Assessment of the total lightning flash rate density (FRD) in northeast Brazil (NEB) based on TRMM orbital data from 1998 to 2013

Abstract The Northeast region of Brazil (NEB) concentrates on average 18 % of the total deaths associated with lightning strikes in Brazil. When considering population, the state of Piaui had the highest mortality rate in the region (1.8 deaths million−1), much higher than the national rate (0.8) and the NEB rate (0.5). This work aimed to evaluate the space-time distribution of total lightning (intracloud, cloud-to-cloud and cloud-to-ground) in NEB, covering the period from 1998 to 2013. For this purpose, we used data from the Lightning Imaging Sensor (LIS) aboard the Tropical Rainfall Measuring Mission (TRMM) satellite, which provided information on the occurrence of total lightning, and the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) sensor aboard on TERRA satellite, which provided the terrain elevation data to verify the influence of topography on the flash rate density (FRD). The full distribution was used to identify the hotspots (cities with the highest FRD), while the monthly distribution helped in the cluster analysis. NEB has great spatial and temporal variability of the recorded lightning rates, with average of 0–44.5 flash km-2 year−1. The regions with high total lightning rates are located in the states of Piaui, Maranhao and west of Bahia. The topography of the region seems to act as a facilitator of the convective process, leading to the formation of intense upward currents, essential for the generation of electric charges inside thunderstorms. CAPE values showed good relationship with lightning occurrence in the region. The cluster with the highest occurrence of lightning and hotspots is in the region of influence of the Intertropical Convergence Zone (ITCZ) and Mesoscale Convective Systems, suggesting an important relationship with large organized cloud systems.

[2]  I. Pinto,et al.  The influence of topography on the cloud-to-ground lightning density in South Brazil , 2009 .

[3]  E. Williams,et al.  Lightning, thermodynamic and hydrological comparison of the two tropical continental chimneys , 2004 .

[4]  P. Sentelhas,et al.  Solar Radiation Models and Gridded Databases to Fill Gaps in Weather Series and to Project Climate Change in Brazil , 2018, Advances in Meteorology.

[5]  J. H. Ward.  Hierarchical Grouping to Optimize an Objective Function , 1963 .

[6]  Robert A. Houze,et al.  Severe convection and lightning in subtropical South America , 2014 .

[7]  Steven J. Goodman,et al.  Regional Differences in Tropical Lightning Distributions , 2000 .

[8]  O. Pinto,et al.  Evaluation of BrasilDAT relative detection efficiency based on LIS observations and a numeric model , 2014, 2014 International Conference on Lightning Protection (ICLP).

[9]  G. Marshall.  Statistical methods in the atmospheric sciences, second edition D. S. Wilks. 1995. International Geophysics Series, Vol 59, Academic Press, 464pp. ISBN‐10: 0127519653. ISBN‐13: 978‐0127519654. £59.99. , 2007 .

[10]  Chuntao Liu,et al.  “Warm Rain” in the Tropics: Seasonal and Regional Distributions Based on 9 yr of TRMM Data , 2009 .

[11]  P. Lucio,et al.  Characterization of Rainfall Patterns in the Semiarid Brazil , 2018, Anuário do Instituto de Geociências - UFRJ.

[12]  M. Zeri,et al.  Cluster analysis applied to the spatial and temporal variability of monthly rainfall in Alagoas state, Northeast of Brazil , 2014 .

[13]  Bergson Guedes Bezerra,et al.  Análises da evapotranspiração de referência e do índice de aridez para o Nordeste do Brasil , 2018, Revista de Geociências do Nordeste.

[14]  Andreas Krause,et al.  The sensitivity of global wildfires to simulated past, present, and future lightning frequency , 2014 .

[15]  Identification of Hydromorphic Areas by Means of Spectral Analysis of Remote Sensing Data, as Support for the Preparation of Forensic Reports Issued to Evaluation of Rural Properties , 2014 .

[16]  On the sensitivity of cloud‐to‐ground lightning activity to surface air temperature changes at different timescales in São Paulo, Brazil , 2008 .

[17]  R. Mahmood,et al.  Lightning activity associated with precipitation and CAPE over Bangladesh , 2018 .

[18]  J. Molinari,et al.  CAPE in Tropical Cyclones , 2012 .

