Creation of Multisensor Precipitation Products from WSI NOWrad Reflectivity Data
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Creation of Multisensor Precipitation Products from WSI NOWrad Reflectivity Data

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  • Journal Title:
    Journal of Hydrologic Engineering
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  • Description:
    Radar and multisensor quantitative precipitation estimates (QPEs) have played a critical role in real-time hydrologic and weather predictions. The utility of this data set for hydrologic model calibration, however, is hampered by the limited data archive and the presence of data gaps. In the research reported in this paper, the use of a composite reflectivity data set is investigated, namely the Weather Services International (WSI) National Operational Weather radar (NOWrad) data set, to create an hourly QPE that would complement the National Weather Service (NWS) Stage III archive by filling in the latter’s gaps and potentially improving the latter’s accuracy. The paper starts from an inventory analysis of Stage III and NOWrad products, and through which it is found that as much as 17% of the Stage III data were missing for some of the regions, and some of these gaps can be filled using the NOWrad data. Then, the paper presents two experimental products, as follows: (1) NOWrad–Raw, based on variable Z(reflectivity)−R(rainrate) relationship derived for each month using monthly Cooperative Observer Porgram (COOP) station totals; and (2) NOWrad–bias-corrected mosaic (BMO), derived by bias-correcting the NOWrad–Raw using hourly rain gauge products. Then the two products along with Stage III products against independent hourly gauge reports over two locations [(1) Charlotte–Mecklenburg metropolitan area in North Carolina, and (2) Lower Colorado River drainage in central Texas]. The analyses over the two test sites reveal the following: (1) NOWrad–Raw product suffers from a negative overall and conditional bias, while the Stage III is closer to bias-neutral; and (2) NOWrad–Raw product, at least over the Charlotte–Mecklenburg area, can outperform the Stage III in terms of correlation with gauge data and skill in detecting light rainfall. Further analyses of the Raw and bias-corrected NOWrad suggest that negative conditional bias may be substantially improved, but there is a tendency to overcorrect the bias for the summer months, possibly due to the presence of false detections. The results show that NOWrad can be a viable source of high-resolution quantitative precipitation information and it complements the current NWS archive in several respects. Possible mechanisms for further improving the accuracy of the NOWrad QPE are discussed.
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  • Source:
    Journal of Hydrologic Engineering Vol. 22, Issue 5 (May 2017)
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  • Document Type:
    Journal Article
  • Rights Information:
    Accepted Manuscript
  • Compliance:
    Submitted
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