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.

Thanks for the visit. Please feel free to visit my Weblogs.

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Showing posts with label research. Show all posts
Showing posts with label research. Show all posts

Tuesday, December 30, 2014

#AMS2015, January 04 - 08, 2015 Phoenix, AZ #conference @ametsoc

Data fusion of Satellite AOD and WRF meteorology for improved PM25 estimation for northeast USA

Monday, 5 January 2015: 1:45 PM 
at Sixth Conference on Environment and Health)
228AB (Phoenix Convention Center - West and North Buildings)
Nabin Malakar, City College of New York, New York, NY; and L. Cordero, B. Gross, D. Vidal, and F. Moshary
The current approach to ingesting satellite data (IDEA- Infusing satellite Data into Environmental air quality Applications Product) into surface PM2.5 retrievals uses a combination of spatial interpolation and a global geo-chemical model (GEOS-CHEM) to define appropriate mass to AOD factor maps that can be used with satellite AOD retreivals. This information is then statistically blended with current AIRNow measurements creating a refined retrieval product. In this paper, we propose to use the same approach except that we replace the GEOS-CHEM component with an alternative high resolution meteorological model scheme. In particular, we illustrate that the GEOS-CHEM factors can be strongly biased and explore methods that incorporate a combination of satellite AOD retrievals with WRF meteorological forecasts on a regional scale. We find that although PBL height should be a significant factor, the WRF model uncertainties for PBL height in comparison to Calipso make this factor less reliable. More directly we find that the covarying PBL averaged temperature (together with wind direction) are the most important factors. Direct statistical comparisons are made against the IDEA product showing the utility of this approach over regional scales. In addition, we explore the importance of a number of factors including season and time averaging showing that the satellite approach improves significantly as the time averaging window decreases illustrating the potential impact that GOES-R will have on PM25 estimation.

Fusing Spatial Kriging with Satellite Estimates to Obtain a Regional Estimation of PM2.5

Daniel Vidal, City College of New York, New York, NY; and B. Gross, N. Malakar, and L. Cordero
This work focuses on developing estimates of ground-level fine particulate matter (PM2.5) in the northeastern U.S. based on measurements derived from the Air Quality System (AQS) repository. Real time monitoring of PM2.5 is important due to its effect on climate change and human health, however, designated samplers used by state agencies do not provide optimal spatial coverage given their high cost and extensive human labor dependence. Through the application of remote sensing instruments, information about PM2.5 concentrations can be generated at certain locations. On the other hand, coverage limitation also occurs when using satellite remote sensing methods due to atmospheric conditions. Therefore, our approach begins by utilizing surface PM2.5 measurements collected from the Remote Sensing Information Gateway (RSIG) portal in order to build fine particulate matter estimations by applying a Spatial Kriging technique. Then, we combine our Kriging estimations to the satellite derived PM2.5 obtained through an Artificial Neural Network (ANN) scheme to generate a daily regional PM2.5 product. Finally, evaluation of our fused algorithm's technique is assessed by performing comparisons against Kriging and neural network individual performances, showing the promising value added by the combination of these two techniques in producing more accurate estimations of surface level PM2.5 over our region of interest.

This one is related to the award winning work by Daniel:

Analysis of New York City traffic data, land use, emissions and high resolution local meteorology for the prediction of neighborhood scale intra-urban PM2.5 and O3
Monday, 5 January 2015: 4:30 PM 

at Sixth Conference on Environment and Health)
228AB (Phoenix Convention Center - West and North Buildings)
Chowdhary Nazmi, NOAA/CREST/City College, New York, NY; and N. Malakar, L. Cordero, and B. Gross
Air pollution affects the health and well-being of residents of mega cities like New York. Predicting the air pollutant concentration throughout the city can be difficult because the sources and levels of the pollutants can vary from season to season. Local meteorology, traffic and land use also play an important role in these variations and the use of statistical machine learning tools such as Neural Networks can be very useful. In order to develop a Neural Network for the prediction of intra-urban air pollutants (PM2.5, O3), high resolution local data are collected and analyzed. Surface level high resolution temperature, relative humidity and wind speed data are collected from the CCNY METNET network. Annual average daily traffic data from NYMTC model as well as continuous and short count traffic data are collected from NYSDOT. High density data from NYC Community Air Survey model is used to analyze the relationship between background and street level indicators for PM2.5 and O3. All the variables (meteorology, population, traffic, land use etc) are ranked according to the absolute strength of their correlation with the measured pollutants and highest ranking variables are identified to be used for the development of a Neural Network. An analysis of how street level pollution differs from background AIRNow observations will be made showing the importance of high density observations. The potential to use the model in other urban areas will also be explored.

