Of Fire and Smoke Plumes, Polarimetric Radar Characteristics

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 wildfire 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 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. To interpret these measurements, Computational Electromagnetics (CEM) tools are applied. For one wildfire from Oklahoma, radar and satellite (GOES-16, Geostationary Operational Environmental Satellite) images are analyzed. The case demonstrates a potential to forecast fire intensification caused by a very rapid cold front. Finally, we suggest a possible way to extract the smoke plume return from the class of nonmeteorological scatterers.

Influence of Convectively Driven Flows in the Course of a Large Fire in Portugal: The Case of Pedrógão Grande

“…This was associated with much lower magnitude reflectivity cores in the current study. Furthermore, the MARC was computed with an unfavourable viewing angle, with the front of the MCS not perpendicular to the radar beam, which may have decreased the computed magnitudes [64]. Nevertheless, while those studies in the USA were specifically concerned with the onset of surface damaging winds, the current study also focused on winds of relatively lower magnitude, as these still can influence fires.…”

Evaluation of Wildfire Plume Injection Heights Estimated from Operational Weather Radar Observations Using Airborne Lidar Retrievals

“…The WSR‐88Dis also designed to detect how similarly the horizontally and vertically polarized returns (of energy) are behaving; this similarity is quantified using the correlation coefficient (Doviak et al., 2000). Atmospheric scatterers that are highly variable in size and shape (such as debris or birds) will likely have less similarly behaving horizontal and vertical returns, leading to lower correlation coefficient values (Melnikov et al., 2008; Zrnic et al., 2020); scatterers that are more uniform in size and shape (such as rain droplets or snow) will have more similarly behaving horizontal and vertical returns, leading to higher correlation coefficient values (Liu & Chandrasekar, 2000).…”

“…Previous studies utilized polarimetric data to identify smoke plumes, observing reflectivity values on the range of 10–25 dBZ (Lang et al., 2014; Zrnic et al., 2020). Therefore, based on the existing literature, reflectivity threshold values for lofted debris were tested in a range of 5–20 dBZ (Figure 2a).…”

Estimating Fire Radiative Power Using Weather Radar Products for Wildfires

“…These filters are based on previous work (Jones & Christopher, 2009) and reduce the possibility of including reflectivity enhancements that are at altitude and also removing rain and drizzle echoes, which tend to show correlation coefficient greater than 0.9 (Liu & Chandrasekar, 2000). Correlation coefficient for pyrometeors tends to be below 0.8 (Melnikov et al., 2008; Zrnic et al., 2020). Figures 1b and 1h shows examples of this processing, showing the pyrometeor plume is captured when it's present, but there are often other features included not related to the fire which are discussed in Section 3.1.…”