[19]  Konstantinos Lagouvardos,et al.  A ten-year analysis of cloud-to-ground lightning activity over the Eastern Mediterranean region , 2015 .

[20]  On Frontal Zone Analysis in the Tropical Region of the Northeast Brazil , 2016, Pure and Applied Geophysics.

[21]  V. B. Rao,et al.  Tropical Cloudiness over the South Atlantic Ocean , 1977 .

[22]  B. Bezerra,et al.  Detecting linear trend of reference evapotranspiration in irrigated farming areas in Brazil’s semiarid region , 2019, Theoretical and Applied Climatology.

[23]  K. C. Lima,et al.  Variabilidade espacial e temporal da precipitação na bacia hidrográfica do Rio Parnaíba, Nordeste do Brasil , 2019 .

[24]  John M. Hall,et al.  The Lightning Imaging Sensor , 1999 .

[25]  Manoel Alonso Gan,et al.  Upper tropospheric cyclonic vortices in the tropical South Atlantic , 1981 .

[26]  C. A. Morales Rodriguez.  Thunderstorm Efficiency Regimes in South America as Observed by STARNET and TRMM , 2019, Journal of Geophysical Research: Atmospheres.

[27]  J. Stape,et al.  Köppen's climate classification map for Brazil , 2013 .

[28]  Robert H. Holzworth,et al.  Relative detection efficiency of the World Wide Lightning Location Network , 2012 .

[29]  O. Pinto,et al.  Anomalous lightning activity over the Metropolitan Region of São Paulo due to urban effects , 2009 .

[30]  A. Collier,et al.  Lightning and the African ITCZ , 2011 .

[31]  Øyvind Saetra,et al.  Can CAPE Maintain Polar Lows , 2010 .

[32]  D. M. Lal,et al.  Effect of urbanization on lightning over four metropolitan cities of India , 2011 .

[33]  J. Lenters,et al.  On the Origin of the Bolivian High and Related Circulation Features of the South American Climate , 1997 .

[34]  Richard J. Blakeslee,et al.  Three Years of the Lightning Imaging Sensor Onboard the International Space Station: Expanded Global Coverage and Enhanced Applications , 2020, Journal of Geophysical Research: Atmospheres.

[35]  A. Dewan,et al.  Fatalities related to lightning occurrence and agriculture in Bangladesh , 2019 .

[36]  K.L. Cummins,et al.  An Overview of Lightning Locating Systems: History, Techniques, and Data Uses, With an In-Depth Look at the U.S. NLDN , 2009, IEEE Transactions on Electromagnetic Compatibility.

[37]  E. Williams,et al.  An Analysis of the Conditional Instability of the Tropical Atmosphere , 1993 .

[38]  Robert A. Houze,et al.  Orogenic Convection in Subtropical South America as Seen by the TRMM Satellite , 2011 .

[39]  M. Tippett,et al.  A Baseline for the Predictability of U.S. Cloud‐to‐Ground Lightning , 2018, Geophysical Research Letters.

[40]  K. C. Lima,et al.  Detecção de descargas elétricas atmosféricas em sistemas convectivos com dados do SIMEPAR , 2009 .

[41]  D. Siingh,et al.  Lightning and convective rain study in different parts of India , 2014 .

[42]  Edward J. Zipser,et al.  The global distribution of largest, deepest, and most intense precipitation systems , 2015 .

[43]  R. Houze.  100 Years of Research on Mesoscale Convective Systems , 2018 .

[44]  Paul Markowski,et al.  A Numerical Investigation of the Effects of Dry Air Aloft on Deep Convection , 2009 .

[45]  O. Pinto,et al.  Lightning Enhancement in the Amazon Region Due to Urban Activity , 2013 .

[46]  W. Petersen,et al.  Intraseasonal Forcing of Convection and Lightning Activity in the Southern Amazon as a Function of Cross-Equatorial Flow , 2006 .

[47]  J. Molinari,et al.  Projected increase in lightning strikes in the United States due to global warming , 2014, Science.

[48]  O. Pinto,et al.  Lightning casualty demographics in Brazil and their implications for safety rules , 2014 .

[49]  P. Murugavel,et al.  Anomalous electric field changes and high flash rate beneath a thunderstorm in northeast India , 2010 .