Having now relocated to NASA JPL, it is fun to reflect back to see what was accomplished during my stay at CCNY.

Friday, December 19, 2014

Presented in the AGU 2014, San Francisco, CA

  • GC51D-0460Ingesting Land Surface Temperature differences to improve Downwelling Solar Radiation using Artificial Neural Network: A Case Study
  • In order to study the effects of global climate change on regional scales, we need high resolution models that can be injected into local ecosystem models. Although the injection of regional Meteorological Models such as Weather Research and Forecasting (WRF) can be attempted where the Global Circulation Model (GCM) conditions and the forecasted land surface properties are encoded into future time slices - this approach is extremely computer intensive.
    We present a two-step mechanism in which low resolution meteorological data including both surface and column integrated parameters are combined with high resolution land surface classification parameters to improve on purely interpolative approaches by using machine learning techniques. In particular, we explore the improvement of surface radiation estimates critical for ecosystem modeling by combining both model and satellite based surface radiation together with land surface temperature differences.

    Nabin Malakar - NASA Jet Propulsion Laboratory
    Mark Bailey
    CUNY City College
    Rebecca Latto
    CUNY City College
    Emmanuel Ekwedike
    CUNY City College
    Barry Gross
    CUNY City College
    Jorge Gonzalez
    CUNY City College
    Charles Vorosmarty
    CUNY City College
    Glynn Hulley - NASA Jet Propulsion Laboratory

    A51B-3024Bias Correction of MODIS AOD using DragonNET to obtain improved estimation of PM2.5

MODIS AOD retreivals using the Dark Target algorithm is strongly affected by the underlying surface reflection properties. In particular, the operational algorithms make use of surface parameterizations trained on global datasets and therefore do not account properly for urban surface differences. This parameterization continues to show an underestimation of the surface reflection which results in a general over-biasing in AOD retrievals. Recent results using the Dragon-Network datasets as well as high resolution retrievals in the NYC area illustrate that this is even more significant at the newest C006 3 km retrievals. In the past, we used AERONET observation in the City College to obtain bias-corrected AOD, but the homogeneity assumptions using only one site for the region is clearly an issue. On the other hand, DragonNET observations provide ample opportunities to obtain better tuning the surface corrections while also providing better statistical validation. In this study we present a neural network method to obtain bias correction of the MODIS AOD using multiple factors including surface reflectivity at 2130nm, sun-view geometrical factors and land-class information. These corrected AOD’s are then used together with additional WRF meteorological factors to improve estimates of PM2.5. Efforts to explore the portability to other urban areas will be discussed. In addition, annual surface ratio maps will be developed illustrating that among the land classes, the urban pixels constitute the largest deviations from the operational model.

Tuesday, November 18, 2014

Daniel Vidal (CCNY, CUNY) Wins first prize

One of my undergraduate student, Daniel Vidal from the City College of New York, has come first in the final round of the technical paper competition in the Society of Hispanic Professional Engineers (SHPE) conference in Detroit, Michigan.

Congratulations to Daniel! 


The work was based upon
and our collaboration over the summer. We expanded the prototype to the northeast, and got nice results.

Saturday, July 19, 2014

Presented at IGARSS 2014, Quebec, Canada

This week I attended the joint International Geoscience and Remote Sensing Symposium (IGARSS 2014) / 35th Canadian Symposium on Remote Sensing (35th CSRS).  The symposium theme was “Energy and our Changing Planet”.

ON Friday I presented my work on:
Assessing MODIS C006 urban corrections using the High Resolution Dragon AERONET Network

The slides are embedded below for viewing:

Monday, June 30, 2014

Monitoring in-situ PM2.5 in NYC metro area using #matlab #trendy

Matlab's Trendy feature can be used to monitor and collect hourly air quality station data directly from the source url. The data can be feed into the trendy app using the urlfilter and updatetrend commands.

