moist air rises above and gets mixed with the freeze air making the warm air to get colder resulting in condensation. In this stage, the cloud forms larger due to the instability in the atmosphere and moves to the next stage. The maturity stage starts when the storm reach its peak and is well developed, including a strong, dense anvil along with updrafts and downdrafts and in this stage hail may also prevail When a storm does this, it means the storm is very strong and has capability to produce severe weather and tornadoes and in the last stage called the dissolving stage. The storm starts fading away, when the cool downdrafts begin to intensify, the storm begins to dissolve. These downdrafts basically push everything out of the storm. Thunderstorm consists of the four types single cell, multi cell cluster, multi cell line and super cells. Where super cell thunderstorms are strongest and associated with severe weather. II. Literature review:- Himadri Chakrabarty, C.A Murthy, S. Bhattacharya and A. D. Gupta [1] used Artificial Neural Network model which predict Squall thunderstorms by using RAWIND data. Litta A.J, Sumam Mary Idicula and Naveen Francis C [2] adopted multilayer perceptron network model to predict thunderstorms where in, the prediction was done using the data obtained from RSRW flight but this is limited to a particular region rather than the entire world prediction over Kolkata. Harvey Stern [3] used a knowledge based system to predict thunderstorms. Tajbakhsh S, Ghafarian P, and Sahraian F [4] adopted numerical weather prediction model in order to survey thunderstorms. Pinto [5] discussed annual values of thunderstorm days and temperature values in the city of Sao Paulo. Alan Czarnetzki C [6] discussed about nocturnal thunderstorms, which produces heavy rains. David Bright R, Matthew

3 Wandishin S, Ryan Jewell E and Steven Weiss J [7] used a physically based parameter for lighning prediction. Ken Harding [8] discussed the formation of thunderstorms and its aviation hazards. Bill Nisley [9] discussed the formation and anatomy of thunderstorms. To segment the input image into several clusters based on similarity measure where clustering is an efficient technique, one of the similarity metric used is Euclidean distance. For segmenting the image k-means clustering is adopted. Based on various colour factors segmentation is performed to image because of colours possess wavelength values. The segmentation process applied for a thunderstorm satellite image, and the resulted clusters generated. The feature extracted clustered image is analyzed further by applying wavelet transformations. The Haar wavelet transform is adopted for the further analysis where columns by transforming from data space to wavelet space in frequency domain and decomposition is applied to the image in rows. The main aim is to detect the thunderstorms as accurate as possible. Third stage is thunderstorm detection, which also provides wavelength calculation. After the calculation of wavelength it detect the lightning thunderstorm region, as these wavelength calculations can detect whether it is thunderstorm or not. The first stage is to recognize the lightning thunderstorm image. When the input images consist of the thunderstorm image and background image. As these image consist of some noise section, by cropping these thunderstorm image noise is been get removed. As the lightning thunderstorm image consists of the region of interest and minimum of background image. 1. Database creation Start Input Image K-Means Clustering Wavelet Transform III. Proposed Methods: The proposed system consist of the three stages explained below. This first stage is to recognize the lighting area with the help of input image and also enhance the image for detection of thunderstorm. Second stage is the clustering where by using the k-means clustering algorithm we add the accuracy in this stage. It will get the more precise lighting thunderstorm image so that it can easily identify. Image Segmentation End Fig1:- Database Creation The database creation stage which consists of the k-means clustering and wavelet transformation of the image; it is an efficient technique to divide the input image into several cluster. Take the input image of

