عنوان مقاله [English]
نویسندگان [English]چکیده [English]
Meteorological radars, after decades of significant advances, have found their unsurpassed active remote sensing role in detecting hazardous weather phenomena, unraveling storms dynamic evolutionary processes, and characterizing precipitation using sophisticated, polarimetric based clutter characterization methods (Melnikov et al., 2003; Bringi et al., 1991; Zmic, 2005 and 2007). Advances, particularly with regard to digital processing and algorithm sophistication have made it possible to infer secondary products such as precipitation liquid water content, rainfall rate, and many other products. Weather radars carry certain ambiguities that are inherent nature of any remote sensing instruments. The ambiguities may arise from electronics stability issues, antenna accuracy, radio electromagnetic interference (RFI) from the natural as well as any man-made microwave emitters, and ground clutter contamination (Hubbert et al., 2003). Furthermore, non-polarimetric radars can cause additional errors as single power measurements can yield ambiguous results in the interpretation of cloud microphysics (Golestani et al., 1995; Bringi et al., 1991). An extensive calibration and validation (cal/val) campaign is an integral part of any successful modern-day weather radar operation (AMS meeting, 2001; Melnikov et al., 2003; Yilmaz et al., 2005). A cal/val program may include a combination of internal and external methods to calibrate radar return parameters and final products. Polarimetric capability further provides cross-calibration means to improve rainfall estimation, to classify precipitation types, and to differentiate weather echoes from those returned from biological scatterers (Yilmaz et al., 2005). Iranian Meteorological Organization (IRIMO) weather radar network — a combination of eight S- and C-band Doppler weather radars is designed to detect and track local mesoscale as well as large-scale meteorological systems entering the country from north-west, west, and south-west regions (Golestani et al., 2000; Golestani et al., 1999). To this date IRIMO network has not demonstrated a successful cal/val program. Tehran C-band polarimetric Doppler weather radar, which is the subject of this study, has not been able to utilize polarimetric capability to constrain ambiguities and enhance radar products. Furthermore, RFI sources have severely contaminated and practically disabled this radar along 45° northeast look angle over major population region. Anomalous propagation and beam blocking errors are also evident on the radar return. This paper presents a processing scheme to identify and remove RFI noise from radar returns. Processing algorithms for radar returns under this scheme are also presented. The algorithm consists of 1) anomalous points exceeding a simple upper threshold are identified and removed from the data set, 2) to minimizing beam broadening error, we divide the entire data domain into equal matrices 3) with respect to beam geometry all boxes are compared with the neighboring beam angles along the vertical structure of clouds, 4) for testing slantwise convection all boxes at lower tilt angle are examined with the 8 neighboring cells at upper tilt to extract the existence of symmetric instability, 5) finally by producing a history map of reflectivity, and statistical analysis of RFI noise, a spectral filter detects and removes RFI noises form radar data. This algorithm was implemented on a squall line data detected by the Tehran weather radar on the 31st of March 2009. The results of the data analysis show the ability of the processing filters in removing RFI noise and detecting convective line and stratiform clouds.