Here is the basic code that gets the job done for the CCNY location. If you are interested to get multiple data, just append it to the update trend array:
% Get the data from CCNY, update the trendy
url = '';
count = urlfilter(url, 'PM25C'); % reading
PMccny = count; % PM at CCNY
The Trendy then can be used to plot the gathered data. I had to let it gather data for few days before I could plot some nice trends. You can already see the diurnal variation in the data below.
PM2.5 trend in NYC. If the image is not available, follow the link below.
The plots can be made with the following code (the time and data will be different for your code):
% PM2.5 hourly measurements in CCNY
%   time vector is: time2322
%   data vector is: data2322
plot(time2322,data2322, 'o-');
hleg = legend('PM2.5(ug/m3)', 'Location', 'SouthWest');
set(hleg, 'FontSize', 8);
title('Air Quality at CCNY station');
I can also set up an email alert if the PM2.5 reading gets higher than some threshold, say 35ugm/m3. Now you can think about the useful applications of such tools!!

Update: well, it has been deprecated! and replaced with "trendy"

Tuesday, June 17, 2014

PM2.5 and O3 dense ground observation in NYC summer

My research involves use of in-situ data, satellite remote sensing data infused with the meteorological information, and application of machine learning techniques to obtain improved estimates of PM2.5. Broadly, my current project involves climate and air quality research, and I have worked with wide variety of model and remote sensing data.

This post is concerned about the dense urban observation in the summer database collected over the New York City over the years 2009-2012.
This database gives insights into the PM2.5 and O3 concentration in the urban setting. Specifically for New York City where these pollutants can affect about 8 million people. Two figures from summer 2010 are presented below showing the relative concentrations in reference to the background EPA measurements binned over the 15-day measurement period. The densely populated area show increased PM2.5 (ug/m3), while decreased O3 concentrations (ppb).  Some interesting geo-chemistry going on! 

Monday, May 5, 2014

Downscaling Shortwave Radiation for northeast regional ecosystem model (ne-resm)

A brief update on our recent progress in downscaling the atmospheric variables. This work was performed to support the input variables for northeast regional ecosystem modeling group ( 

We applied machine learning technique to downscale the GCM in reference to the Daymet variables (which represents the ground truth). Since the Daymet is only available in current scenario, our scheme will be more useful for providing the high resolution atmospheric variables for the future scenario. Moreover, since we have built the framework, this approach can be extended to continental USA.  We have performed downscaling of Maximum temperature, minimum temperature and downwelling shortwave radiation. The shortwave radiation is the one that requires a lot of improvements... details to come out in a paper soon. This work was presented in Machine Learning Conference in NYC and received quite nice receptions from people who visited the poster. You can see some more pics here:

Thanks are due  to Dr. Peter Thornton at Climate Change Science Institute / Environmental Sciences Division, Oak Ridge National Laboratory. I am grateful for his help in ingesting the daylength variable so that ISIMIP and Daymet could be converted to the same 24hr average, 

Saturday, April 26, 2014

PM2.5 Map by fusing Machine-learning and Kriging estimates

Just a brief update on our progress in making PM2.5 maps for the northeast. First we applied machine learning algorithms to estimate PM2.5 from remote sensing, ground station and meteorology data, then we fused Kriging results of the ground station data to obtain the final PM2.5 map. Inverse distance weighting on remote sensing has been applied to improve the coverage on remote sensing. The results were obtained using NY state data as we were funded by NY state agency. 

Friday, March 28, 2014

Presenting in Machine Learning Conference in NYAS today

Creating High-Resolution Climate Meteorological Forecasts by Application of Machine Learning Techniques

Nabin Malakar, PhD, Emmanuel Ekwedike, BS, Barry Gross, PhD, Jorge Gonzalez, PhD, and Charles Vorosmatry, PhD
The City College of New York, New York, New York, United States;

In order to study the effects of global climate change on a regional scale, the low resolution GCM forecast data needs to be intelligently adapted (downscaled) so that it can be injected into high resolution models such as terrestrial ecosystems. Our study region is the North East domain [{35N, 45N} x {-85W,-65W}]. In particular, we focus on High and Low temperature extremes within the Daymet data set, while the low resolution climatology (at 0.5 deg) MET data are obtained from the The Inter-Sectoral Impact Model Intercomparison Project (ISI-MIP) climatology forecast database.  Although the injection of regional Meteorological Models such as Weather Research and Forecasting (WRF) can be attempted where the GCM conditions and the forecasted land surface properties are encoded into a future time slices, this approach is extremely computer intensive. We present a two-step mechanism by using low resolution meteorological data including both surface and column integrated parameters, and then by combining high resolution land surface classification parameters to improve on purely interpolative approaches by using machine learning techniques. 

