Nabin K. Malakar, Ph.D.

I am a computational physicist working on societal applications of machine-learning techniques.

Research Links

My research interests span multi-disciplinary fields involving Societal applications of Machine Learning, Decision-theoretic approach to automated Experimental Design, Bayesian statistical data analysis and signal processing.


Interested about the picture? Autonomous experimental design allows us to answer the question of where to take the measurements. More about it is here...


I addition to the research, I also like to hike, bike, read and play with water color.

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Tuesday, December 10, 2013

Heading to San Francisco For #AGU13

Bias Correction of high resolution MODIS Aerosol Optical Depth in urban areas using the Dragon AERONET Network
Nabin K Malakar, Adam Atia, Barry Gross, Fred Moshary, Samir A Ahmed,
The City College of New York, New York, NY, United States.
David J Lary
University of Texas at Dallas, Richardson, TX, United States.

Aerosol optical depth (AOD) is widely used parameter used to quantify aerosol abundance. Satellite retrievals of aerosols over land is fundamentally more complex than aerosol retrieval over oceans. Due to wide coverage and the extensive validation the Moderate Resolution Imaging Spectroradiometer (MODIS), on board the Terra and Aqua satellites are the workhorse instrument used to retrieve AOD from space. However, satellite algorithms of AOD are extremely complex and depends strongly on sun/view geometry, spectral surface albedo, aerosol model assumptions and surface heterogeneity. This issue becomes even more severe when considering the new MODIS 3 km aerosol retrieval products within version 6. To assess satellite retrievals of these high resolution 3 km products, we use the summer 2011 Dragon AERONET data to assess accuracy as well as major retrieval bias that can occur in MODIS measurements.

In this study, we explore in detail the factors that can drive these biases statistically. As discussed above, our considers multiple conditions such as surface reflectivity at various wavelengths, solar and sensor zenith angles, the solar and sensor azimuth, scattering angles as well as meteorological factors and aerosol type (angstrom coefficient) etc which are used inputs are used to train neural network in regression mode to compensate for biases against the Dragon AERONET AOD values.

In particular, we confirm the results of previous studies where the land cover (urban fraction) appears to be a strong factor in AOD bias and develop a NN estimator which includes land cover directly. The algorithm will be tested not only in the Baltimore/Washington area but assessed in the general North East US where urban biases in the NYC area have been previously identified.