자료유형  

 학위논문

Control Number  

0017163971

International Standard Book Number  

9798384069034

Dewey Decimal Classification Number  

551.5

Main Entry-Personal Name  

Thapa, Laura Hughes.

Publication, Distribution, etc. (Imprint  

[S.l.] : University of California, Los Angeles., 2024

Publication, Distribution, etc. (Imprint  

Ann Arbor : ProQuest Dissertations & Theses, 2024

Physical Description  

267 p.

General Note  

Source: Dissertations Abstracts International, Volume: 86-03, Section: B.

General Note  

Advisor: Saide, Pablo E.

Dissertation Note  

Thesis (Ph.D.)--University of California, Los Angeles, 2024.

Summary, Etc.  

요약Wildfires are increasing in size and frequency in the Western US due to a complex interplay between climate change and landscape-scale fire exclusion practices. The smoke from these fires is degrading air quality across much of the Continental US. Chemical transport models are vital for warning the public about smoky periods, but uncertainties related to fresh smoke plumes can propagate through these models and cause errors in the resulting air quality forecasts.We address model uncertainty related to smoke plume vertical extent and total emissions. First, we use aircraft observations obtained during the 2019 Western US wildfires (FIREX-AQ) to evaluate and constrain a commonly used smoke plume rise parameterization in two smoke models (WRF-Chem and HRRR-Smoke). Observations show that free tropospheric smoke layers occur in 35% of observed plumes and up to 95% of modeled plumes. False free tropospheric smoke injections were primarily associated with models overestimating fire heat flux by up to a factor of 25. Next, we present data-driven methods for predicting day-to-day changes in smoke emissions. Our top-performing model (random forest) explains 48% of the variance in observed daily emissions and outperforms the current operational assumption that emissions will remain constant over a forecast period (persistence, R2=0.02). This model primarily relies on fire weather data to inform its predictions. Finally, we show preliminary results from WRF-Chem simulations which include random forest-derived emissions and updated heat flux values. We find that in the vicinity of large wildfires in 2020 under less severe fire weather, the random forest-derived emissions can produce better predictions of aerosol optical depth (AOD) and fine particulate matter (PM2.5) than the persistence fire emissions. However, in most cases, persistence and random forest-derived emissions yield very similar AOD and PM2.5 predictions, and that the random forest-derived emissions can both improve and degrade AOD and PM2.5 forecasts. Overall, this work demonstrates the utility of incorporating fire observations to quantify and address uncertainties in our state-of-the-art air quality modeling systems.

Subject Added Entry-Topical Term  

Atmospheric chemistry.

Subject Added Entry-Topical Term  

Geophysics.

Subject Added Entry-Topical Term  

Atmospheric sciences.

Index Term-Uncontrolled  

Aerosol optical depth

Index Term-Uncontrolled  

Wildfires

Index Term-Uncontrolled  

Air quality

Index Term-Uncontrolled  

Smoke plumes

Added Entry-Corporate Name  

University of California, Los Angeles Atmospheric & Oceanic Sciences 002E

Host Item Entry  

Dissertations Abstracts International. 86-03B.

Electronic Location and Access  

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Control Number  

joongbu:658129