A Neural-Network Quality Control scheme for improved Quantitative Precipitation Estimation accuracy on the UK weather radar network
| العنوان: | A Neural-Network Quality Control scheme for improved Quantitative Precipitation Estimation accuracy on the UK weather radar network |
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| المؤلفون: | David A. Warde, Nawal Husnoo, Timothy Darlington, Sebastián M. Torres |
| المصدر: | Journal of Atmospheric and Oceanic Technology. |
| بيانات النشر: | American Meteorological Society, 2021. |
| سنة النشر: | 2021 |
| مصطلحات موضوعية: | Scheme (programming language), Atmospheric Science, Quantitative precipitation estimation, Artificial neural network, Computer science, media_common.quotation_subject, Control (management), Real-time computing, Ocean Engineering, law.invention, law, Weather radar, Quality (business), computer, computer.programming_language, media_common |
| الوصف: | In this work, we present a new Quantitative-Precipitation-Estimation (QPE) quality-control (QC) algorithm for the UK weather radar network. The real-time adaptive algorithm uses a neural network (NN) to select data from the lowest useable elevation scan to optimize the combined performance of two other radar data correction algorithms: ground clutter mitigation (using CLEAN-AP) and vertical profile of reflectivity (VPR) correction. The NN is trained using 3D tiles of observed uncontaminated weather signals that are systematically combined with ground-clutter signals collected under dry weather conditions. This approach provides a way to simulate radar signals with a wide range of clutter contamination conditions and with realistic spatial structures while providing the uncontaminated “truth” with respect to which the performance of the QC algorithm can be measured. An evaluation of QPE products obtained with the proposed QC algorithm demonstrates superior performance as compared to those obtained with the QC algorithm currently used in operations. Similar improvements are also illustrated using radar observations from two periods of prolonged precipitation, showing a better balance between overestimation errors from using clutter-contaminated low-elevation radar data and VPR-induced errors from using high-elevation radar data. |
| تدمد: | 1520-0426 0739-0572 |
| DOI: | 10.1175/jtech-d-20-0120.1 |
| URL الوصول: | https://explore.openaire.eu/search/publication?articleId=doi_________::79b504c88869edc60052bc34430975b3 https://doi.org/10.1175/jtech-d-20-0120.1 |
| رقم الانضمام: | edsair.doi...........79b504c88869edc60052bc34430975b3 |
| قاعدة البيانات: | OpenAIRE |
| تدمد: | 15200426 07390572 |
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| DOI: | 10.1175/jtech-d-20-0120.1 |