There are many potential sources of the biases in the radar rainfall
estimation process. This study classified the biases from the
rainfall estimation process into the reflectivity measurement bias
and the rainfall estimation bias by the Quantitative Precipitation
Estimation (QPE) model and also conducted the bias correction
methods to improve the accuracy of the Radar-AWS Rainrate (RAR)
calculation system operated by the Korea Meteorological
Administration (KMA). In the

Weather radars can provide rainfall estimates over the Korean
Peninsula and near seas with high spatial (minimum 0.125

Using a series of procedures which estimate the quantitative rainfalls
derived from radar information, this paper focuses on correcting the
measurement bias and the bias by the QPE model because the measurement
and estimation procedures of rainfall play and important roles to the
accuracy of weather radar rainfall. The measurement bias (hereafter

In this study, the performance of the bias correction methods has been
evaluated by comparing the observed rainfall data from rain gauges
operated by the KMA (Korea Meteorological Administration). The
observed rainfall data were collected from 642 ground rain gauges
(called AWS, Automatic Weather Station) located in the Korean
Peninsula, 321 of which were for calibration, and 321 for validation
in Fig. 1. The Bislsan S-band dual-polarimetric radar, which was
installed and operated by the Ministry of Land, Infrastructure and
Transport (MLIT) in 2009, was selected to be the absolute reference
radar to estimate the

This paper utilized the Radar-AWS Rainrate (RAR) calculation system
(Hereafter called the RAR system) for the QPE model. The RAR system,
which was developed by the KMA in 2006, is operated on site, based on
11 single-polarimetric radars. The RAR system produces a merged
rainfall field for the Korean Peninsula through a series of steps
(production of the radar reflectivity field, calculation of AWS
rainfalls, derivation of the

The RAR system estimates the parameters of the

The conditional probability functions in Eq. (

Secondly, the composite rainfall field for the whole country may be produced using each radar rainfall estimate; however, appropriate merging methods (including maximum value, average value, minimum value, and distance weighting methods) must be conducted because the scan ranges of the radar sites overlap. Because the maximum value method is applied to merge radar rainfalls by the KMA (Korea Meteorological Administration, 2012b), the identical method is also utilized in this paper.

Weather radars continuously carry out measurement cycles, which
include sending signals into the atmosphere and receiving and
analyzing the return signals for meteorological observation. The
measurement of the reflectivity itself suffers from hardware
malfunctions (e.g. electronic miscalibration, signal misprocessing)
and radar characteristics (e.g. attenuation). When converting radar
reflectivity into rainrates (

This paper selected a Bislsan S-band dual-polarimeric radar (hereafter
Bislsan dual-pol radar), which can be self-calibrated and is more
accurate than the reference weather radar. To calibrate the Bislsan
dual-pol radar, a self-consistency constraint method that uses the
relationship between the reflectivity (

Derive the

Calculate the

Calculate the difference angle (

After calibration of the Bislsan dual-pol radar for the

Remove the beam-blockage area using the beam-blockage information (penetration ratio more than 90 %).

Reflect the accumulated attenuation effects, due to the rainfall, in the observed reflectivity (attenuation ratio less than 10 %).

Generate the 3-dimensional CAPPI for the reflectivity.

Set up equidistant pairs between the reference and target
radars, within 200

Compare the reflectivity of the reference and target radars, within a

Calculate the reflectivity differences, at intervals of
0.5

The estimated rainfall, based on the radars, has the QPE model bias
(parameters of

The fundamental concept of the MFBC method is that the bias correct
factor (

This study dealt with the Local Gauge Correction (LGC) method, which has been employed in the NMQ (National Mosaic and QPE) of the NOAA (National Oceanic and Atmospheric Administration) and NSSL (National Severe Storms Laboratory) (Zhang et al., 2011). The LGC method, which assigns the weights to a bias between the ground rainfall detected by AWSs and the radar rainfall estimates, is a modified version of the Inverse Distance Weighting (IDW) method. The LGC method is able to correct the rainfall cases that occur locally by modifying the rainfall estimates in each pixel. The procedure of the LGC method is as follows (refer to Fig. 6):

This paper defined that

If the numbers of AWS in the region are sparse,

In sequence, because the LGC method is highly dependent on the number
of AWSs that are available and accurate, a quality control algorithm
for the AWSs has been conducted to remove lower-quality AWSs that have
larger expected errors than the others. The conditions of the quality
control are as follows: (i) in a certain AWS, if the number of pixels
that have a

In Sect. 2.2.1, the reflectivity measurement bias (

To verify the improvement of the radar rainfall estimates, the RAR
system, which reflected the

Since the rainfall estimates in the RAR system were improved by the

As a result of the rainfall estimation bias correction methods,
Table 4 shows the accuracy of the rainfall estimates for each rainfall
estimation bias method and each rainfall type. In Table 4a, Mean
Absolute Error (MAE) of the

Figure 11 shows the rainfall estimate images of the AWS, the

This paper focuses on correcting the reflectivity measurement bias
(

As a result of the

Since the rainfall estimates in the RAR system have been improved by
the

Therefore, in this paper, it is proven that the accuracy of the
rainfall estimates in the RAR system, to which the

This research was supported by the Daejin University Research Grants in 2015.