DATA OPTIMIZATION OF RAIN RADAR AND ITS COVERAGE EXPANSION USING RADAR NETWORK

Tiin Sinatra, Ginaldi Ari Nugroho, Asif Awaludin

Abstract

This research developed network of two Santanu rain radars of the Center for Atmospheric Science and Technology LAPAN installed in Bandung (SANTANU 1) and Sumedang (SANTANU 2). The objective is to get larger coverage area which also comply data each other. The first step is data optimation to each radar detection results, particularly due to rain attenuation, and then validation with the transportable radar image. The shape analysis on a rain object detected in the slice of both radar images indicates 0.8672 of R2 which show the width of both objectcs has a good similarity. Data optimation due to rain attenuation using attenuation correction for regional weather radar (KA) and path-integrated attenuation (PIA) indicate that the reflectivity improvement by KA is 10 times better than PIA. The object detected by Santanu rain radar is similar to the transportable radar for reflectivity larger than 30 dBZ.  The next step is integration of both rain radar images to create mosaic image and then validation using the transportable radar data. The production of the mosaic image is tried out using three methods: image stitching, averaging, and Cressman scheme. The best result is presented by Cressman scheme which show more integrated slice area and maintain the features of the object. However, the processing time of this scheme is 17 minutes which is longer than  image stitching method. The processing time depend on the number of grid data and the hardware memory. Validation on the mosaic result by using transportable radar data by implementing structural similarity index (SSIM) method yields 0,32 of average value. The improvement on SSIM value is obtained if speckle removal is applied by recording 59% of enhancement

Keywords

rain radar, radar network, attenuation, mosaic

Full Text:

PDF

References

Lengfeld, K., Clemens, M., Münster, H., dan Ament, F., (2014): Performance of High-Resolution X-band Weather Radar Networks the PATTERN Example. Atmos.Meas.Tech., vol. 7, 4151-4166

Trabal, J.M., Colom-Ustariz, J., Cruz-Pol, S., Pablos-Vega, G.A., Mc Laughlin, D.J., 2013. Remote Sensing of Weather Hazards Using a Low Cost and Minimal Infrastructure Off-The-Grid Weather Radar Network. IEEE Trans. Geosci. Remote Sens., 51: 72-82

Sinatra, T., Nugroho, G.A., 2017. Analisis Deteksi Hujan Berbasis Jaringan Radar X-Band di Bandung dan sekitarnya. Bagian Buku Bunga Rmapai : Dinamika Dan Teknologi Atmosfer Benua Maritim Indonesia, 72-82

Pedersen, L., Jensen, N.E., Madsen, H., 2007. Network architecture for small X-band weather radars : Test bed for automatic inter-calibration and nowcasting. The 33rd Conference on Radar Meteorology, Cairns, Australia, 6-10 August. Vol. 12B.2, 2007: 1-7.

Antonini A., Melani S., Corongiu M., Romanelli S., Mazza A., 2017. On the Implementation of a Regional X-Band Weather Radar Network , Atmosphere 2017, 8, 25 : 1-20; doi:10.3390/atmos8020025.

Pedersen, L., Jensen, N.E., Madsen, H., 2010. Calibration of Local Area Weather Radar—Identifying significant factors affecting the calibration. J. of Atmospheric and Oceanic Technology, 33, 2315-2329.

Delrieu, G., Andrieu, H.,Creutin, J.D., 2012. Quantification of Path-Integrated Attenuation for X- and C-Band Weather Radar Systems Operating in Mediterranean Heavy Rainfall. Journal Of Applied Meteorology, Vol. 39, No. 6, Juni 2020 : 840-850.

Lengfeld,K., Clemens, M., Merker, C., Münster, H., dan Ament, F., 2016. A Simple Method for Attenuation Correction in Local X-Band Radar Measurements Using C-Band Radar Data. J. of Atmospheric and Oceanic Technology, 33, 2315-2329.

Zhang,J., Howard,K., Gourley, J.J., (2005): Constructing Three-Dimensional Multiple-Radar Reflectivity Mosaiks: Examples of Convective Storms and Stratiform Rain Echoes. J. of Atmospheric and Oceanic Technology. Vol. 22,p.30-42.

Daliakopoulos, I. N. and Tsanis, I. K., 2019. A SIFT-Based DEM Extraction Approach Using GEOEYE-1 Satellite Stereo Pairs. Sensors, Vol. 19, 1123 : 1-18; doi:10.3390/s19051123.

Dellinger, F. Delon, J., Gousseau Y., Michel, J., Tupin, F., 2019. A SIFT-Based DEM Extraction Approach Using GEOEYE-1 Satellite Stereo Pairs. International Geoscience and Remote Sensing Symposium 2012, 3478-3481

Wang, Z., Bovik, A. C., Sheikh, H. R., and Simoncelli, E. P., 2004. Image quality assessment: From error visibility to structural similarity. IEEE Transactios on Image Processing, Vol. 13, no. 4, pp. 600-612

Refbacks

  • There are currently no refbacks.