Open AccessBook
Characterization of ceramics
Ronald E. Loehman
- 01 Jan 1971
2.2K
TL;DR: This article reviewed the principles of Doppler radar and emphasized the quantitative measurement of meteorological parameters, and illustrated the relation of radar data and images to atmospheric phenomena such as tornadoes, microbursts, waves, turbulence, density currents, hurricanes, and lightning.
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Abstract: This book reviews the principles of Doppler radar and emphasizes the quantitative measurement of meteorological parameters. It illustrates the relation of Doppler radar data and images to atmospheric phenomena such as tornadoes, microbursts, waves, turbulence, density currents, hurricanes, and lightning.
Geared toward upper-level undergraduates and graduate students, this text was written by two scientists at the National Severe Storms Laboratory in Norman, Oklahoma, a division of the National Oceanic and Atmospheric Administration. Topics include electromagnetic waves and propagation, weather signals and their Doppler spectra, weather signal processing, measurements of precipitation and turbulence, and observations of winds and storms as well as fair weather. Radar images and photographs of weather phenomena highlight the text.
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Citations
Differential-frequency Doppler weather radar: Theory and experiment
TL;DR: In this paper, the authors present theoretical calculations of the differential reflectivity and Doppler for several frequency pairs including those proposed for the Global Precipitation Mission (GPM) at 13.6 and 35 GHz.
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Direct Observations of Coherent Backscatter of Radar Waves in Precipitation
TL;DR: In this article, the cross-correlation function of the complex amplitudes in the direction of propagation calculated for nearest neighbor range bins was developed for precipitation in both rain and snow.
•Dissertation
Evaluation of precipitation forecasts by polarimetric radar
Monika Pfeifer
- 04 May 2007
TL;DR: In this paper, a forward operator SynPolRad (Synthetic Polarimetric Radar) has been developed to evaluate the performance of a mesoscale radar forward operator for modeling precipitation in high-resolution numerical weather prediction.
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Atmospheric turbulence component of the innovation covariance
TL;DR: In this paper, the authors derived the innovation covariance for general atmospheric turbulence conditions and for climatologies of upper-level turbulence based on aircraft data and defined the effective spatial filter of the forecast model to produce a consistent definition of measurement error and model error.
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