Online Program Home
My Program

Abstract Details

Activity Number: 403 - SPAAC Poster Competition
Type: Topic Contributed
Date/Time: Tuesday, July 30, 2019 : 2:00 PM to 3:50 PM
Sponsor: Section on Statistics and the Environment
Abstract #304154
Title: Statistical Downscaling with Spatial Misalignment: Application to Wildland Fire PM2.5 Emissions Forecasting
Author(s): Suman Majumder* and Yawen Guan and Brian Reich and Ana Rappold
Companies: North Carolina State University and North Carolina State University and North Carolina State University and US Environmental Protection Agency
Keywords: Image Registraion; Smoothing; Downscaling; Spatial Misalignment; Model Calibration; Air Quality

PM2.5 is known to have adverse health effects on humans and wildfires send out tons of PM2.5 into the air affecting the nearby areas. Forecasters use numerical models to predict PM2.5 concentrations in different areas for the next 24 hours to warn the public of impending health risk. Statistical methods are needed to calibrate numerical model forecast using monitor data and statistical challenges such as spatial misalignment and model bias often come up. Typical model calibration techniques do not allow correction of errors due to spatial misalignment. We propose a spatial downscaling methodology that, using image registration, identifies the spatial misalignments and accounts for and corrects the bias produced by such warping. Our model is fitted in the Bayesian framework to provide uncertainty of the warping function and the forecasts. A short term forecast of PM2.5 concentration from a deterministic model and spatially sparse monitor data is used. We apply this method to both simulated and real datasets to show its utility and applicability as a real-time forecast method. This is a joint work with Yawen Guan and Brian J. Reich of NC State University and Ana G. Rappold of US EPA.

Authors who are presenting talks have a * after their name.

Back to the full JSM 2019 program