Editorial Type:
Article
Article Type:
Research Article

Evolving Multisensor Precipitation Estimation Methods: Their Impacts on Flow Prediction Using a Distributed Hydrologic Model

David Kitzmiller Office of Hydrologic Development, NOAA/National Weather Service, Silver Spring, Maryland

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Suzanne Van Cooten NOAA/National Severe Storms Laboratory, Office of Oceanic and Atmospheric Research, Norman, Oklahoma

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Feng Ding Office of Hydrologic Development, NOAA/National Weather Service, Silver Spring, Maryland

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Kenneth Howard NOAA/National Severe Storms Laboratory, Office of Oceanic and Atmospheric Research, Norman, Oklahoma

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Carrie Langston Cooperative Institute for Mesoscale Meteorological Studies, Norman, Oklahoma

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Jian Zhang NOAA/National Severe Storms Laboratory, Office of Oceanic and Atmospheric Research, Norman, Oklahoma

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Heather Moser Cooperative Institute for Mesoscale Meteorological Studies, Norman, Oklahoma

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Yu Zhang Office of Hydrologic Development, NOAA/National Weather Service, Silver Spring, Maryland

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Jonathan J. Gourley NOAA/National Severe Storms Laboratory, Office of Oceanic and Atmospheric Research, Norman, Oklahoma

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Dongsoo Kim National Climatic Data Center, NOAA/National Environmental Satellite, Data, and Information Service, Camp Springs, Maryland

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David Riley Office of Hydrologic Development, NOAA/National Weather Service, Silver Spring, Maryland

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Print Publication:
01 Nov 2011
DOI:
https://doi.org/10.1175/JHM-D-10-05038.1
Page(s):
1414–1431
Restricted access

Abstract

This study investigates evolving methodologies for radar and merged gauge–radar quantitative precipitation estimation (QPE) to determine their influence on the flow predictions of a distributed hydrologic model. These methods include the National Mosaic and QPE algorithm package (NMQ), under development at the National Severe Storms Laboratory (NSSL), and the Multisensor Precipitation Estimator (MPE) and High-Resolution Precipitation Estimator (HPE) suites currently operational at National Weather Service (NWS) field offices. The goal of the study is to determine which combination of algorithm features offers the greatest benefit toward operational hydrologic forecasting. These features include automated radar quality control, automated Z–R selection, brightband identification, bias correction, multiple radar data compositing, and gauge–radar merging, which all differ between NMQ and MPE–HPE. To examine the spatial and temporal characteristics of the precipitation fields produced by each of the QPE methodologies, high-resolution (4 km and hourly) gridded precipitation estimates were derived by each algorithm suite for three major precipitation events between 2003 and 2006 over subcatchments within the Tar–Pamlico River basin of North Carolina. The results indicate that the NMQ radar-only algorithm suite consistently yielded closer agreement with reference rain gauge reports than the corresponding HPE radar-only estimates did. Similarly, the NMQ radar-only QPE input generally yielded hydrologic simulations that were closer to observations at multiple stream gauging points. These findings indicate that the combination of Z–R selection and freezing-level identification algorithms within NMQ, but not incorporated within MPE and HPE, would have an appreciable positive impact on hydrologic simulations. There were relatively small differences between NMQ and HPE gauge–radar estimates in terms of accuracy and impacts on hydrologic simulations, most likely due to the large influence of the input rain gauge information.

Corresponding author address: David Kitzmiller, Office of Hydrologic Development, NOAA/National Weather Service, 1325 East West Highway, Silver Spring, MD 20910. E-mail: david.kitzmiller@noaa.gov

Abstract

This study investigates evolving methodologies for radar and merged gauge–radar quantitative precipitation estimation (QPE) to determine their influence on the flow predictions of a distributed hydrologic model. These methods include the National Mosaic and QPE algorithm package (NMQ), under development at the National Severe Storms Laboratory (NSSL), and the Multisensor Precipitation Estimator (MPE) and High-Resolution Precipitation Estimator (HPE) suites currently operational at National Weather Service (NWS) field offices. The goal of the study is to determine which combination of algorithm features offers the greatest benefit toward operational hydrologic forecasting. These features include automated radar quality control, automated Z–R selection, brightband identification, bias correction, multiple radar data compositing, and gauge–radar merging, which all differ between NMQ and MPE–HPE. To examine the spatial and temporal characteristics of the precipitation fields produced by each of the QPE methodologies, high-resolution (4 km and hourly) gridded precipitation estimates were derived by each algorithm suite for three major precipitation events between 2003 and 2006 over subcatchments within the Tar–Pamlico River basin of North Carolina. The results indicate that the NMQ radar-only algorithm suite consistently yielded closer agreement with reference rain gauge reports than the corresponding HPE radar-only estimates did. Similarly, the NMQ radar-only QPE input generally yielded hydrologic simulations that were closer to observations at multiple stream gauging points. These findings indicate that the combination of Z–R selection and freezing-level identification algorithms within NMQ, but not incorporated within MPE and HPE, would have an appreciable positive impact on hydrologic simulations. There were relatively small differences between NMQ and HPE gauge–radar estimates in terms of accuracy and impacts on hydrologic simulations, most likely due to the large influence of the input rain gauge information.

Corresponding author address: David Kitzmiller, Office of Hydrologic Development, NOAA/National Weather Service, 1325 East West Highway, Silver Spring, MD 20910. E-mail: david.kitzmiller@noaa.gov
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