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ZENODO
Dataset . 2019
License: CC BY
Data sources: Datacite
image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
ZENODO
Dataset . 2019
License: CC BY
Data sources: ZENODO
image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
ZENODO
Dataset . 2019
License: CC BY
Data sources: ZENODO
ZENODO
Dataset . 2019
Data sources: ZENODO
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EVALUATION OF RADAR-BASED PRECIPITATION DATASETS FOR APPLICATIONS IN THE LOUISIANA COASTAL MASTER PLAN

Authors: Ridwana Sharif; Emad Habib; Mohamed ElSaadani;

EVALUATION OF RADAR-BASED PRECIPITATION DATASETS FOR APPLICATIONS IN THE LOUISIANA COASTAL MASTER PLAN

Abstract

A suite of hydrological and ecological models is used in the Louisiana Coastal Master Plan (CMP) Integrated Compartment Model (ICM) to assess potential benefits of restoration and protection projects. A main driving input for these models is precipitation. Since precipitation is considered a major source of freshwater in coastal Louisiana (50-60 inches/year), accurate information about its magnitudes and spatial and temporal distributions is critical for successful implementation of the ICM and other CMP-related modeling studies. Accurate information on precipitation is also critical for establishing reliable and representative water budget analyses for coastal Louisiana. In contrast to the sparse availability of rain gauges, weather radars can provide high-resolution rainfall estimates with excellent spatial resolutions and coverage over coastal Louisiana. The potential value of radar-based precipitation products has not been capitalized on yet by the CMP studies. Louisiana is covered by several National Weather Service (NWS) NEXRAD radar stations, with the two radar stations located near Slidell and Lake Charles being the most relevant to coastal Louisiana. The main objective of the graduate research project is to perform a regional-scale assessment of radar-rainfall datasets available over coastal Louisiana and evaluate whether they can be directly used by the CMP studies. Unlike past assessment studies, which were geared towards short-term applications (e.g., flood prediction and forecasting), the focus of this study will be on assessing accuracy aspects that are of direct relevance to the CMP applications. The study will examine (a) the reproducibility of inter-annual and intra-annual variability by the radar dataset, (b) the representation of extreme rainfall events, and (c) the representation of spatial distributions of annual and seasonal rainfall across various regions of the coast with distinctly different climatic regimes. The study will provide the CMP with quantitative assessment on the advantages and limitations of radar-based precipitation datasets and whether they can be readily used by the ICM models. This research will also provide insight on the levels of uncertainties in the radar-rainfall datasets and the implications for these uncertainties for the ICM models. Please make sure you work with the latest version of this data set only (check the panel on the right for versioning).

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citations
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
Average
Average
Average