In 2017, BMKG has 41 weather radars covering most of Indonesia region and most of its data are automatically sent to the BMKG headquarter every 10 minutes. There are four different weather radar brands with its specific data format and software analysis. In recent years, the weather radar community has developed open-source software to handle several radar data formats. Based on this, BMKG has developed the Indonesia In-House Radar Integration System (InaRAISE) of BMKG using the open-source weather radar software. InaRAISE has been developed using Python-based libraries Wradlib and Py-ART for processing weather radar data. BMKG radar data have been successfully extracted and transformed into Cartesian coordinates for post-processing. The multiple radars have been successfully composited by comparing column-maximum reflectivity. Web-based near real-time radar images has been experimentally operated, but not officially launched. The main constraint is the susceptible communication network between radar sites and BMKG headquarter causing real-time data transfer problems. InaRAISE serves feasible data radar extraction for data assimilation in the numerical weather prediction model. InaRAISE could serve as a supporting of the existing radar integration system or possibly as a replacement.

Quantitative Precipitation Estimation (QPE) is quite important information for the hydrology fields and has many advantages for many purposes. Its dense spatial and temporal resolution can be combined with the surface observation to enhance the accuracy of the estimation. This paper presents an enhancement to the QPE product from BMKG weather radar network at Surabaya by adjusting the estimation value form radar to the real data observation from rain gauge. A total of 58 rain gauge is used. The Mean Field Bias (MFB) method used to determine the correction factor through the difference between radar estimation and rain gauge observation value. The correction factor obtained at each gauge points are interpolated to the entire radar grid in a multiplicative adjustment. Radar-gauge merging results a significant improvement revealed by the decreasing of mean absolute error (MAE) about 40% and false alarm ratio (FAR) as well an increasing of possibility of detection (POD) more than 50% at any rain categories (light rain, moderate rain, heavy rain, and very heavy rain). This performance improvement is very beneficial for operational used in BMKG and other hydrological needs.