Modeling cloud-to-ground lightning probability in Alaskan tundra through the integration of Weather Research and Forecast (WRF) model and machine learning method
Wildland fires exert substantial impacts on tundra ecosystems of the high northern latitudes (HNL), ranging from biogeochemical impact on climate system to habitat suitability for various species. Cloud-to-ground (CG) lightning is the primary ignition source of wildfires. It is critical to understan...
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ftdoajarticles:oai:doaj.org/article:48439623e83e4bb0996d6c3acbbe3a80 2023-09-05T13:23:48+02:00 Modeling cloud-to-ground lightning probability in Alaskan tundra through the integration of Weather Research and Forecast (WRF) model and machine learning method Jiaying He Tatiana V Loboda 2020-01-01T00:00:00Z https://doi.org/10.1088/1748-9326/abbc3b https://doaj.org/article/48439623e83e4bb0996d6c3acbbe3a80 EN eng IOP Publishing https://doi.org/10.1088/1748-9326/abbc3b https://doaj.org/toc/1748-9326 doi:10.1088/1748-9326/abbc3b 1748-9326 https://doaj.org/article/48439623e83e4bb0996d6c3acbbe3a80 Environmental Research Letters, Vol 15, Iss 11, p 115009 (2020) Alaskan tundra cloud-to-ground lightning empirical-dynamic modeling lightning-ignited wildfire Weather Research and Forecast (WRF) random forest Environmental technology. Sanitary engineering TD1-1066 Environmental sciences GE1-350 Science Q Physics QC1-999 article 2020 ftdoajarticles https://doi.org/10.1088/1748-9326/abbc3b 2023-08-13T00:37:14Z Wildland fires exert substantial impacts on tundra ecosystems of the high northern latitudes (HNL), ranging from biogeochemical impact on climate system to habitat suitability for various species. Cloud-to-ground (CG) lightning is the primary ignition source of wildfires. It is critical to understand mechanisms and factors driving lightning strikes in this cold, treeless environment to support operational modeling and forecasting of fire activity. Existing studies on lightning strikes primarily focus on Alaskan and Canadian boreal forests where land-atmospheric interactions are different and, thus, not likely to represent tundra conditions. In this study, we designed an empirical-dynamical method integrating Weather Research and Forecast (WRF) simulation and machine learning algorithm to model the probability of lightning strikes across Alaskan tundra between 2001 and 2017. We recommended using Thompson 2-moment and Mellor–Yamada–Janjic schemes as microphysics and planetary boundary layer parameterizations for WRF simulations in the tundra. Our modeling and forecasting test results have shown a strong capability to predict CG lightning probability in Alaskan tundra, with the values of area under the receiver operator characteristics curves above 0.9. We found that parcel lifted index and vertical profiles of atmospheric variables, including geopotential height, dew point temperature, relative humidity, and velocity speed, important in predicting lightning occurrence, suggesting the key role of convection in lightning formation in the tundra. Our method can be applied to data-scarce regions and support future studies of fire potential in the HNL. Article in Journal/Newspaper Tundra Directory of Open Access Journals: DOAJ Articles Mellor ENVELOPE(-114.944,-114.944,60.714,60.714) Environmental Research Letters 15 11 115009 |
institution |
Open Polar |
collection |
Directory of Open Access Journals: DOAJ Articles |
op_collection_id |
ftdoajarticles |
language |
English |
topic |
Alaskan tundra cloud-to-ground lightning empirical-dynamic modeling lightning-ignited wildfire Weather Research and Forecast (WRF) random forest Environmental technology. Sanitary engineering TD1-1066 Environmental sciences GE1-350 Science Q Physics QC1-999 |
spellingShingle |
Alaskan tundra cloud-to-ground lightning empirical-dynamic modeling lightning-ignited wildfire Weather Research and Forecast (WRF) random forest Environmental technology. Sanitary engineering TD1-1066 Environmental sciences GE1-350 Science Q Physics QC1-999 Jiaying He Tatiana V Loboda Modeling cloud-to-ground lightning probability in Alaskan tundra through the integration of Weather Research and Forecast (WRF) model and machine learning method |
