Version 1
: Received: 31 December 2019 / Approved: 2 January 2020 / Online: 2 January 2020 (03:35:31 CET)
How to cite:
Zrnic, D.; Zhang, P.; Melnikov, V.; Mirkovic, D. Of Fire and Smoke Plumes, Polarimetric Characteristics. Preprints2020, 2020010005. https://doi.org/10.20944/preprints202001.0005.v1
Zrnic, D.; Zhang, P.; Melnikov, V.; Mirkovic, D. Of Fire and Smoke Plumes, Polarimetric Characteristics. Preprints 2020, 2020010005. https://doi.org/10.20944/preprints202001.0005.v1
Zrnic, D.; Zhang, P.; Melnikov, V.; Mirkovic, D. Of Fire and Smoke Plumes, Polarimetric Characteristics. Preprints2020, 2020010005. https://doi.org/10.20944/preprints202001.0005.v1
APA Style
Zrnic, D., Zhang, P., Melnikov, V., & Mirkovic, D. (2020). Of Fire and Smoke Plumes, Polarimetric Characteristics. Preprints. https://doi.org/10.20944/preprints202001.0005.v1
Chicago/Turabian Style
Zrnic, D., Valery Melnikov and Djordje Mirkovic. 2020 "Of Fire and Smoke Plumes, Polarimetric Characteristics" Preprints. https://doi.org/10.20944/preprints202001.0005.v1
Abstract
Weather surveillance radars routinely detect smoke of various origin. Of particular significance to the meteorological community are wildfires in forests and/or prairies. For example, one responsibility of the National Weather Service in the USA is to forecast fire outlooks as well as to monitor wild fire evolution. Polarimetric variables have enabled relatively easy recognitions of smoke plumes in data fields of weather radars. Presented here are the fields of these variables from smoke plumes caused by grass fire, brush fire, and forest fire. Histograms of polarimetric data from plumes contrast these three cases. Most of the data are from the polarimetric Weather Surveillance Radar 1988 Doppler (WSR-88D aka Nexrad, 10 cm wavelength) hence the wavelength does not influence these comparisons. Nevertheless, in one case simultaneous observations of a plume by the operational Terminal Doppler Weather Radar (TDWR, 5 cm wavelength) and a WSR-88D is used to infer backscattering characteristic and hence sizes of dominant contributors to the returns. In addition, comparisons with observations by other investigators of plumes from urban area but at a 5 cm wavelength are made. To interpret some measurements Computational Electromagnetics (CEM) tools are applied.
Environmental and Earth Sciences, Atmospheric Science and Meteorology
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
