Estimation Of Aerosol Properties Research Articles

Synergy of Satellite‐ and Ground‐Based Aerosol Optical Depth Measurements Using an Ensemble Kalman Filter Approach

AbstractSatellite‐ and ground‐based remote sensing are two widely used techniques to measure aerosol properties. However, neither is perfect in that satellite retrievals suffer from various sources of uncertainties, and ground observations have limited spatial coverage. In this study, focusing on improving estimates of aerosol information on large scale, we develop a data synergy technique based on the ensemble Kalman filter (EnKF) to effectively combine these two types of measurements and yield a monthly mean aerosol optical depth (AOD) product with global coverage and improved accuracy. We first construct a 474‐member ensemble using 11 monthly mean AOD data sets to represent the variability of the AOD field. Then Moderate Resolution Imaging Spectroradiometer AOD retrievals are selected as the background field into which ground‐based measurements from 135 Aerosol Robotic Network sites are assimilated using the EnKF. Compared with satellite data, the bias and root‐mean‐square errors of the combined field are greatly reduced, and correlation coefficients are greatly improved. Moreover, cross validation shows that at locations where surface observations were not assimilated, the reduction in root‐mean‐square error and bias and the increase in correlation can still reach ~20%. Locations where the spatial representativeness of AOD is large or the site density is high are where the greatest changes are typically found. This study shows that the EnKF technique effectively extends the information obtained at surface sites to a larger area, paving the way for combining information from different types of measurements to yield better estimates of aerosol properties as well as their space‐time variability.

A weighted least squares approach to retrieve aerosol layer height over bright surfaces applied to GOME-2 measurements of the oxygen A band for forest fire cases over Europe

Abstract. This paper presents a weighted least squares approach to retrieve aerosol layer height from top-of-atmosphere reflectance measurements in the oxygen A band (758–770 nm) over bright surfaces. A property of the measurement error covariance matrix is discussed, due to which photons travelling from the surface are given a higher preference over photons that scatter back from the aerosol layer. This is a potential source of biases in the estimation of aerosol properties over land, which can be mitigated by revisiting the design of the measurement error covariance matrix. The alternative proposed in this paper, which we call the dynamic scaling method, introduces a scene-dependent and wavelength-dependent modification in the measurement signal-to-noise ratio in order to influence this matrix. This method is generally applicable to other retrieval algorithms using weighted least squares. To test this method, synthetic experiments are done in addition to application to GOME-2A and GOME-2B measurements of the oxygen A band over the August 2010 Russian wildfires and the October 2017 Portugal wildfire plume over western Europe.

Estimation of aerosol properties over the Chinese desert region with MODIS AOD assimilation in a global model

A Local Ensemble Transform Kalman Filter assimilation system has been implemented into an aerosol-coupled global nonhydrostatic model to simulate the aerosol mass concentration and aerosol optical properties of 3 desert sites (Ansai, Fukang, Shapotou) in northwestern China. One-month experiment results of April 2006 reveal that the data assimilation can correct the much overestimated aerosol surface mass concentration, and has a strong positive effect on the aerosol optical depth (AOD) simulation, improving agreement with observations. Improvement is limited with the Ångström Exponent (AE) simulation, except for much improved correlation coefficient and model skill scores over the Ansai site. Better agreement of the AOD spatial distribution with the independent observations of Terra (Deep Blue) and Multi-angle Imaging Spectroradiometer (MISR) AODs is obtained by assimilating the Moderate Resolution Imaging Spectroradiometer (MODIS) AOD product, especially for regions with AODs lower than 0.30. This study confirms the usefulness of the remote sensing observations for the improvement of global aerosol modeling.

Retrieval of aerosol properties from moments of the particle size distribution for kernels involving the step function: cloud droplet activation

Aerosol properties such as the number of particles that activate to form cloud drops and the mass contained within specified size ranges (as in the PM 2.5 and PM 10 regulatory standards) require integration over only part of the full size range of the particle distribution function (PDF) and may be formally expressed as integrals over kernels involving the Heaviside step function. Determination of these properties requires essentially that the size spectrum be partitioned into two (or more) portions, and poses a special challenge for aerosol modeling with the method of moments. To assess the ability of moment-based methods to treat kernels involving step functions, several algorithms for the estimation of aerosol properties associated with cloud activation have been evaluated. For 240 measured continental distributions employed here as test cases, the full size spectrum of the PDF was partitioned into three distinct portions based upon characteristic critical radii for activation in cumulus and stratiform clouds, and mass- and number-concentration metrics were evaluated for each portion. The first six radial moments yielded results accurate to within about 10% or better, on average, and the numbers of particles activated as cloud drops and the aerosol mass taken into cloud water were estimated to an accuracy of 5% or better. Of the moment-based approaches evaluated, the multiple isomomental distribution aerosol surrogate (MIDAS) (Wright, J. Aerosol Sci. 31 (2000) 1) technique performed best. Accurate results were also obtained with the randomized minimization search technique (RMST) (Yue et al., Geophys. Res. Lett. 24 (1997) 651; Heintzenberg et al., Appl. Opt. 20 (1981) 1308).