2.2 Radar data and data processing

Figure 2.1 shows a map of the area around Manila Bay. Radar coverage is provided by a Doppler S-band radar based near the city of Subic. The radar device is located at 500 m a.s.l. and has a nominal range of 120 km, a range resolution of 500 m, and an angular resolution of 1\(^{\circ}\). Radar sweeps are repeated at an interval of 9 min and at 14 elevation angles (0.5\(^{\circ}\), 1.5\(^{\circ}\), 2.4\(^{\circ}\), 3.4\(^{\circ}\), 4.3\(^{\circ}\), 5.3\(^{\circ}\), 6.2\(^{\circ}\), 7.5\(^{\circ}\), 8.7\(^{\circ}\), 10\(^{\circ}\), 12\(^{\circ}\), 14\(^{\circ}\), 16.7\(^{\circ}\), and 19.5\(^{\circ}\)).

In addition, 25 rain gauges were used as ground reference. The rain gauge recordings were obtained from automatic rain gauges (ARGs) and automatic weather stations (AWSs) under Project NOAH; all instruments have a temporal resolution of 15 min.

For radar data processing, the wradlib software (M. Heistermann, Jacobi, and Pfaff 2013) was used. wradlib is an open source library for weather radar processing and allows for the most important steps of radar-based quantitative precipitation estimation (QPE). The reconstruction of rainfall depths from 6 to 9 August included all available radar sweep angles and was based on a four-step procedure (see library reference on http://wradlib.bitbucket.org for further details):

  1. Clutter detection: clutter is generally referred to as nonmeteorological echo, mainly ground echo. Clutter was identified by applying the algorithm of to the rainfall accumulation map. Pixels flagged as clutter were filled by using nearest neighbour interpolation.

  2. Conversion from reflectivity (in dBZ) to rainfall rate (in mm/hr): for this purpose, we used the Z–R relation which is applied by the United States national weather service NOAA for tropical cyclones (\(Z = 250 \cdot R^{1.2}\)). According to Moser et al. (2010), the use of this tropical Z–R relation could be shown to reduce the underestimation of rainfall rates in tropical cyclones as compared to standard Z–R relationships.

  3. Gridding: based on the data from all available elevation angles, a constant altitude plan position indicator (Pseudo-CAPPI) was created for an altitude of 2000 m a.s.l. by using three-dimensional inverse distance weighting. The CAPPI approach was used in order to increase the comparability of estimated rainfall at different distances from the radar—an important precondition for the following step of gauge adjustment.

  4. Gauge adjustment: the radar-based rainfall estimate accumulated over the entire event was adjusted by rain gauge observations using the simple, but robust mean field bias (MFB) approach (Goudenhoofdt and Delobbe 2009; Heistermann and Kneis 2011). A correction factor was computed from the mean ratio between rain gauge observations and the radar observations in the direct vicinity of the gauge locations. Basically, this procedure is equivalent to an ex-post adjustment of the coefficient a in the Z–R relationship.

References

Goudenhoofdt, E., and L. Delobbe. 2009. “Evaluation of Radar-Gauge Merging Methods for Quantitative Precipitation Estimates.” Hydrology and Earth System Sciences 13 (2): 195–203. http://www.hydrol-earth-syst-sci.net/13/195/2009/hess-13-195-2009.html.

Heistermann, Maik, and David Kneis. 2011. “Benchmarking Quantitative Precipitation Estimation by Conceptual Rainfall-Runoff Modeling.” Water Resources Research 47 (6). https://doi.org/10.1029/2010WR009153.

Heistermann, M., S. Jacobi, and T. Pfaff. 2013. “Technical Note: An Open Source Library for Processing Weather Radar Data (Wradlib).” Hydrology and Earth System Sciences 17 (2): 863–71. https://doi.org/10.5194/hess-17-863-2013.

Moser, Heather, Kenneth Howard, Jian Zhang, and Steven Vasiloff. 2010. “Improving QPE for Tropical Systems with Environmental Moisture Fields and Vertical Profiles of Reflectivity.” In Extended Abstract for the 24th Conf. On Hydrology. Amer. Meteor. Soc. https:// ams.confex.com/ams/pdfpapers/162510.pdf.