Adaptive Radar Data Quality Control and Ensemble-Based Assimilation for Analyzing and Forecasting High-Impact Weather

AbstractRecently, a radar data quality control algorithm has been devised to discriminate between weather echoes and echoes due to nonmeteorological phenomena, such as bioscatter, instrument artifacts, and ground clutter (Lakshmanan et al.), using the values of polarimetric moments at and around a range gate. Because the algorithm was created by optimizing its weights over a large reference dataset, statistical methods can be employed to examine the importance of the different variables in the context of discriminating between weather and no-weather echoes. Among the variables studied for their impact on the ability to identify and censor nonmeteorological artifacts from weather radar data, the method of successive permutations ranks the variance of Zdr, the reflectivity structure of the virtual volume scan, and the range derivative of the differential phase on propagation [PhiDP (Kdp)] as the most important. The same statistical framework can be used to study the impact of calibration errors in variables such as Zdr. The effects of Zdr calibration errors were found to be negligible.

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A radar data quality control scheme used in hydrology

Physics and Chemistry of the Earth Part B Hydrology Oceans and Atmosphere ◽

10.1016/s1464-1909(00)00168-4 ◽

2000 ◽

Vol 25 (10-12) ◽

pp. 1141-1146 ◽

Cited By ~ 5

Author(s):

T Einfalt ◽

B Maul-Kötter ◽

S Spies

Keyword(s):

Quality Control ◽

Data Quality ◽

Radar Data ◽

Data Quality Control ◽

Control Scheme ◽

Quality Control Scheme

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Updates on the Radar Data Quality Control in the MRMS Quantitative Precipitation Estimation System

Journal of Atmospheric and Oceanic Technology ◽

10.1175/jtech-d-19-0165.1 ◽

2020 ◽

Vol 37 (9) ◽

pp. 1521-1537

Author(s):

Lin Tang ◽

Jian Zhang ◽

Micheal Simpson ◽

Ami Arthur ◽

Heather Grams ◽

...

Keyword(s):

Quality Control ◽

Data Quality ◽

Wind Farm ◽

Critical Role ◽

Radar Data ◽

Data Quality Control ◽

Quantitative Precipitation Estimation ◽

Precipitation Estimation ◽

Estimation System ◽

Environmental Prediction

AbstractThe Multi-Radar-Multi-Sensor (MRMS) system was transitioned into operations at the National Centers for Environmental Prediction in the fall of 2014. It provides high-quality and high-resolution severe weather and precipitation products for meteorology, hydrology, and aviation applications. Among processing modules, the radar data quality control (QC) plays a critical role in effectively identifying and removing various nonhydrometeor radar echoes for accurate quantitative precipitation estimation (QPE). Since its initial implementation in 2014, the radar QC has undergone continuous refinements and enhancements to ensure its robust performance across seasons and all regions in the continental United States and southern Canada. These updates include 1) improved melting-layer delineation, 2) clearance of wind farm contamination, 3) mitigation of corrupt data impacts due to hardware issues, 4) mitigation of sun spikes, and 5) mitigation of residual ground/lake/sea clutter due to sidelobe effects and anomalous propagation. This paper provides an overview of the MRMS radar data QC enhancements since 2014.

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