@Article{SchumacherSeSaNaMaJu:2022:ChLiWi,
               author = "Schumacher, Van{\'u}cia and Setzer, Alberto Waingort and Saba, 
                         Marcelo Magalh{\~a}es Fares and Naccarato, Kleber Pinheiro and 
                         Mattos, Enrique and Justino, Fl{\'a}vio",
          affiliation = "{Instituto Nacional de Pesquisas Espaciais (INPE)} and {Instituto 
                         Nacional de Pesquisas Espaciais (INPE)} and {Instituto Nacional de 
                         Pesquisas Espaciais (INPE)} and {Instituto Nacional de Pesquisas 
                         Espaciais (INPE)} and {Universidade Federal de Itajub{\'a} 
                         (UNIFEI)} and {Universidade Federal de Vi{\c{c}}osa (UFV)}",
                title = "Characteristics of lightning-caused wildfires in central Brazil in 
                         relation to cloud-ground and dry lightning",
              journal = "Agricultural and Forest Meteorology",
                 year = "2022",
               volume = "312",
                pages = "e108723",
                month = "Jan.",
             keywords = "Dry lightning, Lightning, Natural wildfires, Remote sensing.",
             abstract = "Lightning ignition is the major cause of natural wildfires in 
                         several regions worldwide. Determining if wildfires in remote 
                         uncontrolled areas result from natural lightning as opposed to 
                         anthropic action is a relevant and yet-unsolved challenge for 
                         large regions of the planet, with scientific and management 
                         implications ranging from environmental conservation to mitigation 
                         of climate-related emissions of gases and aerosols. Brazil is the 
                         country with one of the highest occurrences of lightning (50 to 
                         100 million/year) and which is also subject to numerous and vast 
                         wildfires (up to \∼600 × 103 km2/year) affecting all its 
                         biomes. To quantify natural fires we combined cloud-to-ground (CG) 
                         lightning and CG dry-lightning (CGDL) detected by a ground 
                         network, with fire pixels mapped by satellite remote sensing 
                         (AQUA, S-NPP and NOAA-20) over \∼1,8 × 106 km2 in Central 
                         Brazil, between 2015 to 2019. Lightning ignition candidates were 
                         selected based on the distance between fires and lightning in time 
                         and space. The selected cases were investigated according to 
                         annual and monthly distributions in space and time, to local 
                         weather at the time of occurrence and, electrical characteristics 
                         related to ignition. Space-time distributions of CG lightning, 
                         CGDL and of active fires were also analyzed. Results showed that 
                         the CGDLs pattern is not different from that of the overall CG 
                         lightning, with both presenting similar kernel density, polarity 
                         and peak current. The lightning candidates indicated predominance 
                         of negative polarity and peak current frequency below 20 kA. In 
                         this range, average values for weather conditions for CG lightning 
                         matched to fires (CGDL matched to fires) had: precipitation 6 mm 
                         (< 1 mm), relative humidity 57 % (48 %), and temperature 
                         \∼30°C and wind speed of \∼ 2 m.s\−1 for 
                         both. The results showed that satellite detection of active fires 
                         is a useful tool to identify lightning-induced wildfires.",
                  doi = "10.1016/j.agrformet.2021.108723",
                  url = "http://dx.doi.org/10.1016/j.agrformet.2021.108723",
                 issn = "0168-1923",
             language = "en",
           targetfile = "schumacher_characteristics.pdf",
        urlaccessdate = "29 jun. 2024"
}