Abstract. A new multiangle implementation of the atmospheric correction (MAIAC)
algorithm has been applied in the Moderate Resolution Imaging
Spectroradiometer (MODIS) sensor and has recently provided globally high-spatial-resolution aerosol optical depth (AOD) products at 1 km. Moreover,
several improvements have been modified in the classical Dark Target (DT) and
Deep Blue (DB) aerosol retrieval algorithms in MODIS Collection 6.1 products.
Thus, validation and comparison of the MAIAC, DT, and DB algorithms are urgent
in China. In this paper, we present a comprehensive assessment and comparison
of AOD products at a 550 nm wavelength based on three aerosol retrieval
algorithms in the MODIS sensor using ground-truth measurements from AErosol
RObotic NETwork (AERONET) sites over China from 2000 to 2017. In general,
MAIAC products achieved better accuracy than DT and DB products in the
overall validation and accuracy improvement of DB products after the QA
filter, demonstrating the highest values among the three products. In
addition, the DT algorithms had higher aerosol retrievals in cropland,
forest, and ocean land types than the other two products, and the MAIAC
algorithms were more accurate in grassland, built-up, unoccupied, and
mixed land types among the three products. In the geometry dependency
analysis, the solar zenith angle, scattering angle, and relative azimuth
angle, excluding the view zenith angle, significantly affected the
performance of the three aerosol retrieval algorithms. The three products
showed different accuracies with varying regions and seasons. Similar spatial
patterns were found for the three products, but the MAIAC retrievals were
smaller in the North China Plain and higher in Yunnan Province compared with
the DT and DB retrievals before the QA filter. After the QA filter, the DB
retrievals were significantly lower than the MAIAC retrievals in south China.
Moreover, the spatiotemporal completeness of the MAIAC product was also
better than the DT and DB products.
The objective of this paper is to compare the performance of the ensemble Kalman filter (EnKF) to the performance of a gradientbased minimization method for the problem of estimation of facies boundaries in history matching. The EnKF is a Monte Carlo method for data assimilation that uses an ensemble of reservoir models to represent and update the covariance of variables. In several published studies, it outperforms traditional historymatching algorithms in adaptability and efficiency.Because of the approximate nature of the EnKF, the realizations from one ensemble tend to underestimate uncertainty, especially for problems that are highly nonlinear. In this paper, the distributions of reservoir-model realizations from 20 independent ensembles are compared with the distributions from 20 randomized-maximum-likelihood (RML) realizations for a 2D waterflood model with one injector and four producers. RML is a gradientbased sampling method that generates one reservoir realization in each minimization of the objective function. It is an approximate sampling method, but its sampling properties are similar to the Markov-chain Monte Carlo (McMC) method on highly nonlinear problems and are relatively more efficient than McMC.Despite the nonlinear relationship between the data (such as production rates and facies observations) and the model variables, the EnKF was effective at history matching the production data. We find that the computational effort to generate 20 independent realizations was similar for the two methods, although the complexity of the code is substantially less for the EnKF.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.