A5085691224- Atmospheric chemistry and aerosols
- Fire effects on ecosystems
- Atmospheric aerosols and clouds
- Air Quality Monitoring and Forecasting
- Aeolian processes and effects
- Robot Manipulation and Learning
- Wireless Signal Modulation Classification
- Speech and Audio Processing
- Target Tracking and Data Fusion in Sensor Networks
- Robotics and Sensor-Based Localization
- Meteorological Phenomena and Simulations
- Atmospheric and Environmental Gas Dynamics
- Vestibular and auditory disorders
- Plant Water Relations and Carbon Dynamics
- Air Quality and Health Impacts
- Wind and Air Flow Studies
George Mason University
2024-2025
NOAA Air Resources Laboratory
2024-2025
National Oceanic and Atmospheric Administration
2024-2025
University of Maryland, College Park
2025
Cooperative Institute for Climate and Satellites
2024
University Corporation for Atmospheric Research
2021
Albany State University
2020-2021
University at Albany, State University of New York
2020-2021
Lite-On Technology Corporation (Taiwan)
2016
Yuan Ze University
2012
Abstract Fire activities introduce hazardous impacts on the environment and public health by emitting various chemical species into atmosphere. Most operational air quality forecast (AQF) models estimate smoke emissions based latest available satellite fire products, which may not represent real‐time behaviors without considering spread. Hence, a novel machine learning (ML) spread model, Intensity spRead forecAst (FIRA), is developed for AQF model applications. FIRA aims to improve...
Smoke aerosols emitted from wildfires can transport across long distances and affect the local air quality in downwind regions. In New York State (NYS), has significantly improved due to reductions anthropogenic emission over past decades. As intensity frequency of are continuously increasing under changing climate, smoke predicted become dominant source fine particulate matter (PM2.5) concentration NYS future. this study, non-smoke cases during summer seasons 2012–2019 were identified using...
Abstract The representation of vegetative sub‐canopy wind is critical in numerical weather prediction (NWP) models for the determination air‐surface exchange processes heat, momentum, and trace gases. Because relationship between speed fire behaviors, influence canopy on near‐surface prognostic spread used regional NWP models. In practice, at midflame point fires (midflame speed) to determine rate spread. However, speeds from most situ measurements are taken some reference height above...
In New York State (NYS), episodic high fine particulate matter (PM2.5) concentrations associated with aerosols originated from the Midwest, Mid-Atlantic, and Pacific Northwest states have been reported. this study, machine learning techniques, including multiple linear regression (MLR) artificial neural network (ANN), were used to estimate surface PM2.5 mass at air quality monitoring sites in NYS during summers of 2016–2019. Various predictors considered, meteorological, aerosol, geographic...
Smoke aerosols emitted by wildfires can ascend to the free troposphere, travel over long distances and descend affect local air quality (AQ) in downwind areas. This study investigates AQ impact of long-range transported (LRT) smoke from western North America during summer 2017 New York State (NYS) using observations numerical products. Analysis total fine particulate matter (PM2.5) black carbon measurements at Queens Buffalo shows that about 38% 43% polluted events are related LRT aerosols,...
NOAA is developing the next generation air quality  prediction system for United States  and global aerosol predictions within Unified Forecast System (UFS) framework.  A major goal of this effort to better represent forecast impacts extreme events like wildfires dust storms on aerosols globally weather ranging from hourly  seasonal scales. The FENSGHA (English analog Mandarin term wind-blown dust)  emissions scheme developed at Air Resources...
This paper proposed a radar emitter identification method based on cerebellar model articulation controller(CMAC), the controller inputs are emitter's characteristic like radio frequency(RF), pulse repetition interval(PRI) and width(PW) etc. The CMAC has to train be by training data. After training, input unknown characteristic, network may appropriately identify types. simulation results show that can quickly accurately classify