Enhancing the consistency of spaceborne and ground-based radar comparisons by using quality filters

Abstract

Abstract.Coinciding monsoon and typhoon seasons in the Philippines cause torrential rainfall, and associated hazards such as flooding and landslides. While early warning systems require accurate radar-based rainfall estimates, low-density rain gauge networks in the Philippines make it challenging to monitor the calibration of the ground-based radars (GRs). As an alternative, we explore the potential of spaceborne radar (SR) observations from the Ku-band precipitation radars on board the TRMM and GPM 5 satellites as a reference to quantify the calibration bias of an S-band GR in the Philippines. To this end, the 3D volume-matching algorithm proposed by Schwaller and Morris (2009) is implemented and applied to five 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 10 quality-weighted averaging by using the beam blockage fraction as a 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 open source software library 15 wradlib, 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.Copyright statement.

Radar-based Precipitation Climatology in Germany – Developments, Uncertainties and Potentials

“…Both remote sensing-based products are estimates derived from indirect measurements, which are subject to various potential sampling errors causing systematic bias and random errors [6]. Consequently, there are numerous studies that have evaluated radar [7][8][9][10][11][12][13] and satellite [14] products against rain gauges, conducted comparisons between radar and satellite products [15][16][17][18] or between all three methods [19,20].…”