We evaluated the potential of polarimetric rainfall retrieval methods for the Tagaytay C-Band weather radar in the Philippines. For this purpose, we combined a method for fuzzy echo classification, an approach to extract and reconstruct the differential propagation phase, Φ DP , and a polarimetric self-consistency approach to calibrate horizontal and differential reflectivity. The reconstructed Φ DP was used to estimate path-integrated attenuation and to retrieve the specific differential phase, K DP . All related algorithms were transparently implemented in the Open Source radar processing software wradlib. Rainfall was then estimated from different variables: from re-calibrated reflectivity, from re-calibrated reflectivity that has been corrected for path-integrated attenuation, from the specific differential phase, and from a combination of reflectivity and specific differential phase. As an additional benchmark, rainfall was estimated by interpolating the rainfall observed by rain gauges. We evaluated the rainfall products for daily and hourly accumulations. For this purpose, we used observations of 16 rain gauges from a fivemonth period in the 2012 wet season. It turned out that the retrieval of rainfall from K DP substantially improved the rainfall estimation at both daily and hourly time scales. The measurement of reflectivity apparently was impaired by severe miscalibration while K DP was immune to such effects. Daily accumulations of rainfall retrieved from K DP showed a very low estimation bias and small random errors. Random scatter was, though, strongly present in hourly accumulations.
This case study evaluates the suitability of radar-based quantitative precipitation estimates (QPEs) for the simulation of streamflow in the Marikina River Basin (MRB), the Philippines. Hourly radar-based QPEs were produced from reflectivity that had been observed by an S-band radar located about 90 km from the MRB. Radar data processing and precipitation estimation were carried out using the open source library wradlib. To assess the added value of the radarbased QPE, we used spatially interpolated rain gauge observations (gauge-only (GO) product) as a benchmark. Rain gauge observations were also used to quantify rainfall estimation errors at the point scale. At the point scale, the radarbased QPE outperformed the GO product in 2012, while for 2013, the performance was similar. For both periods, estimation errors substantially increased from daily to the hourly accumulation intervals. Despite this fact, both rainfall estimation methods allowed for a good representation of observed streamflow when used to force a hydrological simulation model of the MRB. Furthermore, the results of the hydrological simulation were consistent with rainfall verification at the point scale: the radar-based QPE performed better than the GO product in 2012, and equivalently in 2013. Altogether, we could demonstrate that, in terms of streamflow simulation, the radar-based QPE can perform as good as or even better than the GO product À even for a basin such as the MRB which has a comparatively dense rain gauge network. This suggests good prospects for using radar-based QPE to simulate and forecast streamflow in other parts of the Philippines where rain gauge networks are not as dense.
Abstract. We explore the potential of spaceborne radar (SR) observations
from the Ku-band precipitation radars onboard the Tropical Rainfall Measuring
Mission (TRMM) and Global Precipitation Measurement (GPM)
satellites as a reference to quantify the ground radar (GR) reflectivity
bias. To this end, the 3-D volume-matching algorithm proposed by
Schwaller and Morris (2011) is implemented and applied to 5 years
(2012–2016) of observations. We further extend the procedure by a framework
to take into account the data quality of each ground radar bin. Through these
methods, we are able to assign a quality index to each matching SR–GR
volume, and thus compute the GR calibration bias as a quality-weighted
average of reflectivity differences in any sample of matching GR–SR volumes.
We exemplify the idea of quality-weighted averaging by using the beam
blockage fraction as the basis of a quality index. As a result, we can
increase the consistency of SR and GR observations, and thus the precision of
calibration bias estimates. The remaining scatter between GR and SR
reflectivity as well as the variability of bias estimates between overpass
events indicate, however, that other error sources are not yet fully
addressed. Still, our study provides a framework to introduce any other
quality variables that are considered relevant in a specific context. The
code that implements our analysis is based on the wradlib open-source
software library, and is, together with the data, publicly available to
monitor radar calibration or to scrutinize long series of archived radar data
back to December 1997, when TRMM became operational.
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