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  • Author or Editor: Suzanne Van Cooten x
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Suzanne Van Cooten
,
Kimberly L. Elmore
,
Donald E. Barbé
,
J. Alex McCorquodale
, and
Denise J. Reed

Abstract

This study quantifies the spatial distribution of precipitation patterns on an annual basis for southeast Louisiana. To compile a long-term record of 24-h rainfall, rainfall reports collected by National Weather Service (NWS) cooperative observers were gathered from National Climatic Data Center (NCDC) archives, private collections of observational data held at regional and local libraries, NWS offices, and local utility providers. The reports were placed into a digital database in which each station’s record was subjected to an extensive quality control process. This process produced a database of daily rainfall reports for 59 south Louisiana stations for the period 1836–2002, with extensive documentation for each site outlining the differences between the study’s data and the data available from the NCDC Web page. A statistical methodology was developed to determine if the four NCDC climate divisions for southeast Louisiana accurately depict average monthly rainfall for the area. This method employs cluster analysis, using Euclidean distance as the measure of dissimilarity for the clustering technique. To resolve missing rainfall observations, an imputation scheme was developed that uses the two most similar stations (based on Euclidean distance) to determine appropriate values for missing rainfall observations. Results from this testing structure show statistical evidence of precipitation microclimates across south Louisiana at higher spatial scales than those of the NCDC climate zones. Quantifying the spatial extent of daily precipitation and documenting historical trends of precipitation provides critical design information for regional infrastructure within this highly vulnerable area of the central Gulf Coast region.

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David Kitzmiller
,
Suzanne Van Cooten
,
Feng Ding
,
Kenneth Howard
,
Carrie Langston
,
Jian Zhang
,
Heather Moser
,
Yu Zhang
,
Jonathan J. Gourley
,
Dongsoo Kim
, and
David Riley

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 ZR 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 ZR 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.

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