EnviroInfo 2007 (Warschau) Environmental Informatics and Systems Research Copyright © Shaker Verlag, Aachen 2007. ISBN: 978-3-8322-6397-3 Numerical Methods for the Detection of Whirlwind (Cyclone, Tornado, Hurricane) on Satellite Data István Pál1 Abstract This paper studies and discusses some methods for the detection, localization and tracing of the hurricane/thunderstorm relevant features on digital satellite pictures, focused on geometrical structures such as circle, spiral etc. using image processing techniques and numerical methods. 1. Introduction The climate changing, the global warming up is a much discussed topic, which implies (but it is frequently disputed) the increasing number of hurricane / thunderstorms occurrences. So the automatic monitoring and prediction of these events is of great important. The first satellite images were available in the 1960s. The identification and tracking of intense hurricanes became a trivial task according to the detection of the hurricane eye Zehr (2004). A methodology by Vern Dvorak at NOAA/NESDIS to estimate tropical cyclone using geostationary satellite data was developed during the 1970’s. This technique, known as the Dvorak Technique (DT), has been used by operational hurricane forecasting centers worldwide and in regions where aircraft information is not available. The Dvorak Technique was supplemented in 1984 with the enhanced infrared (EIR) method for independently assigning hurricane intensity. Further advances to Dvorak’s technique were developed in the late 1980’s and early 1990’s as analysis of global satellite data became more widely available (Olander Velden, 2004). In the mid 1990’s, scientists at the University of Wisconsin/Cooperative Institute for Meteorological Satellite Studies (UW-CIMSS) began an effort to reconstruct the DT within a computer-based environment. The prototype design, the UW-CIMSS Objective Dvorak Technique (ODT), was developed as a first step along this research path. Meteorological satellites e.g. the Meteosat Second Generation (MSG) (see Figure 1) provide global informations about a part of the earth (see Figure 2), which can be used for the refinement of the wetter prediction. From the MSG satellite in every 15 minutes can be received full disk data in 3×3km resolution in eight different spectral domain, which can be used for the near real-time monitoring of the development of the cloud structures in the atmosphere. For the whirlwind analyze the channels VIS006, NIR016, IR108 (see Figure 3) can be used, also as components of the RGB color channels. Unfortunately the 3×3km resolution didn’t allow the detection at these finer structures. In this way smaller tornados can’t be detected, but in this case the radar data may be used to detect it. Another problem is, that only the top of the atmosphere can be seen on the satellite pictures but the deeper layer not, which can be sometimes bridge-over with using different channels of the satellite. Often is the time activity so short (few minutes), that there is no possibility to detect it. There are statements that the forecasting of tornados in Germany is not possible, because under every thunderstorm cloud (supercells) can be built a tornado. The duration and another physical sizes (small scale phenomenon –but not the destroying–) of this event are so small, that they are impossible to acquire. 1 Technical University Munich, 85354 Freising, Am Hochanger 13, Germany, e-mail: [email protected] 425 Figure 1: The Meteorological satellites included also the Meteosat Second Generation (MSG). (origin: http: //www.eumetsat.int) The thunderstorm events, the eye of the hurricane and its geometrical structures on the satellite pictures can be characterized by circles, ellipses and spirals. In the next sections there will be studied and discussed some methods to detect and to trace the cloud structures and hurricane/thunderstorm/whirlwind relevant features, focused on geometrical structures such as circle, spiral etc. using image processing techniques and numerical methods. Figure 2: The MSG coverage area (origin: http: //www.eumetsat.int) 426 Copyright © Shaker Verlag, Aachen 2007. ISBN: 978-3-8322-6397-3 (a) (b) (c) Figure 3: Images from MSG. (a) VIS006 visible channel 0.56 - 0.71 µm, (b) NIR016 near-infrared channel 1.50 - 1.78µm (c) IR108 infrared channel 9.8 - 11.8 µm (origin: http: //http: //www.sat.dundee.ac.uk/abin/geobrowse/) 2. Image Processing of the Satellite Data 2.1 Preprocessing The satellite pictures are first filtered or smoothed in order to reduce the noise. The smoothing of the image is performed using the convolution filter with a 2-dimensional Gaussian kernel 1 (r − x)2 + (s − y)2 h(r,s) = f (x, y) ⋅ e − . (1) σ 2π 2 xy 2σ 427 Copyright © Shaker Verlag, Aachen 2007. ISBN: 978-3-8322-6397-3 The segmentation of the images can be