Nowcasting of significant convection by application of cloud tracking algorithm to satellite and radar images

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1 Nowcasting of significant convection by application of cloud tracking algorithm to satellite and radar images Ng Ka Ho, Hong Kong Observatory, Hong Kong Abstract Automated forecast of significant convection for the next 12 hours was generated using cloud tracking and extrapolation method based on both satellite and radar observations. The forecast positions of significant convection were evaluated by comparing with the deep convection satellite product and radar products respectively. The accuracy of nowcast was found to be very promising for the first 3 to 6 hours, and still with some reference values for up to 12 hours. Introduction Air traffic is heavily disrupted by convection activities, which could be minimized if good flow management has been imposed considering air space capacity. Reliable weather forecast is essential for successful planning and management of the air traffic flow and control, especially those over key air traffic control areas. These key areas in Hong Kong Flight Information Region (HKFIR) include major holding areas for arrival flights and the northern and southern boundary zones. Major holding areas for Hong Kong International Airport (HKIA) are generally within 30 nautical miles from holding points at Gamba (21.32 N, E), Canto (21.65 N, E) and Abbey (22.27 N, E). Figure 1 shows the positions of the holding areas and the northern and southern boundary zones. When significant convective activities occur in the holding area, Air Traffic Control (ATC) cannot use it as a buffer to queue up the arriving flights, reducing the air space capacity. If these convections can be reasonable anticipated a few hours ahead, ATC could operate flow control measures to reduce the number of aircrafts entering the HKFIR and mitigate the risk of disruption. Currently, the significant convective activities were forecast using numerical weather prediction (NWP). Unfortunately, NWP can only output forecast twice daily, and it requires up to 9 hours to release the products. Method Advection Scheme Semi-Lagrangian advection is used. Given a motion field, the location that the echo come from at the last time step can be calculated. This method has the advantage that unique values can always be found for any grid points, but has the disadvantage that some information in the echo field has either been used more than once or unused, which leads to some data lost. Normally the echo did not come exactly from one of the grid points, but between a number of points, bilinear interpolation is used because the exact variation of the echo strength between grid points is unknown, but we assume the variation is slow. Bilinear interpolation is the simplest way to calculate the echo strength of the target point, without losing a lot of accuracy. However, due to the interpolation done, very localised data is smoothed. Testing has been done on an artificial A -shaped echo and constant, diverging, converging and rotating fields are applied, as shown in figure 2. The general motion of the echo is as expected but the resultant echo after a few hours becomes blurred. Also, the echoes are weakening, especially in the diverging motion field. This is due to the bilinear interpolation that the extreme values are slowly smoothed. Size of the sample is becoming smaller under a rotating field, but this problem can be solved when the time step is small enough.

2 Obtaining the motion field Optical flow 1 Optical flow is used to obtain the motion field between two images. There are three main types of optical flow algorithm considered, namely Horn & Schunk s, Lucas & Karnade s, and Block Matching algorithm. Horn & Schunk s algorithm handles better for large-scale objects, such that a global movement can be found. Lucas & Kanade s algorithm handles smaller objects or peripheral of a large object better, because it is more sensitive to local changes. Block Matching technique identifies similar features nearby. Block matching technique is less useful in this project since the echoes are continuously growing or decaying, such that the block matching technique will easily identify the wrong block to calculate the motion vectors. Horn & Schunk s and Lucas & Karnade s algorithm has advantage in handling global and local objects, respectively. Therefore, combining the advantages of both methods will give excellent results in experiments. The combination of the above two methods is known as Variation Flow. There are many parameters to be set in Variation Flow, and the best choice of parameters is found experimentally. Application of optical fields in satellite images Deep convection satellite imagery is chosen in this project at the beginning is because satellite images covers a much larger area, comparing to a single 256-km radius radar image. Moreover, it is believed that convection seen at the satellite images generally has longer lifetime than the echoes seen at the radar images, making the longer time of prediction possible. Variation Flow is employed with ρ, α, and σ set at 150, 600 and 1.6 respectively by experiments to obtain the most reasonable motion field. The box level is set to be from level 3 to level 8, corresponding to box size from about 600 km x 600 km, down to 30 km x 30 km. The reason that the box levels chosen giving the best motion field may be due to it includes large scale weather systems such as tropical cyclones, as well as small convective cells. Finally, the motion field is assumed to be constant in time throughout the time of interest, i.e. no feedback is present in the nowcast. A sample output is shown in figure 3. Testing results show that a very smooth motion field will be obtained using the above parameters. Pixel-by pixel verification is done on the nowcast based on optical flow for 12 hours. Threshold is set at 25 K, a pixel below that temperature indicates deep convection is present. Comparison between nowcast and observation is listed in Table 1: Nowcast / Observation Yes No Yes Hit (H) False Alarm (F) No Miss (M) Null Event (Z) Table 1: Comparison between nowcast and observation Define Critical Success Index 2 H (CSI) by C. The CSI of all cases in April using H M F optical flow method are listed in Table 2: Time of Nowcast Optical flow 1 hour hours hours hours 0.27 Table 2: CSI of optical flow nowcast based on satellite images in April Using the results of optical flow images, significant convection forecast are derived. Currently, yellow and red alerts are issued concerning the holding areas and HKFIR when significant convection is forecast to be present based on radar echoes. If 1/8 of the concerned area is affected by convections with echo 15 dbz or above, and 1/80 of the area is affected by convections with echo 33 dbz or above in at least one of the radar image within a 3-hour

