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Atmospheric Measurement Techniques An interactive open-access journal of the European Geosciences Union
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Discussion papers
https://doi.org/10.5194/amt-2019-126
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/amt-2019-126
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.

Submitted as: research article 29 Apr 2019

Submitted as: research article | 29 Apr 2019

Review status
This discussion paper is a preprint. It is a manuscript under review for the journal Atmospheric Measurement Techniques (AMT).

Using ground radar overlaps to verify the retrieval of calibration bias estimates from spaceborne platforms

Irene Crisologo and Maik Heistermann Irene Crisologo and Maik Heistermann
  • Institute of Environmental Sciences and Geography, University of Potsdam, Potsdam, Germany

Abstract. Many institutions struggle to tap 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 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 much 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 C- or X-band. Correspondingly, the study setup includes a C-band radar which 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.

Irene Crisologo and Maik Heistermann
Interactive discussion
Status: final response (author comments only)
Status: final response (author comments only)
AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment
  • RC1: 'Review', Anonymous Referee #1, 24 May 2019 Printer-friendly Version Printer-friendly Version
  • RC2: 'RC2', Anonymous Referee #2, 27 Jul 2019 Printer-friendly Version Printer-friendly Version
  • RC3: 'review', Anonymous Referee #3, 27 Jul 2019 Printer-friendly Version Printer-friendly Version
Irene Crisologo and Maik Heistermann
Irene Crisologo and Maik Heistermann
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Short summary
Archives of radar observations often suffer from errors, one of which is calibration. However, it is possible to correct them after the fact by using satellite radars as calibration reference. We propose improvements to this calibration method by considering factors that affect the data quality, such that poor quality data gets filtered out in the bias calculation by assigning weights. We also show that the bias can be interpolated in time even for days when there are no satellite data.
Archives of radar observations often suffer from errors, one of which is calibration. However,...
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