Arif Albayrak

Global bias adjustment for MODIS aerosol optical thickness using neural network

Abstract Large uncertainties in calculating radiative forcings from aerosols due to their location, loading, and types pose a great challenge to global climate modeling. Trying to improve retrievals in a statistical manner normally requires detailed knowledge of uncertainty statistics and bias due to possible error sources such as different measurement viewing geometries, instrument calibration, and dynamically changing atmospheric and earth surface conditions. However, a priori estimates of these error sources are not, in general, available. The use of a neural network (NN) approach to compensate for biases and systematic errors of aerosol optical thickness (AOT) from the Moderate Resolution Imaging Spectrometer (MODIS) operational retrieval algorithm is explored. By utilizing the NN as an estimator, we can compensate against unknown sources of errors, nonlinearity in the data sets, and the presence of non-normal distributions. In this study, the highly accurate ground-based Aerosol Robotic Network (AERONET) measurements are used as the ground truth (GT). Our results show that the adjusted AOT with NN has decreased root mean square errors, improved correlations with GT data by 4% to 6%, and increased the number of NN-adjusted data falling within the published expected error envelope by ∼ 10 % .

Advances in

NASA Goddard Earth Sciences Data and Information Services Center (GES DISC) archives and distributes pioneering collections of data on atmospheric greenhouse gases. In September of 2012, the Atmospheric Infrared Sounder (AIRS) marked a decade of tropospheric observations of carbon dioxide (CO2). Most recently, the Atmospheric CO2 Observations from Space (ACOS) project and GES DISC released CO2 retrievals derived from radiances observed by the Japanese Greenhouse gases Observing SATellite (GOSAT) satellite, launched in 2009. In this letter, we present the most recent estimates of decadal mid-tropospheric trends of CO2 from AIRS, as well as the most recent status of the total column-average distribution of CO2 from ACOS. We also demonstrate that significant discrepancies still exist in the global distribution of observed and modeled column amounts of CO2 using the CO2 retrievals from the ACOS project.

\hbox{CO}_{2}

Measurements made by satellite remote sensing, Moderate Resolution Imaging Spectroradiometer (MODIS), and globally distributed Aerosol Robotic Network (AERONET) are compared. Comparison of the two datasets measurements for aerosol optical depth values show that there are biases between the two data products. In this paper, we present a general framework towards identifying relevant set of variables responsible for the observed bias. We present a general framework to identify the possible factors influencing the bias, which might be associated with the measurement conditions such as the solar and sensor zenith angles, the solar and sensor azimuth, scattering angles, and surface reflectivity at the various measured wavelengths, etc. Specifically, we performed analysis for remote sensing Aqua-Land data set, and used machine learning technique, neural network in this case, to perform multivariate regression between the ground-truth and the training data sets. Finally, we used mutual information between the ob...

Observations From AIRS and ACOS

Air quality information is increasingly becoming a public health concern, since some of the aerosol particles pose harmful effects to peoples health. One widely available metric of aerosol abundance is the aerosol optical depth (AOD). The AOD is the integrated light extinction coefficient over a vertical atmospheric column of unit cross section, which represents the extent to which the aerosols in that vertical profile prevent the transmission of light by absorption or scattering. The comparison between the AOD measured from the ground-based Aerosol Robotic Network (AERONET) system and the satellite MODIS instruments at 550 nm shows that there is a bias between the two data products. We performed a comprehensive search exploring possible factors which may be contributing to the inter-instrumental bias between MODIS-Aqua land data set and AERONET. The analysis used several measured variables, including the MODIS AOD, as input in order to train a neural network in regression mode to predict the AERONET AOD values. This not only allowed us to obtain an estimate, but also allowed us to infer the optimal sets of variables that played an important role in the prediction. In addition, we applied machine learning to infer the global abundance of ground level PM2.5 from the AOD data and other ancillary satellite and meteorology products. This research is part of our goal to provide air quality information, which can also be useful for global epidemiology studies.