Forecasting ultrafine particle concentrations from satellite and in situ observations

P Crippa,S C Pryor,S Castruccio

doi:10.1002/2016jd026021

Abstract

AbstractRecent innovations in remote sensing technologies and retrievals offer the potential for predicting ultrafine particle (UFP) concentrations from space. However, the use of satellite observations to provide predictions of near‐surface UFP concentrations is limited by the high frequency of incomplete predictor values (due to missing observations), the lack of models that account for the temporal dependence of UFP concentrations, and the large uncertainty in satellite retrievals. Herein we present a novel statistical approach designed to address the first two limitations. We estimate UFP concentrations by using lagged estimates of UFP and concurrent satellite‐based observations of aerosol optical properties, ultraviolet solar radiation flux, and trace gas concentrations, wherein an expectation maximization algorithm is used to impute missing values in the satellite observations. The resulting model of UFP (derived by using an autoregressive moving average model with exogenous inputs) explains 51 and 28% of the day‐to‐day variability in concentrations at two sites in eastern North America.

Highlights

Atmospheric ultrafine particles (UFPs, i.e., particles with diameters < 100 nm) significantly impact human health [Penttinen et al, 2001; Utell and Frampton, 2000] and the Earth’s climate by providing a potential source of cloud condensation nuclei [Pierce et al, 2014]
We focus on observations from the Sun-synchronous Terra satellite (local equatorial overpass time ~10:30 local solar time (LST)), which are indicative of conditions prior to the occurrence of highest UFP concentrations
UFP measurements in eastern North America exhibit strong autocorrelation [Crippa and Pryor, 2013; Jeong et al, 2010; Sullivan et al, 2016; Sullivan and Pryor, 2016]; we propose a functional form that allows use of only concurrent satellite observations as predictors and one in which past particle number concentrations are included in the proxy algorithm to allow an investigation of the dependence of particle concentration from its past values

Summary

Introduction

Atmospheric ultrafine particles (UFPs, i.e., particles with diameters < 100 nm) significantly impact human health [Penttinen et al, 2001; Utell and Frampton, 2000] and the Earth’s climate by providing a potential source of cloud condensation nuclei [Pierce et al, 2014]. Kulmala et al [2011a] were the first to propose that boundary layer nucleation mode number concentrations could be predicted based on transfer functions, wherein the predictors were drawn from remotely sensed properties In their analysis the predictors were once-daily satellite-based observations of ultraviolet radiation (UV), sulfur dioxide (SO2), and aerosol optical depth (AOD) and their predictand was 30 min mean particle number concentrations in the diameter range (Dp) of 3–25 nm. They obtained correlations (R) of 0.25 (R2 = 0.06) between nucleation mode number concentrations observed at the Hyytiälä site in Finland and the satellite proxy

Methods

Results

Conclusion