[50]  A. Blyth,et al.  A projected decrease in lightning under climate change , 2018, Nature Climate Change.

[51]  A. Barros,et al.  Orographic Effects during a Severe Wintertime Rainstorm in the Appalachian Mountains , 1998 .

[52]  Jian Yuan,et al.  Global Variability of Mesoscale Convective System Anvil Structure from A-Train Satellite Data , 2010 .

[53]  M. F. Correia,et al.  Descargas atmosféricas e interrupções de energia elétrica na área da CHESF: relação com variáveis atmosféricas em anos de El Niño e La Niña , 2008 .

[54]  S. Massie,et al.  Relative influence of meteorological conditions and aerosols on the lifetime of mesoscale convective systems , 2016, Proceedings of the National Academy of Sciences.

[55]  R. Mahmood,et al.  Spatial and temporal analysis of a 17-year lightning climatology over Bangladesh with LIS data , 2018, Theoretical and Applied Climatology.

[56]  Richard J. Blakeslee,et al.  TRMM LIS Climatology of Thunderstorm Occurrence and Conditional Lightning Flash Rates , 2015 .

[57]  Richard J. Blakeslee,et al.  Where Are the Lightning Hotspots on Earth , 2015 .

[58]  Carey W. King,et al.  Daily gridded meteorological variables in Brazil (1980–2013) , 2016 .

[59]  C. M. Santos e Silva,et al.  Climatology and trend analysis of extreme precipitation in subregions of Northeast Brazil , 2017, Theoretical and Applied Climatology.

[60]  J. Skindlov,et al.  Spatial and Temporal Distributions of Lightning over Arizona from a Power Utility Perspective , 1997 .

[61]  J. Wallace,et al.  Atmospheric Science: An Introductory Survey , 1977 .

[62]  Richard J. Blakeslee,et al.  THE CHUVA PROJECT How Does Convection Vary across Brazil , 2014 .

[63]  P. Bitzer.  Global distribution and properties of continuing current in lightning , 2017 .

[64]  E. Williams,et al.  Stroke multiplicity and horizontal scale of negative charge regions in thunderclouds , 2016 .

[65]  E. Williams,et al.  The physical origin of the land–ocean contrast in lightning activity , 2002 .

[66]  Richard J. Blakeslee,et al.  Gridded lightning climatology from TRMM-LIS and OTD: Dataset description , 2014 .

[67]  C. M. Santos e Silva,et al.  Precipitation and air temperature extremes in the Amazon and northeast Brazil , 2018, International Journal of Climatology.

[68]  David P. Yorty,et al.  WHERE ARE THE MOST INTENSE THUNDERSTORMS ON EARTH , 2006 .

[69]  K. Lagouvardos,et al.  A Climatology of Thunderstorms across Europe from a Synthesis of Multiple Data Sources , 2019, Journal of Climate.

[70]  V. Dubreuil,et al.  A detailed framework for the characterization of rainfall climatology in semiarid watersheds , 2019, Theoretical and Applied Climatology.

[71]  Daniel Vila,et al.  Climatological Behavior of Precipitating Clouds in the Northeast Region of Brazil , 2017 .

[72]  O. Pinto,et al.  Evidence of thermal and aerosol effects on the cloud‐to‐ground lightning density and polarity over large urban areas of Southeastern Brazil , 2003 .

[73]  William J. Koshak,et al.  Assessing the performance of the Lightning Imaging Sensor (LIS) using Deep Convective Clouds , 2014 .

[74]  Wenjuan Zhang,et al.  Lightning casualties and damages in China from 1997 to 2009 , 2011, 2012 International Conference on Lightning Protection (ICLP).

[75]  C. A. Coelho,et al.  Climatic diagnostics associated with anomalous lightning incidence during the summer 2012/2013 in Southeast Brazil , 2018 .

[76]  F. Ludlam.  Clouds and Storms: The Behavior and Effect of Water in the Atmosphere , 1990 .

[77]  Umran S. Inan,et al.  Highly intense lightning over the oceans: Estimated peak currents from global GLD360 observations , 2013 .

[78]  R. Singh,et al.  On the association of lightning activity and projected change in climate over the Indian sub-continent , 2017 .

