Abstract
Abstract. Many institutions struggle to tap into the potential of their large archives of radar reflectivity: these data are often affected by miscalibration, yet the bias is typically unknown and temporally volatile. Still, relative calibration techniques can be used to correct the measurements a posteriori. For that purpose, the usage of spaceborne reflectivity observations from the Tropical Rainfall Measuring Mission (TRMM) and Global Precipitation Measurement (GPM) platforms has become increasingly popular: the calibration bias of a ground radar (GR) is estimated from its average reflectivity difference to the spaceborne radar (SR). Recently, Crisologo et al. (2018) introduced a formal procedure to enhance the reliability of such estimates: each match between SR and GR observations is assigned a quality index, and the calibration bias is inferred as a quality-weighted average of the differences between SR and GR. The relevance of quality was exemplified for the Subic S-band radar in the Philippines, which is greatly affected by partial beam blockage. The present study extends the concept of quality-weighted averaging by accounting for path-integrated attenuation (PIA) in addition to beam blockage. This extension becomes vital for radars that operate at the C or X band. Correspondingly, the study setup includes a C-band radar that substantially overlaps with the S-band radar. Based on the extended quality-weighting approach, we retrieve, for each of the two ground radars, a time series of calibration bias estimates from suitable SR overpasses. As a result of applying these estimates to correct the ground radar observations, the consistency between the ground radars in the region of overlap increased substantially. Furthermore, we investigated if the bias estimates can be interpolated in time, so that ground radar observations can be corrected even in the absence of prompt SR overpasses. We found that a moving average approach was most suitable for that purpose, although limited by the absence of explicit records of radar maintenance operations.
Highlights
Weather radar observations are key to quantitative precipitation estimation (QPE) with a large spatial coverage and a high resolution in space and time
The present study extends the concept of quality-weighted averaging by accounting for path-integrated attenuation (PIA) in addition to beam blockage
We demonstrate the effect of extending the framework of quality-weighting via path-integrated attenuation. This is done by analyzing the mean and the standard deviation of differences between the two ground radars, SUB and TAG, in different scenarios of quality filtering for a case in December 2014
Summary
Weather radar observations are key to quantitative precipitation estimation (QPE) with a large spatial coverage and a high resolution in space and time (on the order of 102–103 m and 100–101 min). The indirect nature of the precipitation retrieval paves the way for a multitude of systematic estimation and measurement errors. We define estimation errors as errors that occur in the retrieval of the precipitation rate R from the radar’s prime observational target variable, the radar reflectivity factor Z. These errors are caused mainly by the unknown microphysical properties of the target – be it meteorological or non-meteorological. The prominence of these measurement errors heavily depends on scenariospecific interaction of factors, such as radar bandwidth, beam width, obstacles in the direct and wider vicinity, topography in the radar coverage, atmospheric refractivity, or the microphysical properties of precipitation along the beam’s propagation path.
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