taken using adaptive methods, statistical and texture based techniques, but a simple thresholding gives also good result for the extraction of the geometrical features of the cloud structures. The different cloud types can be classified with the Random Markov Fields, which provide a very helpful tool for the cloud type texture structure segmentation (Cadez, Smyth, 1999; Gordon, Tag, and Bankert, 1995; Papin and Bouthemy, 2002). The edge segmentation is realized with a Sobel operator which provides the boundaries of the objects. The norm of the gradient vector and the orientation of the edges at a given pixel (x,y) is calculated as follows: ∂f ∂f ∂f ∂f | ∇f | = ( )2 + ( )2 ; θ (x, y) = tan−1( )2 (/ ). (2) ∂x ∂y ∂y ∂x The Sobel filter, which is based on the looking-up of the maxima of the first derivates, can be supported by its second derivates, which are the looks-up of the zero-crossing. The second order derivates of the Gaussian kernel is the Laplacian of Gaussian or the Marr-Hildreth operator: 1 (x2 + y2 ) (x)2 + (y)2 ∇2 (G ∗ f ) = (∇2G)∗ f = − 2 e − . (3) 2 2 2 σ σ 2σ 2.2 Tracking and Detection of the Cloud Motion For the motion tracking the optical flow algorithm will be used. The motion of the atmosphere on the satellite pictures series S(x,y,t) can be tracked with the help of the block matching and the optical flow method. On the satellite pictures only the top layer of the clouds can be seen. Lower layers can be also displayed with the combination of the different channels. It’s assumed that within a short time there is no change in the texture and/or the structure of the clouds. The registration of the same or similar cloud structures in the different time series can be taken with the template matching, correlation or sum of squares difference method. With the block matching the positions of a structure in the different time sequences will be determinated. Using a difference-function d(x,y) will be ordered to each reference pixel a vector and so a vector field will be obtained: d(x,y) = S(x,y,t + )1 − S(x,y,t) = S(x + D, + D, ) − (,,), (4) where Δ x,Δ y are the coordinates of the shift vector. 3. Detection of Geometrical Structures Since the hurricane eye or a supercell is a dark pixel structure inside of the white cloud pixels on the satellite pictures and because of the rotation builded arms of the cloud structures, its detection is based on the localization of geometrical structures such as circle, ellipses, spiral. Geometrical Structures or shapes on the digital image can be detected by their features, which can be derivated by the edge detection or from the segmented object using skeletonization, from image sequences with optical flow or by another methods. On the segmented digital images the object middle lines can be determinated using skeletonization, which provide informations about the shape structures and are used for the object detection. If the segmentation provides disconnected pixel structures than the skeletonization or the middle axis transformation works faulty. In this case and in the case of noisy pictures it is better to use the principal curve clustering method (Kégl, 1999; Kégl and Krzyzak, 2002), which provides the middle curve of a point cloud and a kind of skeletonization. So the principal curve 428 Copyright © Shaker Verlag, Aachen 2007. ISBN: 978-3-8322-6397-3 clustering of the pixel clouds can be used to determinate and to reduce the nonrelevant informations (e.g. noisy data) but obtain the geometric relevant features for the numerical calculations. Identification of small objects can be taken with the template matching method. It works however properly on symmetrical shapes, because it isn’t rotation invariant. Using correlation can help to avoid the rotations problem. Numerical approaches e.g. Hough transformation provide good solutions for the object detection (circle, ellipses, spiral or general shape etc.). Another approaches work in the frequency domain e.g. in the Fourier space or on the complex plane and on vector fields. These methods were frequently used in the fingerprint recognition (Chikkerur and Ratha, 2005) for the localization of the singularities of vector fields and in the astronomy for the spiral detection (Lin, Yuan and Shu, 1969), but it can also be applied for the determination of hurricane-eye relevant points on the satellite pictures. The zero crossing with the Mexican-hut kernel or LoG or Marr-Hildreth operator can be also used for the hurricane eye detection. 3.1 Circle Detection with Hough Transform The Hough Transform is one of the most widely used algorithms in image processing to find straight lines in an image. It was published in a paper, and a patent in 1962. Later the Hough transform was extended to arbitrary shape detection. It maps the points/pixels of an image to sinusoidal curves in Hough space.