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. The use of satellite aerosol optical thickness (AOT) from imagingspectrometers has been successful in quantifying and mapping high- PM2.5 (particulate matter with a mass µ m diameter) episodes forpollution abatement and health studies. However, some regions have high PM2.5 but poor estimation success. The challenges in using AOTfrom imaging spectrometers to characterize PM2.5 worldwidewas especially evident in the wintertime San Joaquin Valley (SJV). The SJV'sattendant difficulties of high-albedo surfaces and very shallow, variablevertical mixing also occur in other significantly polluted regions aroundthe 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 severalweeks in that winter, the DISCOVER-AQ (DerivingInformation on Surface Conditions from COlumn and VERtically ResolvedObservations Relevant to Air Quality) California mission. We found success with a relatively simple method based on calibration andchecking with surface monitors and a characterization of vertical mixing,and incorporating specific understanding of the region's climatology. Weestimate PM2.5 to within ∼7 µ g m −3 root mean square error (RMSE) and with R values of ∼0.9 , basedon remotely sensed multi-angle implementation of atmosphericcorrection (MAIAC) observations, and certain further work will improve thataccuracy. Mapping is at 1 km resolution. This allows a time sequence ofmapped aerosols at 1 km for cloud-free days. We describe our technique as a“static estimation.” Estimation procedures like this one, not dependent onwell-mapped source strengths or on transport error, should help fullsource-driven simulations by deconstructing processes. They also provide arapid 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 ofmixed layer dilution using column water vapor (CWV, otherwise “precipitablewater”). We noted that on multi-day timescales both water vapor andparticles share near-surface sources and both fall to very low values withaltitude; indeed, both are largely removed by precipitation. The existenceof layers of H2O or aerosol not within the mixed layer adds complexity,but mixed-effects statistical regression captures essential proportionalityof PM2.5 and the ratio variable (AOT  ∕  CWV). Accuracy is much higher thanprevious statistical models and can be extended to the whole Aqua satellitedata record. The maps and time series we show suggest a repeated pattern forlarge valleys like the SJV – progressive stabilization of the mixingheight after frontal passages: PM2.5 is somewhat more determined byday-by-day changes in mixing than it is by the progressive accumulation ofpollutants (revealed as increasing AOT).

atmospheric chemistry and physics

Satellite mapping of PM&amp;lt;sub&amp;gt;2.5&amp;lt;/sub&amp;gt; episodes in the wintertime San Joaquin Valley: a “static” model using column water vapor