Wildfires pose a critical threat to human life, property safety, and ecological systems. Weather radar has demonstrated significant potential as an effective wildfire monitoring tool. However, as the dominant sources of radar backscattering signatures, the mechanisms of particle scattering are unclear. In this study, the microwave scattering characteristics of pyrometeors (lofted above wildfires that are composed of the byproducts of fuel combustion) were systematically investigated through integrated experimental measurements and numerical simulations. The complex permittivity of pyrometeors was measured across 1–18 GHz using the coaxial transmission/reflection method. The scattering properties of pyrometeors at microwave frequencies were determined using the discrete dipole approximation method. Results indicate that the scattering polarization characteristics of pyrometeors are regulated by particle size and aspect ratio within microwave frequencies. The magnitude ofF11(π) is positively correlated with particle aspect ratio under horizontal orientation. The backscattering cross section exhibits an inverse proportionality to incident wavelength across L–Ku-bands and a positive proportionality to particle size. C-, X-, and Ku-band radars show high sensitivity to pyrometeor morphology, necessitating precise shape modeling for accurate echo interpretation. Differential reflectivity ZDR exhibits nonmonotonic frequency dependence within the Mie scattering regime, confirming the joint influence of particle size and shape on polarimetric response. The effective reflectivity factor derived from this experiment falls within the range of observed measurements, supporting the feasibility of this study method in modeling the microwave scattering properties of pyrometeor particles.

SUMMARY: Single-cell RNA-seq (scRNA-seq) is a powerful technique for decoding the complex cellular compositions in the tumor microenvironment (TME). As previous studies have defined many meaningful cell subtypes in several tumor types, there is a great need to computationally transfer these labels to new datasets. Also, different studies used different approaches or criteria to define the cell subtypes for the same major cell lineages. The relationships between the cell subtypes defined in different studies should be carefully evaluated. In this updated package scCancer2, designed for integrative tumor scRNA-seq data analysis, we developed a supervised machine learning framework to annotate TME cells with annotated cell subtypes from 15 scRNA-seq datasets with 594 samples in total. Based on the trained classifiers, we quantitatively constructed the similarity maps between the cell subtypes defined in different references by testing on all the 15 datasets. Secondly, to improve the identification of malignant cells, we designed a classifier by integrating large-scale pan-cancer TCGA bulk gene expression datasets and scRNA-seq datasets (10 cancer types, 175 samples, 663 857 cells). This classifier shows robust performances when no internal confidential reference cells are available. Thirdly, scCancer2 integrated a module to process the spatial transcriptomic data and analyze the spatial features of TME. AVAILABILITY AND IMPLEMENTATION: The package and user documentation are available at http://lifeome.net/software/sccancer2/ and https://doi.org/10.5281/zenodo.10477296.