3.5 Conclusions

In 2011, Schwaller and Morris presented a new technique to match spaceborne radar (SR) and ground-based radar (GR) reflectivity observations, with the aim to determine the GR calibration bias. Our study extends that technique by an approach that takes into account the quality of the ground radar observations. Each GR bin was assigned a quality index between 0 and 1, which was used to assign a quality value to each matched volume of SR and GR observations. For any sample of matched volumes (e.g. all matched volumes of one overpass, or a combination of multiple overpasses), the calibration bias can then be computed as a quality-weighted average of the differences between GR and SR reflectivity in all samples. We exemplified that approach by applying a GR data quality index based on the beam blockage fraction, and we demonstrated the added value for both TRMM and GPM overpasses over the 115 km range of the Subic S-band radar in the Philippines for a 5-year period.

Although the variability of the calibration bias estimates between overpasses is high, we showed that taking into account partial beam blockage leads to more consistent and more precise estimates of GR calibration bias. Analyzing 5 years of archived data from the Subic S-band radar (2012–2016), we also demonstrated that the calibration standard of the Subic radar substantially improved over the years, from bias levels of around -4.1 dB in 2012 to bias levels of around 1.4 dB in 2014 and settling down to a bias of 0.6 dB in 2016. Of course, more recent comparisons with GPM are needed to verify that this level of accuracy has been maintained. Case studies for specific overpass events also showed that the necessity to account for partial beam blockage might even increase for higher antenna elevations. That applies when sectors with total beam blockage (in which no valid matched volumes are retrieved at all) turn into sectors with partial beam blockage at higher elevation angles.

Considering the scatter between SR and GR reflectivity in the matched volumes of one overpass (see case studies), as well as the variability of bias estimates between satellite overpasses (see time series), it is obvious that we do not yet account for various sources of uncertainties. Also, the simulation of beam blockage itself might still be prone to errors. Nevertheless, the idea of the quality-weighted estimation of calibration bias presents a consistent framework that allows for the integration of any quality variables that are considered important in a specific environment or setting. For example, if we consider C-band instead of S-band radars, path-integrated attenuation needs to be taken into account for the ground radar, and wet radome attenuation probably as well (Austin 1987; Merceret and Ward 2000; Villarini and Krajewski 2010). The framework could also be extended by explicitly assigning a quality index to SR observations, too. In the context of this study, that was implicitly implemented by filtering the SR data, e.g. based on brightband membership. An alternative approach to filtering could be weighting the samples based on their proximity to the brightband, the level of path-integrated attenuation (as e.g. indicated by the GPM 2AKu variables pathAtten and the associated reliability flag (reliabFlag)) or the prominence of non-uniform beam filling (which could e.g. be estimated based on the variability of GR reflectivity within the SR footprint; see e.g. Han et al. (2018)).

In addition, with the significant effort devoted to weather radar data quality characterization in Europe (Michelson et al. 2005), and the number of approaches in determining an overall quality index based on different quality factors (Einfalt, Szturc, and Ośródka 2010), it is straightforward to extend the approach beyond beam blockage fraction.

Despite the fact that there is still ample room for improvement, our tool that combines SR–GR volume matching and quality-weighted bias estimation is readily available for application or further scrutiny. In fact, our analysis is the first of its kind that is entirely based on open-source software, and is thus fully transparent, reproducible, and adjustable (see also Heistermann et al. (2014)). Therefore this study, for the first time, demonstrates the utilization of wradlib functions that have just recently been implemented to support the volume matching procedure and the simulation of partial beam blockage. We also make the complete workflow available together with the underlying ground and spaceborne radar data. Both code and results can be accessed at the following repository https://github.com/wradlib/radargpm-beamblockage upon the publication of this paper.

Through these open-source resources, our methodology provides both research institutions and weather services with a valuable tool that can be applied to monitor radar calibration, and—perhaps more importantly—to quantify the calibration bias for long time series of archived radar observations, basically beginning with the availability of TRMM radar observations in December 1997.

References

Austin, Pauline M. 1987. “Relation Between Measured Radar Reflectivity and Surface Rainfall.” Monthly Weather Review 115 (5): 1053–70. https://doi.org/10.1175/1520-0493(1987)115<1053:RBMRRA>2.0.CO;2.

Einfalt, Thomas, Jan Szturc, and Katarzyna Ośródka. 2010. “The Quality Index for Radar Precipitation Data: A Tower of Babel?” Atmospheric Science Letters 11 (2): 139–44. https://doi.org/10.1002/asl.271.

Han, Jing, Zhigang Chu, Zhenhui Wang, Dan Xu, Nan Li, Leilei Kou, Fen Xu, and Yiqing Zhu. 2018. “The Establishment of Optimal Ground-Based Radar Datasets by Comparison and Correlation Analyses with Space-Borne Radar Data.” Meteorological Applications 25 (1): 161–70. https://doi.org/10.1002/met.1682.

Heistermann, M., S. Collis, M. J. Dixon, S. Giangrande, J. J. Helmus, B. Kelley, J. Koistinen, et al. 2014. “The Emergence of Open-Source Software for the Weather Radar Community.” Bulletin of the American Meteorological Society 96 (1): 117–28. https://doi.org/10.1175/BAMS-D-13-00240.1.

Merceret, Francis, and Jennifer G. Ward. 2000. “Attenuation of Weather Radar Signals Due to Wetting of the Radome by Rainwater or Incomplete Filling of the Beam Volume.” NASA Center for AeroSpace Information. https://ntrs.nasa.gov/search.jsp?R=20020043890.

Michelson, Daniel, Thomas Einfalt, Iwan Holleman, Uta Gjertsen, Friedrich, Katja, Günther Haase, Magnus Lindskog, and Anna Jurczyk. 2005. Weather Radar Data Quality in Europe Quality Control and Characterisation. Luxembourg: Publications Office.

Villarini, Gabriele, and Witold F. Krajewski. 2010. “Review of the Different Sources of Uncertainty in Single Polarization Radar-Based Estimates of Rainfall.” Surveys in Geophysics 31 (1): 107–29. https://doi.org/10.1007/s10712-009-9079-x.