#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 

(Session: Air pollution and air quality impacts on health-Part II

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.

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

Sunday, 4 January 2015 (Student Conference Poster Session, 14th Annual Student Conference)

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:

http://www.nabinkm.com/2014/11/daniel-vidal-ccny-cuny-wins-first-prize.html

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 

(Session: Air pollution and air quality impacts on health-Part III 

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.

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.
  Authors
  
  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.

Authors

• Barry Gross
  • CUNY City College
• Nabin Malakar
  • CUNY City College
• Adam Atia
  • CUNY City College
• Fred Moshary
  • CUNY City College
• Samir Ahmed
  • CUNY City College
• Min Oo
  • University of Wisconsin - Madison

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! 

Cheers!

CREATING A REGIONAL PM2.5 MAP BY FUSING SATELLITE AND KRIGING ESTIMATES from Nabin Malakar

The work was based upon
http://www.nabinkm.com/2014/04/pm25-map-by-fusing-machine-learning-and.html
and our collaboration over the summer. We expanded the prototype to the northeast, and got nice results.

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

http://igarss2014.org/Papers/viewpapers.asp?papernum=3464

The slides are embedded below for viewing:

An Interview with Dr. Churamani Gaire

Dr. Churamani Gaire, from Syangja, Nepal,  shares fond memories of the school days, teachers and mentors. He also shares his experience in joining the semiconductor industry as he is currently working as Principal Process Engineer at GLOBALFOUNDRIES.  It is our pleasure to have him in my frame of reference.

Could you please tell us a little bit about yourself.

I was born and raised in an economically subaltern farming family in Kuwakot-8, Syangja. I went to local school (Now Bhanu Higher Secondary School, Chaughera, Syangja), where I learned Nepali and English alphabets from my teachers Durga Pangeni and Hari Bdr Ale, respectively. I completed high school from Keware (Now Bal Siddha Higher Secondary School), Syangja. I then continued my undergraduates at Prithvi Narayan College, Pokhara and Masters in Physics at Tribhuvan University, Kirtipur. Having to answer this question makes me a bit nostalgic of all the fond memories.
My research interest is in the nanotechnology. My professional profile is in LinkedIn: https://www.linkedin.com/pub/churamani-gaire/1a/45a/2a1
My research activities are disseminated in various journals and conference proceedings and are available for public consumption: http://scholar.google.com/citations?user=fY905A4AAAAJ&hl=en&oi=ao.

How did you decide to study physics? Did anyone, in particular, influence you?

I was shy as a child and did not know what I was up to. Growing up, I used to carve tops and toy cars out of wood and play. By high school time, it was clear to me that I was more into mathematics and physical sciences than other subjects. I was privileged to have great teachers like Bhoj Raj Gurung and Babu Ram Sharma in High School, Namo Narayan Yadav, Pabitra Mani Poudel and Binay Kumar Jha in Undergraduates; Devendra Raj Mishra and Shekhar Gurung in my Masters. I have the highest regard to Babu Ram Sharma, Binay Kumar Jha and Shekhar Gurung for their support I got during my high school and college times. While I recall these great names today, it is Guru-Purnima, and I salute these great teachers and mentors on this occasion.

What strategies did you use to be successful in college? Please give out some tips on how to become a successful student in Nepal?

My strategy was to attend all the classes, go through the subject material more than once, note down the areas of weakness and focus on those items in the next iteration of study. In my opinion, one has to develop his/her own style. There is no universally applicable strategy per se. However, my biggest tip to the Nepalese students would be to not waste their time by going to political pep-rallies and become puppets of political parties.

Could you also describe your academic and research journey in USA? What are the challenges for Nepalese students?

I started my graduate study from University at Albany in theoretical physics. I later transferred to Rensselaer Polytechnic Institute (RPI), due to a better match to my academic interest. I conducted my graduate research in nanoscale growth and characterization of semiconducting materials. After my graduation, I continued my research at RPI and developed strategy to grow near single crystal semiconductor material starting from amorphous substrate suitable for low-cost energy application. 

As regard to the challenges, it's the initial first few months when one is trying to adjust with so many new things: new place, new education system and some language barrier. Back in the country, our focus was more on theoretical than experimental physics. So, if you want to pursue theoretical physics in USA, I think you can immediately choose a research group and start contributing towards your graduate dissertation. However, if you want to pursue experimental physics, it takes one-to-two semesters before you can contribute towards your graduate dissertation. Again, it varies from person to person. I believe that Nepalese graduates are capable of competing with international graduates.

Could you please suggest the practical applications of your research outcomes? Do you have a favorite research paper (written by yourself or somebody else)?

My research was about nano-heteroepitaxial growth of near single crystal semiconductor material starting from amorphous substrate. This is applicable in substrate fabrication for low-costing solar cells. I use epitaxial growth method in my current job to create individual transistor units used in computer chips. My publications done during my graduate school are in the public domain and are available for your viewing pleasure as I mentioned earlier. Instead of me saying which of my articles I like, I will leave that option to the public to judge. However, I shall say some of my papers are cited more than the others by the researchers in the field.

How is your experience in joining the Industry? Was there any culture shock for an academician? Or shall a PhD holder expect any difference?

Definitely there is a bit of culture shock. I find rather interesting differences in academia and high-tech industry. Industry has more stringent requirements for deadlines. Your decisions can make immediate financial impact on the order of 10s of millions of dollars. To this end, I would say both the risk and reward are much higher in the high tech industry. And whether you have a PhD or not does not make much difference in the industry, your abilities are judged through whether you can solve problems efficiently and precisely and deliver the solution or not. Nevertheless, the rigorous training process that you have gone through during your PhD definitely prepares you for the rigorousness required in the industry.

A general perception is that industry experience is very demanding. Could you please give us a snapshot of your one day in office?

Yes, you are correct that the industry is very demanding due to short shelf-life of technology. One has to constantly update the new things that are in the market, and stay up to date with the technology challenges and solutions.
I work in semiconductor foundry and own a critical process responsible for transistor performance. On my typical day, I have to fulfill three kinds of major responsibilities and generally attend/prepare for 5-6 meetings a day to:

• Ensure that there are no problems in my process step from both process and hardware aspects. If there are problems, resolve immediately.

• Ensure that internal and external customer demands are met. Internal customers include integration and device team who are constantly looking for new processes and recipes to improve the overall flow. The external customers are real customers who I have to engage time to time and ensure they are comfortable working with us.

• Ensure that we offer improved process to our internal and external customers. We constantly conduct experiments to improve our process. We analyze/interpret data and feed-forward the learning to next cycle, a process we call “continuous improvement process”. 

What have you found to be the toughest aspect of being a physicist, if any?

The job hunting was the toughest part as a physicist.

Which of the skills are strongly recommended for the job hunters in this field?

In my opinion, strong communication skill, data analysis/interpretation and decision making ability are some of the key items recruiters are looking in new hires.

Sir, since I am not a professional interviewer, would you like to add anything else? Thank you for your time!

I am afraid your questions are more professional than my answers. I commend you for your effort. And I wish you for your continuous success as blogger in the future. Thanks very much.

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