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Tornadoes are severe convective weather exhibiting localized and sudden occurrences. Weather radar is widely regarded as the most effective tool for monitoring tornadoes and issuing early warnings. Dual-polarization updating has significantly improved tornado prediction and forecasting abilities. This article proposes an innovative tornado detection algorithm based on XGBoost which is suitable for dual-polarization radar data, was upgraded and has been used in China since 2019, and has been applied in the Tornado Key Open Laboratory of the China Meteorological Administration. The characteristics associated with the velocity attributes, reflectivity, velocity spectrum width, differential reflectivity, and correlation coefficient are used in the algorithm to achieve real-time tornado detection. Experimental evaluations have demonstrated that the proposed algorithm significantly improves tornado detection rates and leading times. Compared with the traditional TDA-RF algorithm based on Doppler weather radar data, the TDA-XGB algorithm introduces dual polarization parameters (such as differential reflectivity and the correlation coefficient), which effectively improves tornado identification performance. In addition, the TDA-XGB algorithm combines artificial intelligence-assisted learning to optimize the traditional algorithm based on the tornado vortex signature (TVS) and tornado debris signature (TDS), further improving the detection effect. Furthermore, the algorithm provides classification probabilities in the genesis and evolution of tornadoes, thereby supporting forecasters in their efforts to anticipate and issue timely tornado warnings. [ABSTRACT FROM AUTHOR]

BACKGROUND: Root knot nematodes (RKNs) pose a great threat to agricultural production worldwide. The bacterial nematocides have received increasing attention due to their safe and efficient control against RKNs. Here, we investigated the biocontrol efficacy of Paenibacillus polymyxa J2‐4 against Meloidogyne incognita in the field and analyzed the rhizosphere microbiome of cucumber under nematode infection after application of the J2‐4 strain. Furthermore, a biomarker strain of Pseudomonas spp. was isolated from the J2‐4‐inoculated rhizosphere soil, and its nematocidal activity and growth‐promoting effect on host plants were determined. In addition, chemotaxis assay of P. fluroescens ZJ5 toward root exudates was carried out. RESULTS: The field experiment demonstrated that P. polymyxa J2‐4 could effectively suppressed gall formation in cucumber plants, with the galling index reduced by 67.63% in 2022 and 65.50% in 2023, respectively, compared with controls. Meanwhile, plant height and yield were significantly increased in J2‐4 treated plants compared with controls. Metagenomic analysis indicated that J2‐4 altered the rhizosphere microbial communities. The relative abundance of Pseudomonas spp. was notably enhanced in the J2‐4 group, which was consistent with Linear discriminant analysis Effect Size results that Pseudomonas was determined as one of the biomarkers in the J2‐4 group. Furthermore, the ZJ5 strain, one of the biomarker Pseudomonas strains, was isolated from the J2‐4‐inoculated rhizosphere soil and was identified as Pseudomonas fluorescens. In addition, P. fluorescens ZJ5 exhibited high nematicidal activity in vitro and in vivo, with 99.20% of the mortality rate of M. incognita at 24 h and 69.75% of gall index reduction. The biocontrol efficiency of the synthetic community of ZJ5 plus J2‐4 was superior to that of any other single bacteria against M. incognita. Additionally, ZJ5 exhibited great chemotaxis ability toward root exudates inoculated with J2‐4. CONCLUSION: Paenibacillus polymyxa J2‐4 has good potential in the biological control against M. incognita under field conditions. Enrichment of the beneficial bacteria Pseudomonas fluorescens ZJ5 in the J2‐4‐inoculated rhizosphere soil contributes to M. incognita management. © 2024 Society of Chemical Industry. [ABSTRACT FROM AUTHOR]