Cross Validation of TRMM PR Reflectivity Profiles Using 3D Reflectivity Composite from the Ground-Based Radar Network over the Korean Peninsula

Abstract

The Tropical Rainfall Measuring Mission (TRMM) Precipitation Radar (PR) measures reflectivity downward from space and provides observations of the vertical distributions of precipitation over land as well as the ocean. It overpasses the southern part of the Korean Peninsula where (i) a dense network of operational S-band scanning radars is available and (ii) various types of precipitation occur. By utilizing a 3D reflectivity composite from the ground S-band radar (GR) observations, this paper shows a comparison of reflectivity profiles observed with both PR and GR focusing on their vertical structure. For four cases of widespread rain, visual and statistical analyses show that PR attenuation-corrected reflectivity agrees closely with reflectivity observed from the GR composite below the melting layer. Above and within the melting layer, PR is affected critically by its sensitivity while GR beam broadening at far ranges causes systematic differences in the PR-GR comparisons. For four cases of convective rain, PR underestimates the mean reflectivities by 1-3 dB compared with those from GR at low levels where precipitation attenuation is significant toward the ground. In these cases, the low sensitivity of PR results in a small number of matched points for weak echoes. Also, the PR-GR discrepancy for the convective case is more affected by time mismatching.

Enhancing the consistency of spaceborne and ground-based radar comparisons by using beam blockage fraction as a quality filter

“…Relative calibration (defined as the assessment of bias between the reflectivity of two radars) has been steadily gaining popularity, in particular the comparison with spaceborne precipitation radars (SR) (such as the precipitation radar onboard the Tropical Rainfall Measuring Mission (TRMM;1997Kummerow et al, 1998) and the dualfrequency precipitation radar on the subsequent Global Precipitation Measurement mission (GPM;2014-present;Hou et al, 2013)). Several studies have shown that surface precipitation estimates from GRs can be reliably compared to precipitation estimates from SRs for both TRMM (Amitai et al, 2009;Joss et al, 2006;Kirstetter et al, 2012) and GPM (Gabella et al, 2017;Petracca et al, 2018;Speirs et al, 2017). In addition, a major advantage of relative calibration and gauge adjustment in contrast to the absolute calibration (i.e.…”

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

“…However, we need to continue disentangling different sources of uncer-tainty for both SR and GR observations in order to distinguish actual variations in instrument calibration and stability from measurement errors that accumulate along the propagation path and to better understand the requirements for robustly estimating these properties from limited samples. This also includes extending the quality-weighting framework to the quality of SR reflectivity measurements, as already outlined in Crisologo et al (2018), particularly in regard to the combined effects of attenuation at Ku band and nonuniform beam filling that several authors found to cause systematic errors of SR reflectivity measurements in convective situations (see, e.g., Deo et al, 2018 andPark et al, 2015 for an in-depth discussion). Progress towards these ends should also improve the potential for interpolating calibration bias estimates in time, in order to tap into the potential of historical radar archives for radar climatology and increase the homogeneity of composite products from heterogeneous weather radar networks.…”

“…The main components of that workflow are based on the open-source software library for processing weather radar data called wradlib version 1.2 (Heistermann et al, 2013b) (released on 31 October 2018), based on Python 3.6. The main dependencies of wradlib include Numerical Python (NumPy; Oliphant, 2015), Matplotlib (Hunter, 2007), Scientific Python (SciPy; Virtanen et al, 2019), h5py (Collette, 2013), netCDF4 (Rew et al, 1989), gdal (GDAL Development Team, 2017), and pandas (McKinney, 2010).…”

Studies of General Precipitation Features with TRMM PR Data: An Extensive Overview

“…Gabella et al [32][33][34][35] designed range-dependent adjustment methods for the compensation of GR observations in consideration of the increasing sampling volume of the GR with range, and used this method to improve the rainfall estimation of the GR. Some recent studies made comparisons to evaluate the GR reflectivity in East Asia [36][37][38][39][40], in which stratiform precipitation was generally used to avoid uncertainties of PR products related to convective rain.…”