3 period, yellow alert will be issued. If half of the concerned area is affected by convections with echo 15 dbz or above, and 1/8 of the area is affected by convections with echo 33 dbz or above in at least one of the radar image within a 3-hour period, red alert will be issued. Different sizes of area and threshold combination applied to satellite nowcast images to imitate the radar output and verified against the original definition of alerts using radar images. The results shows that the CSI will drop significantly to a very low level in a short period of time, no matter what combination of area and temperature threshold are used, but the CSI decrease in a relatively slow rate with time. This is because the deep convection seen in satellite images cannot be directly converted to echoes of rainbands in radar images. Deep convections seen in satellite images do not necessarily correspond to strong rainfall activities in radar images, and vice versa. Application of optical fields in radar composite images Since optical flow technique does not depend on the image types, and the original product is based on the radar images, applying the optical flow techniques to radar images should improve the CSI. To solve the small coverage of radar images, a new composite radar image consists of 256-km radar images from the HKO, Guangdong ( 廣 東 ) composite and Huanan ( 華 南 ) composite radar image from the China Meteorological Administration (CMA) is used. However, currently no binary of text data of the radar echoes are available of the Guangdong or Huanan images, instead only the radar images with coastline and other marks are available for usage. Some of the data might be lost because the coastline covers the echo results. As for the research propose, those images are used directly by filtering the coastlines. The 256- km radius radar images from the HKO will be used first, followed by the Guangdong composite, finally the Huanan composite. The sequence is chosen because the 256-km radar from the HKO is giving the least noise return among the 3 radar images. Guangdong radar is then used because the echoes on the figures are over the coastline, while the Huanan radar has the coastline over the echoes. The 3 images have different update frequency, ranged from 6 minutes to 30 minutes. Hence, images are combined every 30 minutes. Unlike satellite images, feedback is employed to radar images because it can produce more accurate results confirmed by experiments. Different parameters are used with ρ, α, and σ set at 2, 300 and 1.5 respectively. The box level is set to be from level 2 to level 6, which the box size approximately coincides with that chosen for the satellite images. Verification is done using the similar pixel-by-pixel method as that in satellite images. The CSI of all cases in April and May using optical flow and persistence method are listed as follows: Time of Nowcast/ Threshold 15 dbz 33 dbz 1 hour hours hours Table 3: CSI of optical flow nowcast in April and May based on radar images Then, we also verify against the three holding areas with their CSI listed in the following table: Time of Nowcast/ Alert Yellow Red 3 hours hours hours hours Table 4: CSI of alerts for the 3 holding areas in April and May based on radar images It is seen that the 3-hour nowcast is very promising, much better than the products derived from the NWP or satellite nowcast results. Although the CSI decreases rapidly with time, it

4 still has some reference values up to 12 hours. The rapid decay at the end of the nowcast period is because the nowcast time exceeds the usual lifetime of rain bands. Also, the nowcast tend to weaken the echoes (Figure 6), leading to the short term sources of errors mainly from miss. The false alarm ratio (FAR), defined by H, is listed in the following F table: Time of Nowcast/ Alert Yellow Red 3 hours hours hours hours Table 5: FAR of alerts for the 3 holding areas in April and May based on radar images H Summary and conclusion In the project, significant convection forecast is generated using different methods. The nowcasts are verified using the CSI. Optical flow using radar images gives very good results in short term, but the accuracy decreases rapidly with time. Optical flow using satellite images gives fair results in short term, and the accuracy decreases slowly with time. Future possible development Optical flow using radar images gives higher accuracy in short term, and optical flow using satellite images gives better results for longer nowcast periods, while giving a larger area of forecast. On the other hand, NWP can give a very long period of convection forecast but the accuracy in short term is not very good. It is possible to generate forecast combining radar, satellite and NWP products in the future. Motion field obtained by the optical flow might also be used by the third parties for other research and developments. Finally, self-advection of the motion fields obtained by optical flow can be tested as it may give a higher accuracy of nowcast results. Acknowledgement Ng Ka Ho would like to thank Ms. C. C. Lam, Mr. P. Cheung, Mr. C. K. So, Mr. S. Y. Tang and Mr. K. Y. Chan for the assistance and discussion throughout the project. Reference 1 Andres Bruhn, Joachim Weickert and Christoph Schnorr, Combining the Advantages of Local and Global Optic Flow Methods, Lecture Notes in Computer Science, Vol. 2449, 2002, pp Schaefer, Joseph T., The Critical Success Index as an Indicator of Warning Skill, Weather and Forecasting, Vol.5, Dec 1990, pp

5 Figures Figure 1: The positions of the holding areas and the northern and southern boundary zones. Figure 2a: The original artificial A -shaped echoes Figure 2b and 2c: The A -shaped echoes applied to a diverging wind-field (left) and a constant wind-field (right). Note that the edges becomes less clear in both situations

6 Figure 2d (left) and 2e (right): The A -shaped echoes applied to a tropical-cyclone-like wind-field. Figure 3a: The initial satellite image of the testing case on 22 Apr

7 Figure 3b: The nowcasting result (left) and the actual satellite image (right) 3 hours later Figure 3c: The nowcasting result (left) and the actual satellite image (right) 6 hours later

8 Figure 4a: HK-256km radar image at 0830HKT 22 Apr Figure 4b: Guangdong radar composite image at 0830HKT 22 Apr

9 Figure 4c: Huanan radar composite image at 0830HKT 22 Apr Figure 4d: Combined radar composite image at 0830HKT 22 Apr

10 Figure 5a: The nowcasting results (up) and the actual satellite image (down) 3 hours later

11 Figure 5b: The nowcasting results (up) and the actual satellite image (down) 6 hours later

12 Figure 6: Comparison between actual and nowcast convective pixel counts (solid line: actual, dotted lines: nowcast)

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