topic_facet |
Alaskan tundra cloud-to-ground lightning empirical-dynamic modeling lightning-ignited wildfire Weather Research and Forecast (WRF) random forest Environmental technology. Sanitary engineering TD1-1066 Environmental sciences GE1-350 Science Q Physics QC1-999 |
description |
Wildland fires exert substantial impacts on tundra ecosystems of the high northern latitudes (HNL), ranging from biogeochemical impact on climate system to habitat suitability for various species. Cloud-to-ground (CG) lightning is the primary ignition source of wildfires. It is critical to understand mechanisms and factors driving lightning strikes in this cold, treeless environment to support operational modeling and forecasting of fire activity. Existing studies on lightning strikes primarily focus on Alaskan and Canadian boreal forests where land-atmospheric interactions are different and, thus, not likely to represent tundra conditions. In this study, we designed an empirical-dynamical method integrating Weather Research and Forecast (WRF) simulation and machine learning algorithm to model the probability of lightning strikes across Alaskan tundra between 2001 and 2017. We recommended using Thompson 2-moment and Mellor–Yamada–Janjic schemes as microphysics and planetary boundary layer parameterizations for WRF simulations in the tundra. Our modeling and forecasting test results have shown a strong capability to predict CG lightning probability in Alaskan tundra, with the values of area under the receiver operator characteristics curves above 0.9. We found that parcel lifted index and vertical profiles of atmospheric variables, including geopotential height, dew point temperature, relative humidity, and velocity speed, important in predicting lightning occurrence, suggesting the key role of convection in lightning formation in the tundra. Our method can be applied to data-scarce regions and support future studies of fire potential in the HNL. |
format |
Article in Journal/Newspaper |
author |
Jiaying He Tatiana V Loboda |
author_facet |
Jiaying He Tatiana V Loboda |
author_sort |
Jiaying He |
title |
Modeling cloud-to-ground lightning probability in Alaskan tundra through the integration of Weather Research and Forecast (WRF) model and machine learning method |
title_short |
Modeling cloud-to-ground lightning probability in Alaskan tundra through the integration of Weather Research and Forecast (WRF) model and machine learning method |
title_full |
Modeling cloud-to-ground lightning probability in Alaskan tundra through the integration of Weather Research and Forecast (WRF) model and machine learning method |
title_fullStr |
Modeling cloud-to-ground lightning probability in Alaskan tundra through the integration of Weather Research and Forecast (WRF) model and machine learning method |
title_full_unstemmed |
Modeling cloud-to-ground lightning probability in Alaskan tundra through the integration of Weather Research and Forecast (WRF) model and machine learning method |
title_sort |
modeling cloud-to-ground lightning probability in alaskan tundra through the integration of weather research and forecast (wrf) model and machine learning method |
publisher |
IOP Publishing |
publishDate |
2020 |
url |
https://doi.org/10.1088/1748-9326/abbc3b https://doaj.org/article/48439623e83e4bb0996d6c3acbbe3a80 |
long_lat |
ENVELOPE(-114.944,-114.944,60.714,60.714) |
geographic |
Mellor |
geographic_facet |
Mellor |
genre |
Tundra |
genre_facet |
Tundra |
op_source |
Environmental Research Letters, Vol 15, Iss 11, p 115009 (2020) |
op_relation |
https://doi.org/10.1088/1748-9326/abbc3b https://doaj.org/toc/1748-9326 doi:10.1088/1748-9326/abbc3b 1748-9326 https://doaj.org/article/48439623e83e4bb0996d6c3acbbe3a80 |
op_doi |
https://doi.org/10.1088/1748-9326/abbc3b |
container_title |
Environmental Research Letters |
container_volume |
15 |
container_issue |
11 |
container_start_page |
115009 |
_version_ |
1776204371239895040 |