Improved Estimation of Radar Rainfall Bias Over Klang River Basin Using a Kalman Filtering Approach Sharifah Nurul Huda Syed Yahya 1 , Wardah Tahir 1 , Suzana Ramli 1 Sayang Mohd Deni 2 , Hamzah Arof 3 , Muhammad Faiz Mohamed Saaid 3 1 Faculty of Civil Engineering, Universiti Teknologi MARA, 40450 Shah Alam, Selangor, Malaysia 2 Faculty of Computer and Mathematical Science, Universiti Teknologi MARA, 40450 Shah Alam, Selangor, Malaysia 3 Faculty of Electrical Engineering, Universiti Malaya, 50603 Kuala Lumpur, Malaysia Abstract—Bad weather, consisting of thunderstorms, normally causes the presence of strong winds and heavy rain that may develop into a storm over a certain area. Radar has been the most potential and powerful instrument used to detect and monitor the development of thunderstorms over a large area; however, it also has certain weaknesses. Weather radar can be affected by different sources of errors, which have to be well considered and quantiÝed for a proper interpretation of the collected data. We design a method that combines the Kalman Filter with a multivariate analysis technique. The implementation of this technique is for the purpose of developing a formulation that may help to reduce error. These studies involved parameters such as temperature, humidity, point of gauge rainfall, and weather radar reflectivity. The approach of using the Kalman Filter combined with multivariate analysis is still a new way to improve radar rainfall estimates by prediction (time update) and correction (measurement update). This particular research was developed purposefully to reduce radar rainfall bias due to the uncertain sources of error seen in the weather radar, and many studies have been developed, but still did not achieve suitable values between radar readings with rain gauge returns. Keywords- Bias; Radar Rainfall; Kalman Filter; time update; measurement update I. INTRODUCTION The implementation of filtering techniques for reducing radar rainfall error estimation is still new in Malaysia. Previously, the Malaysian Meteorology Department (MMD) used conventional radar, and also used the 3D-Rapid Program, which provided virtually all of the features required for the operation of a radar network and distribution [1]. In early 1998 the MMD started using Doppler Weather Radar with IRIS software as the tool to derive the rainfall intensity map and other elements of weather data. The type of the radar used in Malaysia for the purpose of rainfall estimation was S-band pulse radar. Historically, it was placed for the purpose of aviation systems, but was also able to be used for the purpose of weather estimation. MMD decided to use it because it is an advanced hardware and software system that can provide high spatial and temporal resolution rainfall information with a detailed view of the rainstorm, and can detect air turbulence, called “wind shear” or “down burst”. It is very useful for the purpose of aviation systems and hydrology in defining the weather characteristics and for the purpose of forecasting the weather over a large area. Besides that, radar rainfall data is affected by several errors. The normal factors in radar rainfall that contribute to errors are ground clutter, partial beam occultation caused by interception of terrain, beam blockage caused by anomalous propagation as it passes through atmospheric layers with different densities, and attenuation effects. This is caused by atmospheric gases and attenuation by the rainfall range, which has an effect on radar measurement of rainfall [2]. Thus, a good understanding in the fundamental knowledge of radar- rainfall is needed in order to improve and to enhance the algorithm and uncertainty analysis for the calculation of rainfall. One of the main advantages of using radar for rainfall measurements is because it can cover a large area in real time. On the other hand, getting accurate radar rainfall estimation for hydrological applications is not a simple task. The complexity of using radar in hydrological applications is mainly due to error characteristics. Removing the systematic error (bias) and enhancing the precision, accuracy, and limitations of radar data sources will be the main issues in promoting accuracy in measurement for radar hydrology applications. It was reported by [3], [4], and [5] about the procedure of bias correction. This study applies the Kalman Filter equation in a way that will reduce the uncertainty in radar rainfall estimation. Weather radar sends off microwave pulses and measures various targets reflected by the atmosphere, such as raindrops, hail, and snow. The characteristics of weather contain elements such as humidity, temperature, wind direction, evaporation, as well as birds need to be considered in collecting the radar echo. The authors would like to thank MOSTI E- Science Fund Grant (06-01-01- SF0290) 2012 IEEE Symposium on Business, Engineering and Industrial Applications 978-1-4577-1634-8/12/$26.00 ©2012 IEEE 368