[79]  C. Morales,et al.  Electrification of precipitating systems over the Amazon: Physical processes of thunderstorm development , 2011 .

[80]  W. David Rust,et al.  A Comparison of the Optical Pulse Characteristics of Intracloud and Cloud-to-Ground Lightning as Observed above Clouds. , 1988 .

[81]  V. Singh,et al.  An application of sample entropy to precipitation in Paraíba State, Brazil , 2019, Theoretical and Applied Climatology.

[82]  S. Hastenrath.  Exploring the climate problems of Brazil’s Nordeste: a review , 2012, Climatic Change.

[83]  P. Murugavel,et al.  Climatology of lightning over Indian region and its relationship with convective available potential energy , 2014 .

[84]  S. Kandalgaonkar,et al.  Spatio-temporal variability of lightning activity over the Indian region , 2005 .

[85]  W. Briggs.  Statistical Methods in the Atmospheric Sciences , 2007 .

[86]  P. Murugavel,et al.  Trends of Convective Available Potential Energy over the Indian region and its effect on rainfall , 2012 .

[87]  J. Thepaut,et al.  The ERA‐Interim reanalysis: configuration and performance of the data assimilation system , 2011 .

[88]  S. Soula.  Lightning and Precipitation , 2009 .

[89]  H. Christian.  Global Frequency and Distribution of Lightning as Observed From Space , 2001 .

[90]  M. Tippett,et al.  Performance of a simple reanalysis proxy for U.S. cloud‐to‐ground lightning , 2019, International Journal of Climatology.

[91]  K. P. Naccarato,et al.  Lightning detection in Southeastern Brazil from the new Brazilian Total Lightning Network (BrasilDAT) , 2012, 2012 International Conference on Lightning Protection (ICLP).

[92]  E. Ávila,et al.  Climatic Projections of Lightning in Southeastern Brazil Using CMIP5 Models in RCP’s Scenarios 4.5 and 8.5 , 2017 .

[93]  D. Rind,et al.  Possible implications of global climate change on global lightning distributions and frequencies , 1994 .

[94]  O. Pinto,et al.  The effects of Sao Paulo urban heat island on lightning activity: Decadal analysis (1999–2009) , 2016 .

[95]  Chuntao Liu,et al.  Relationships between lightning flash rates and passive microwave brightness temperatures at 85 and 37 GHz over the tropics and subtropics , 2011 .

[96]  W. Cotton,et al.  Storm and Cloud Dynamics , 1992 .

[97]  Richard J. Blakeslee,et al.  Performance Assessment of the Optical Transient Detector and Lightning Imaging Sensor. Part I: Predicted Diurnal Variability , 2002 .

[98]  P. Kirstetter,et al.  Influence of biomass aerosol on precipitation over the Central Amazon: an observational study , 2015 .

[99]  Upul J. Sonnadara,et al.  Climatology of lightning flash activities over Sri Lanka , 2019, Theoretical and Applied Climatology.

[100]  Vincent Dubreuil,et al.  Basin scale rainfall-evapotranspiration dynamics in a tropical semiarid environment during dry and wet years , 2019, Int. J. Appl. Earth Obs. Geoinformation.

[101]  A. Lupo,et al.  Lightning and Rainfall Characteristics in Elevated vs. Surface Based Convection in the Midwest that Produce Heavy Rainfall , 2017 .

[102]  Joao R. Neves,et al.  Sferics Timing And Ranging NETwork - STARNET: 8 years of measurements in South America , 2014 .

[103]  P. Sentelhas,et al.  Assessment of different gridded weather data for soybean yield simulations in Brazil , 2018, Theoretical and Applied Climatology.

[104]  William B. Rossow,et al.  Structural Characteristics and Radiative Properties of Tropical Cloud Clusters , 1993 .

[105]  C. Price,et al.  Using a cloud electrification model to study relationships between lightning activity and cloud microphysical structure , 2013 .

[106]  The influence of urban effect on lightning activity: Evidence of weekly cycle , 2014 .

[107]  R. Houze,et al.  The variable nature of convection in the tropics and subtropics: A legacy of 16 years of the Tropical Rainfall Measuring Mission satellite , 2015, Reviews of geophysics.