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

Crisologo, Irene; Heistermann, Maik

doi:10.5194/amt-13-645-2020

Cited by 3 publications

(6 citation statements)

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“…Calibrating ground‐based radars using space‐borne radar data have seen ongoing investigation and application over the last decade (Crisologo & Heistermann, 2019; Schwaller & Morris, 2011; Warren et al, 2018, 2020) and are now used operationally in Australia and implemented in open source radar software (https://docs.wradlib.org/en/stable/). Fig.…”

Section: Methodsmentioning

confidence: 99%

Quantifying Hail and Lightning Risk Factors Using Long‐Term Observations Around Australia

et al. 2020

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There is a growing need to better understand and quantify risks associated with extreme weather, including severe thunderstorm-related hazards such as hail and lightning. Hail occurrence based on a long-term archive of radar observations is presented for the first time in many temperate and subtropical regions of Australia, together with lightning observations from a ground-based network of sensors. Mean monthly and hourly occurrence frequencies are examined for hail and lightning. Environmental conditions obtained from hourly reanalysis data indicate stronger wind shear on average for hail than lightning. The environmental conditions also indicate higher freezing levels on average for lightning than hail. These environmental differences provide plausible physical reasons for observed differences between hail and lightning climatology through the year. The study results are intended to help inform future planning and preparedness for thunderstorm-related risks, including for severe weather forecasting and climate risk applications. Plain Language Summary Natural hazards caused by thunderstorms, such as hail and lightning, can have severe impacts on society. Radar data are used to examine hail events at 10 Australian locations, and comparisons are made with lightning observations. Results are averaged over several years to calculate average annual values, including for the hourly and the monthly number of the hail and the lightning events. We find differences between hail and lightning events in the environmental conditions that they occur in. These environmental differences are found to help explain differences through the year in the average risk of occurrence of hail or lightning. The results are intended to enhance resilience in relation to thunderstorm-related risks and provide guidance for extreme weather modeling and climate adaptation purposes.

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Section: Methodsmentioning

confidence: 99%

Quantifying Hail and Lightning Risk Factors Using Long‐Term Observations Around Australia

et al. 2020

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“…Compared to the method of Cao et al. (2013) adopted in previous works (Crisologo & Heistermann, 2020; Warren et al., 2018), this new method makes use of local PSD retrievals based on the DPR.…”

Section: Methodsmentioning

confidence: 99%

“…(2018) found that the variation in reflectivity bias over time can reach around 5 dB for radars near Sydney, Australia. Crisologo and Heistermann (2020) also identified low biases of about 5 and 14 dB for two radars in Philippines.…”

Section: Introductionmentioning

confidence: 92%

“…In this study, we convert the Ku-band VPR observations from GPM-DPR to equivalent C-band or S-band data based on the method of Zhu et al (2021). Compared to the method of Cao et al (2013) adopted in previous works (Crisologo & Heistermann, 2020;Warren et al, 2018), this new method makes use of local PSD retrievals based on the DPR.…”

Section: Reflectivity Frequency Correctionmentioning

confidence: 99%

“…Moreover, most of the aforementioned studies focused on the observations in the rain region and ignored the attenuation of the SR observations, with the exception of Warren et al (2018). Furthermore, only a few studies, such as Crisologo et al (2018) and Crisologo and Heistermann (2020), investigated the impact of partial beam blocking on the GR-SR comparison, and they only reduced the weight of the partially blocked observations rather than eliminating these low-quality data. In addition, different studies have used different approaches to match GR with SR for the inconsistent sampling geometries, but have not compared their accuracy.…”

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confidence: 99%

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Improving CINRAD Radar QPE Through GPM‐DPR Reflectivity Bias Correction

Zhu,

Qi,

2023

Water Resources Research

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Reflectivity bias of ground‐based weather radar (GR) is a common error source that can lead to biases in quantitative precipitation estimation (QPE). In this study, the Dual‐frequency Precipitation Radar onboard the Global Precipitation Measurement Mission Core Observatory (GPM‐DPR) is used to correct GR reflectivity bias. The reflectivity bias correction comprehensively considers variations in GR–DPR reflectivity difference associated with radar frequency difference, data matching approach, precipitation type, GR orographic beam blockage, attenuation, and sample size. The criteria to perform a robust GR–DPR comparison are presented. Disdrometer validation, based on 8 typical rain events from April to August 2019, demonstrates that GPM‐DPR can effectively reduce GR reflectivity bias to ±1 dB. For the period from June to August 2021, the reflectivity correction method is applied to 17 operational GRs. The multi‐radar QPE shows a significant increase in correlation coefficient (from 0.62 to 0.71), and decreases in relative mean bias (from ‐0.28 to ‐0.19) and root mean square error (from 3.96 mm to 3.54 mm) compared to rain gauges. These results confirm that GPM‐DPR can effectively correct the reflectivity biases of a GR network and improve multi‐radar QPE accuracy and consistency.This article is protected by copyright. All rights reserved.

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Real-Time Calibration and Monitoring of Radar Reflectivity on Nationwide Dual-Polarization Weather Radar Network

2021

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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.

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

Cited by 3 publications

References 32 publications

Quantifying Hail and Lightning Risk Factors Using Long‐Term Observations Around Australia

Quantifying Hail and Lightning Risk Factors Using Long‐Term Observations Around Australia

Improving CINRAD Radar QPE Through GPM‐DPR Reflectivity Bias Correction

Real-Time Calibration and Monitoring of Radar Reflectivity on Nationwide Dual-Polarization Weather Radar Network

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