4 lightning thunderstorm where the k means clustering is done. Where the thunderstorm image consist of the noise, which is been remove by enhancing the thunderstorm image. While clustering is been done to get the perfect thunderstorm image after the deletion of the noise. The wavelet transformation is done to enhance the image. Segmentation is based on different values grouped into the different clusters. Edge are been get calculated in these segmentation, where the edge of thunderstorm is calculated. And the thunderstorm is been stored into the database creation. 2. Thunder Strom Detection Start unwanted noise and enhance thunderstorm image. Image segmentation is done to calculate the edges of the thunderstorm. While this evaluation of edges, the thunderstorm will be detect. Thunderstorm detection will show the pattern of the lightning thunderstorm form the detected thunderstorm image. After the detection of lightning thunderstorm the knowledge representation is done. This shows the data information. It represents the formation of information. Result:- Input Image Image Segmentation Wavelength Calculation Fig 3:-Screen Shot Thunder Strom Detection Knowledge Representation Fig 2:- Thunder Strom Detection The next stage is testing stage where the thunderstorm image is been tested for the detection. Whether these input image is thunderstorm or not. Take the input lightning thunderstorm image from the database. Enhance this input image of lightning thunderstorm, which extract the IV. Fig.4:-Screen Shot Conclusion: As we have proposed the thunderstorm detection, where these detection of

5 thunderstorm image is been done. The thunderstorm image is been enhanced. The extract region of interest of images which gives the perfect image of thunderstorm and get the section of thunderstorm. The wavelength calculation is been done and get the segmented image of thunderstorm. This detection of thunderstorm by wavelength based image segmentation and knowledge representation is also been done. The projected method predicts 75% of the thunderstorm more accurately than the previous method. In future work, weather models can predict the future state of the atmosphere they must know its present state very accurately. It is the primary purpose of a disparate array of weather measurement instruments and platforms and various other special-purpose devices. V. Reference : [1] Himadri Chakrabarty, Murthy, C. A., Sonia Bhattacharya and Ashis Das Gupta, Application of Artificial Neural Network to Predict Squall-Thunderstorms Using RAWIND Data, International Journal of Scientific and Engineering Research, 2013, pp [2] Litta, A.J., Sumam Mary Idicula and Naveen Francis C, Artificial Neural Network Model for the Prediction of Thunderstorms over Kolkata, International Journal of Computer Applications, 2012, pp [3] Harvey Stern, Using A Knowledge based System to predict Thunderstorms, Bureau of Meteorology, Australia. [4] Tajbakhsh, S., Ghafarian, P, and Sahraian, F., Instability Indices and Forecasting Thunderstorms: the case of 30 April 2009, Natural hazards and Earth System Sciences, 2012, pp [5] Pinto, The Sensitivity of The Thunderstorm Activity in the city of Sao paulo to temperature Changes: predicting the Future Activity for Different Scenarios, International Lightning Detection Conference, 2012, pp [6] Alan czarnetzki, C., Evaluation of a Forecast strategy for Nocturnal Thunderstorms that Produce heavy rain, pp [7] David Bright, R., Matthew Wandishin, S., Ryan Jewell, E., and Steven Weiss, j., A Physically Based Parameter for Lightning Prediction and its Calibration in Ensemble Forecasts, Confeerence on Meteorological Applications of Lightning Data, AMS, 2005, pp [8] Ken Harding, Thunderstorm Formation and Aviation Hazards, NOAA s National Weather Service, pp.1-4. [9] Bill Nisley, Thunderstorm Anatomy and Dynamics, Naval Postgraduate School, california, 1999, pp [10] Kanwaljot Singh Sidhu, Baljeet Singh Khaira, Ishpreet Singh Virk, Medical Image Denoising In The Wavelet Domain Using Haar and DB3 Filtering, International Refereed Journal of Engineering and Science, 2012, pp [11] Candra Dewi, Mega Satya Ciptaningrun, Nmuh Arif Rahman, Denoising Cloud Interference on Landsat Satellite Image Using Discrete Haar Wavelet Transformation, International Journal of Computer Science and Information Security, 2012, pp [12] C. Fraley and A. Raftery, "How Many Clusters? Which Clustering method? Answers via Model-Based Cluster Analysis," The Computer Journal, 1998, pp