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Advancements and Characteristics of Gauge Ingest and Quality Control within the Multi-Radar Multi-Sensor System

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  • 1 aCooperative Institute for Mesoscale Meteorological Studies, University of Oklahoma, Norman, Oklahoma
  • | 2 bNOAA/OAR/National Severe Storms Laboratory, Norman, Oklahoma
  • | 3 cNational Weather Center Research Experience for Undergraduates, Norman, Oklahoma
  • | 4 dCentral Michigan University, Mt. Pleasant, Michigan
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Abstract

This study describes recent advancements in the Multi-Radar Multi-Sensor (MRMS) automated gauge ingest and quality control (QC) processes. A data latency analysis for the combined multiple gauge collection platforms provided guidance for a multiple-pass generation and delivery of gauge-based precipitation products. Various advancements to the gauge QC logic were evaluated over a 21-month period, resulting in an average of 86% of hourly gauge observations per hour being classified as useful. The fully automated QC logic was compared to manual human QC for a limited domain, which showed a >95% agreement in their QC reasoning categories. This study also includes an extensive evaluation of various characteristics related to the gauge observations ingested into the MRMS system. Duplicate observations between gauge collection platforms highlighted differences in site coordinates; moreover, errors in Automated Surface Observing System (ASOS) station site coordinates resulted in >79% of sites being located in a different MRMS 1-km grid cell. The ASOS coordinate analysis combined with examinations of other limitations regarding gauge observations highlight the need for robust and accurate metadata to further enhance the quality control of gauge data.

Significance Statement

This study examines an advanced quality control technique for the MRMS system and how it performs against manual quality control by forecasters, which showed >95% match for the reasoning of flagging a gauge; moreover, this study examines other characteristics pertaining to the ingest and quality control of automated gauge observations, including duplicate observations, errors in location, and the need for more robust metadata to improve hydrometeorological product verification and corrections.

© 2021 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Steven Martinaitis, steven.martinaitis@noaa.gov

Abstract

This study describes recent advancements in the Multi-Radar Multi-Sensor (MRMS) automated gauge ingest and quality control (QC) processes. A data latency analysis for the combined multiple gauge collection platforms provided guidance for a multiple-pass generation and delivery of gauge-based precipitation products. Various advancements to the gauge QC logic were evaluated over a 21-month period, resulting in an average of 86% of hourly gauge observations per hour being classified as useful. The fully automated QC logic was compared to manual human QC for a limited domain, which showed a >95% agreement in their QC reasoning categories. This study also includes an extensive evaluation of various characteristics related to the gauge observations ingested into the MRMS system. Duplicate observations between gauge collection platforms highlighted differences in site coordinates; moreover, errors in Automated Surface Observing System (ASOS) station site coordinates resulted in >79% of sites being located in a different MRMS 1-km grid cell. The ASOS coordinate analysis combined with examinations of other limitations regarding gauge observations highlight the need for robust and accurate metadata to further enhance the quality control of gauge data.

Significance Statement

This study examines an advanced quality control technique for the MRMS system and how it performs against manual quality control by forecasters, which showed >95% match for the reasoning of flagging a gauge; moreover, this study examines other characteristics pertaining to the ingest and quality control of automated gauge observations, including duplicate observations, errors in location, and the need for more robust metadata to improve hydrometeorological product verification and corrections.

© 2021 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Steven Martinaitis, steven.martinaitis@noaa.gov
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