Application of Machine-Learning for Estimation of PM2.5 by Data Fusion of Satellite Remote Sensing, Meteorological Factors, and Ground Station Data

Lina Cordero, MS, Nabin Malakar, PhD, Yonghua Wu, PhD,  Barry Gross, PhD,  Fred Moshary, PhD
Optical Remote Sensing Laboratory, CCNY, New York, New York, United States;

Particulate matter with dimension less than 2.5 micrometers (PM2.5) can have adverse health effects. These particles can enter into the blood streams via lungs, reach vital organs and cause serious damages by oxidative inflammations. We present our latest progress in obtaining correct estimates of PM2.5 on regional scale by using machine learning techniques. Specifically, we apply a neural network method for better describing the non-linear conditions surrounding the PM2.5-MODIS AOD while at the same time investigating dependencies on additional factors or seasonal changes.  In our local test, we find very good agreement of the neural network estimator when AOD, PBL, and seasonality are ingested (R~0.94 in summer). Next, we test our regional network and compare it with the GEOS-CHEM product. In particular, we find significant improvement of the NN approach with better correlation and less bias in comparison with GEOS-CHEM. We also show that further improvements are obtained if additional satellite information and land surface reflection, is included. Finally, comparisons with Community Multi-scale Air Quality Model (CMAQ) PM2.5 are also presented.

Using NN techniques to ingest Meteorological Weather Satellite data in support of Defense Satellite Observations

Crae Sosa, BS, Gary Bouton, MS, Sam Lightstone, MS, Nabin Malakar, PhD,  Barry Gross, PhD and Fred Moshary, PhD
The City College of New York, New York, New York, United States;

The need to observe thermal targets from space is crucial to monitoring both natural events and hostile threats. Satellites must choose between high spatial resolution with high sensitivity and multiple spectral channels. Defense satellites ultimately choose high sensitivity with a small number of spectral channels. This limitation makes atmospheric contamination due to water vapor a significant problem which can not be determined from the satellite itself. For this reason, we show how it is possible to ingest meteorological satellite data using NN to allow for the compensation of water absorption and re-emssion in near-real time

Tuesday, March 4, 2014

Updates on Work, bibliography

Here are the latest in the line of my work at CCNY:

Bias Correction of high resolution MODIS Aerosol Optical Depth in urban areas using the Dragon AERONET Network
N Malakar, M Oo, A Atia, B Gross, F Moshary
AGU 2013 Oral Presentation in A31K (SWIRL_DA)
Injection Of Meteorological Factors Into Satellite Estimates Of Surface PM2.5
N Malakar, L Cordero, Y Wu, B Gross, M Ku
2013 EMEP Conference (, Albany, NY
Assessing satellite based PM2. 5 estimates against CMAQ model forecasts
L Cordero, N Malakar, Y Wu, B Gross, F Moshary, M Ku
SPIE Remote Sensing, 88900U-88900U-15, Germany
Ingesting MODIS land surface classification into AOD retrievals
AA Atia, A Picon, N Malakar, B Gross, F Moshary
SPIE Remote Sensing, 888707-888707-11, Germany
L Cordero, N. Malakar, Y Wu, B Gross, F Moshary
2013 CMAS Conference, NC, USA
Nabin Malakar, A. Atia, B. Gross, F. Moshary, S. Ahmed, and D. Lary
AMS 2014, Atlanta, GA, USA

Lina Cordero, N. Malakar, D. Vidal, R. Latto, B. Gross, F. Moshary, and S. Ahmed
AMS 2014, Atlanta, GA, USA

Nabin Malakar,  B. Gross, J. E. Gonzalez, P. Yang, and F. Moshary
AMS 2014, Atlanta, GA, USA

L Cordero, N Malakar,  Y Wu, B Gross, M Ku, British Journal of Environment and Climate Change 3 (4), 547-565, 2013
DOI : 10.9734/BJECC/2013/7668