1NASA Postdoctoral Program Fellow, NASA Goddard Institute for Space Studies, New York, USA
2NASA Goddard Institute for Space Studies, New York, USA
3NASA Langley Research Center, Hampton, Virginia, USA
4Bay Area Environmental Research Institute, Sonoma, CA, USA
5NASA Ames Research Center, Moffett Field, California, USA
6University of Hawaii, Honolulu, Hawaii, USA
Abstract. Absorbing aerosols play an important, but uncertain, role in the global climate. Much of this uncertainty is due to a lack of adequate aerosol measurements. The Aerosol Polarimetery Sensor (APS), which is on the NASA Glory satellite scheduled for launch in the spring of 2011, is designed to help resolve this issue by making accurate, multi-spectral, multi-angle polarized observations. Field observations with the Research Scanning Polarimeter (RSP, the APS airborne prototype), however, have established that simultaneous retrievals of aerosol absorption and vertical distribution over bright land surfaces are quite uncertain. We test a merger of RSP and High Spectral Resolution Lidar (HSRL) data with observations of boreal forest fire smoke, collected during the Arctic Research of the Composition of the Troposphere from Aircraft and Satellites (ARCTAS). During ARCTAS, the RSP and HSRL instruments were mounted on the same aircraft, and validation data were provided by instruments on an aircraft flying a coordinated flight pattern. We found that the lidar data did indeed improve aerosol retrievals using an optimal estimation method, although not primarily because of the contraints imposed on the aerosol vertical distribution. The more useful piece of information from the HSRL was the total column aerosol optical depth, which was used to select the initial value (optimization starting point) of the aerosol number concentration. When ground based sun photometer network climatologies of number concentration were used as an initial value, we found that roughly half of the retrievals had unrealistic sizes and imaginary indices, even though the retrieved spectral optical depths agreed within uncertainties to independent observations. The convergence to an unrealistic local minimum by the optimal estimator is related to the relatively low sensitivity to particles smaller than 0.1 µm at large optical thicknesses. Thus, optimization algorithms used for operational APS retrievals of the fine mode size distribution, when the total optical depth is large, will require initial values generated from table look-ups that exclude unrealistic size/complex index mixtures. External constraints from lidar on initial values used in the optimal estimation methods will also be valuable in reducing the likelihood of obtaining spurious retrievals.