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Ami Arthur

Researcher at Cooperative Institute for Mesoscale Meteorological Studies

Publications -  14
Citations -  1281

Ami Arthur is an academic researcher from Cooperative Institute for Mesoscale Meteorological Studies. The author has contributed to research in topics: Flash flood & Quantitative precipitation estimation. The author has an hindex of 9, co-authored 14 publications receiving 1070 citations. Previous affiliations of Ami Arthur include University of Oklahoma & National Oceanic and Atmospheric Administration.

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National Mosaic and Multi-Sensor QPE (NMQ) System: Description, Results, and Future Plans

TL;DR: The National Mosaic and Multi-sensor Quantitative Precipitation Estimation (NMQ) system was initially developed from a joint initiative between the National Oceanic and Atmospheric Administration's National Severe Storms Laboratory, the Federal Aviation Administration's Aviation Weather Research Program, and the Salt River Project.
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Multi-Radar Multi-Sensor (MRMS) Quantitative Precipitation Estimation: Initial Operating Capabilities

TL;DR: In this paper, the authors provide an overview of the initial operating capabilities of MRMS QPE products and present a suite of severe weather and quantitative precipitation estimation (QPE) products, which can be integrated with high-resolution numerical weather prediction model data, satellite data, and lightning and rain gauge observations.
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A Unified Flash Flood Database across the United States

TL;DR: In this article, the authors present a large-scale dataset of U.S. flash flooding in terms of spatiotemporal behavior and specificity of impacts, which is the first of its kind to assemble, reprocess, describe, and disseminate a georeferenced database providing a longterm, detailed characterization of flash flooding.
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Probabilistic precipitation rate estimates with ground‐based radar networks

TL;DR: In this paper, the uncertainty structure of radar quantitative precipitation estimation (QPE) is largely unknown at fine spatiotemporal scales near the radar measurement scale and a new method is proposed and called PRORATE for probabilistic QPE using radar observations of rate and typology estimates.