Abstract. The use of satellite aerosol optical thickness (AOT) from imaging
spectrometers has been successful in quantifying and mapping high-PM2.5
(particulate matter with a mass <2.5 µm diameter) episodes for
pollution abatement and health studies. However, some regions have high
PM2.5 but poor estimation success. The challenges in using AOT
from imaging spectrometers to characterize PM2.5 worldwide
was especially evident in the wintertime San Joaquin Valley (SJV). The SJV's
attendant difficulties of high-albedo surfaces and very shallow, variable
vertical mixing also occur in other significantly polluted regions around
the world. We report on more accurate PM2.5 maps (where cloudiness permits)
for the whole winter period in the SJV (19 November 2012–18 February 2013).
Intensive measurements by including NASA aircraft were made for several
weeks in that winter, the DISCOVER-AQ (Deriving
Information on Surface Conditions from COlumn and VERtically Resolved
Observations Relevant to Air Quality) California mission. We found success with a relatively simple method based on calibration and
checking with surface monitors and a characterization of vertical mixing,
and incorporating specific understanding of the region's climatology. We
estimate PM2.5 to within ∼7 µg m−3
root mean square error (RMSE) and with R values of ∼0.9, based
on remotely sensed multi-angle implementation of atmospheric
correction (MAIAC) observations, and certain further work will improve that
accuracy. Mapping is at 1 km resolution. This allows a time sequence of
mapped aerosols at 1 km for cloud-free days. We describe our technique as a
“static estimation.” Estimation procedures like this one, not dependent on
well-mapped source strengths or on transport error, should help full
source-driven simulations by deconstructing processes. They also provide a
rapid method to create a long-term climatology. Essential features of the technique are (a) daily calibration of the AOT to
PM2.5 using available surface monitors, and (b) characterization of
mixed layer dilution using column water vapor (CWV, otherwise “precipitable
water”). We noted that on multi-day timescales both water vapor and
particles share near-surface sources and both fall to very low values with
altitude; indeed, both are largely removed by precipitation. The existence
of layers of H2O or aerosol not within the mixed layer adds complexity,
but mixed-effects statistical regression captures essential proportionality
of PM2.5 and the ratio variable (AOT ∕ CWV). Accuracy is much higher than
previous statistical models and can be extended to the whole Aqua satellite
data record. The maps and time series we show suggest a repeated pattern for
large valleys like the SJV – progressive stabilization of the mixing
height after frontal passages: PM2.5 is somewhat more determined by
day-by-day changes in mixing than it is by the progressive accumulation of
pollutants (revealed as increasing AOT).
Satellite mapping of PM<sub>2.5</sub> episodes in the wintertime San Joaquin Valley: a “static” model using column water vapor
