Abstract
Monitoring calibration bias in reflectivity (ZH) in an operational S-band dual-polarization weather radar is the primary requisite for monitoring and prediction (nowcasting) of severe weather and routine weather forecasting using a weather radar network. For this purpose, we combined methods based on self-consistency (SC), ground clutter (GC) monitoring, and intercomparison to monitor the ZH in real time by complementing the limitations of each method. The absolute calibration bias can be calculated based on the SC between dual-polarimetric observations. Unfortunately, because SC is valid for rain echoes, it is impossible to monitor reflectivity during the non-precipitation period. GC monitoring is an alternative method for monitoring changes in calibration bias regardless of weather conditions. The statistics of GC ZH near radar depend on the changes in radar system status, such as antenna pointing and calibration bias. The change in GC ZH relative to the baseline was defined as the relative calibration adjustment (RCA). The calibration bias was estimated from the change in RCA, which was similar to that estimated from the SC. The ZH in the overlapping volume of adjacent radars was compared to verify the homogeneity of ZH over the radar network after applying the calibration bias estimated from the SC. The mean bias between two radars was approximately 0.0 dB after correcting calibration bias. We can conclude that the combined method makes it possible to use radar measurements, which are immune to calibration bias, and to diagnose malfunctioning radar systems as soon as possible.
Highlights
A heterogeneous radar network consisting of different frequencies, models, manufacturers, dual-polarization capability, and production years makes it difficult to maintain the same quality of radar data across the network
The miscalibration strongly affects the overall performance of network-based operational radar applications, such as nationwide quantitative precipitation estimation (QPE), quantitative precipitation forecasting, and hydrometeor classification algorithm
It was proven that relative calibration adjustment (RCA) was stable despite the short period for constructing the GC map (GCM)
Summary
Considering the spatial coverage of a single weather radar, the nationwide radar network can enhance the utilization of a weather radar in monitoring severe weather, radar rainfall estimation, hydrometeor classification algorithm, and short-term range forecasting of precipitation. Uniform quality maintenance among the individual radar observations in the network is the primary concern in terms of the utilization of the entire network. Even in a homogeneous radar network, establishing a regular radar calibration and monitoring procedure is essential to ensure high-quality data because miscalibration of individual radars results in considerable discrepancies in radar measurements over the network. The miscalibration strongly affects the overall performance of network-based operational radar applications, such as nationwide quantitative precipitation estimation (QPE), quantitative precipitation forecasting, and hydrometeor classification algorithm
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