A newly calibrated long-term radar data set for Darwin dual-polarization radar

Description

The tropics are known to be a key player for the Earth atmospheric system. A key process for the exchange of energy and water in this region is precipitating tropical convection, which is in principle well observable with radars. However, ground radar data sets for research in this area are quite rare and generally short term. The C-band POLarimetric (CPOL) radar stationed near Darwin, (11°S, 131°E) Australia since 1998 is a great tool for studies of tropical convection. CPOL is a research focus dual-polarization Doppler radar that has been working, and still is, over 16 wet seasons (November to May, in general) producing more than 250,000 plan position indicator scans. To properly and swiftly calibrate CPOL reflectivity, we applied the Relative Calibration Adjustment (RCA) technique. The RCA technique is shown to be a powerful benchmarking tool for monitoring the 250,000+ files of the data set as it is (1) efficient - it automatically finds data that require a deeper investigation; (2) precise - it shows any change in radar's calibration; and (3) rapid. Nevertheless, the RCA needs to be used complementarily with other calibration techniques so that it provides an absolute calibration. This is achieved by comparison with the space-borne radars TRMM and GPM. The newly calibrated CPOL radar data set presented here is being delivered at three product levels. The Level 1 data is composed of 2 main sets: all the calibrated and filtered, corrected and raw, data for each radar scan in (1) CF/Radial format and (2) Cartesian projection. The Level 2 data contains daily products distributed in two sets: (1) 2D fields, such as 2.5 km reflectivity, cloud top height, 0/17/20/40 dBZ height, rainfall rate, convective/stratiform classification and many more; and (2) 3D fields, such as 3D Winds and hydrometeor classifications. The Level 3 data is composed of monthly products (area mean rainfall, peak rainfall, area fraction, convective mass flux) and long-term statistics. Apart from highlighting the construction of the data set, we will provide examples for its potential use in scientific investigations of atmospheric convection and its representation in models.