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Non-negative K DP Estimation by Monotone Increasing ΦDP Assumption below Melting Layer

Dual-polarimetric weather radar becomes prevalent for Quantitative Precipitation Estimation (QPE) in recent years. Especially, Ministry of Land, Infrastructure, Transport and Tourism (MLIT), Japan started deploying X-band Dual-polarimetric radar network around great urban areas and local major cities for a real-time monitoring of heavy precipitation. Now 27 radars in 11 areas have already been deployed, and more 8 radars will be installed by FY 2013. This real-time QPE system generates a precipitation intensity data with the horizontal resolution of 250 m, and the data are updated every 1 minute (Maesaka et al. 2011). In this system, the precipitation intensity (R) is calculated not from radar reflectivity (Z h) but from specific differential phase (KDP) for heavy or moderate rainfalls. Moreover, KDP is used for attenuation corrections of Zh and differential reflectivity (ZDR). So the recent QPE progress in X-band radar is due to that the KDP becomes available. The KDP is not directly observed by radar system (not a result of I/Q signal processing), but is calculated by a derivative of a differential phase (ΦDP) with respect to range. Because the observed ΦDP contaminated by noise, and the differentiation works as a high-pass filler, it is difficult to retrieve the originalKDP caused by meteorological phenomena. Thus local linear or polynomial regressions are generally used to estimate the original KDP; however, these procedures make a spatial resolution of the KDP coarse. Furthermore, a differential backscatter phase (δ) overlaps in the observed Φ DP. It is not negligible if large precipitation particles exist in the radar beam. Scarchilli et al. (1993) estimated the differential backscatter phase from the attenuation-corrected ZDR. Hubbert and Bringi (1995) proposed an iterative filtering technique to remove the backscatter phase from the observed ΦDP. There are also recent studies on the robust KDP estimation with high spacial resolution, e.g., Wang and Chandrasekar (2009) and Otto and Russchenberg (2011). At present, the stable version algorithms of KDP estimation in MLIT and National Research Institute for Earth Science and Disaster Prevention (NIED) are based on the classical methods: the iterative filtering and the local linear regression (Maesaka et al. 2011). These algorithms sometimes estimate a negative KDP in rainfall (below melting layer), though the positive KDP is expected for pure rain drops. The negative KDP tends to be estimated in a weak rainfall area and far-side of heavy precipitation. The former negative KDP is caused by the noise of ΦDP , and the latter is influenced by the differential backscatter phase. This fake KDP is a problem in the MLIT and NIED algorithms, because it disable the rainfall estimation and the attenuation correction. In this paper, a new KDP estimation method is proposed by assuming the monotone increasing Φ DP below melting layer. The KDP estimated by this new method always takes a positive value.