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.
and
David J Lary
University of Texas at Dallas, Richardson, TX, United States.

Abstract
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.

EMEP Poster Presentation

INJECTION OF METEOROLOGICAL FACTORS INTO SATELLITE ESTIMATES OF SURFACE PM2.5
Nabin Malakar
in collaboration with
Lina Cordero, Yonghua Wu, Barry Gross, Fred Moshary, Mike Ku

Abstract

Prior efforts to connect surface PM2.5 to satellite retrieval of aerosol optical depth (AOD) have been mainly made based on statistical approaches connecting AIRNow PM2.5 measurements and satellite AOD for different seasons and geographic regions. However, this approach does not account for complex aerosol behavior including planetary boundary layer (PBL) dynamics. In another approach used operationally within the IDEA (Infusing satellite Data into Environmental air quality Applications) product, the use of a global model (GEOS-CHEM) is used to estimate on a daily basis, the spatial relationship between forecast PM2.5and column path AOD, which can then be used with satellite AOD estimates. However, one difficulty with the GEOS-CHEM approach is the poor spatial resolution symptomatic of global models with a spatial resolution of 2.5 degrees, which fails to particularly resolve issues in the urban/nonurban interface To improve on this, the WRF/CMAQ model is a high-resolution algorithm that accounts for physically based meteorological factors and surface boundary conditions including emission inventories to estimate particulate concentrations and vertical distributions; therefore, it is considered in our work.

Because of the complexity observed in the PM2.5-AOD relationship, our focal point is the application of a neural network for better describing the non-linear conditions surrounding the PM2.5-AOD environment while at the same time investigating other dependences such as additional factors or seasonal changes. Neural networks have proven to perform well in different areas of study, including atmospheric sciences where many complex relationships cannot be sufficiently understood by using statistical approaches. As part of our analysis, we first explore the baseline effectiveness of AOD and PBL as strong factors in estimating PM2.5 in a local experiment using data collected at one site in New York City. Then, we expand our analysis to a regional domain where daily estimations are compared based on site location and season. 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. Further, we show that further improvements are obtained if additional satellite information, including satellite/view geometry and land surface reflection, is included. Finally, comparisons with WRF/CMAQ PM25 are included. 

Presenting Poster

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About 2013 EMEP Conference
Environmental Monitoring, Evaluation and Protection in New York: Linking Science and Policy

Holiday Inn - Wolf Road
205 Wolf Road, Albany, New York
November 6 & 7

This conference brings together policy makers and nationally renowned scientists to share information on environmental research initiatives in New York State.
Production and use of energy impose one of the greatest burdens on our environment of any human activity. The Environmental Monitoring, Evaluation, and Protection (EMEP) Program at NYSERDA provides scientifically credible and objective information on environmental impacts of energy systems to assist the state in developing science-based and cost-effective policies to mitigate impacts. The EMEP program supports policy-relevant research in order to enhance understanding of energy-related environmental issues.

Monty Hall’s #paradox

Consider an urn , with 15 blue balls, and 10 red balls, and an urn , with 10 blue balls, and 15 red balls. We select randomly one urn (with probability 50% for each urn).
We draw a ball, which turns out to be blue, and we put it back in the urn, Now, we draw a (second) ball. What is the probability that this (second) ball is blue?

Solution after the break!

India launching first #Mars mission #Mangalyaan

After the successful mission to Moon (Chandrayaan-I), India is heading to Mars!

Indian Space Research Organisation (ISRO) is launching  Mangalyaan using the Polar Satellite Launch Vehicle (PSLV). The planned launch date is Nov 5th, 2013.   The Mangalyaan will carry five scientific payloads which include: 

1. Methane Sensor For Mars (MSM), 
2. Mars Color Camera (MCC), 
3. Mars Exospheric Neutral Composition Analyzer (MENCA), 
4. TIR Spectrometer (TIS), and 
5. Lyman-Alpha Photometer (LAP). 

It will also use Indian Deep Space Network (IDSN), as well as NASA deep space network for navigation and tracking during its mission. The spacecraft is expected to take nearly 300 days to reach the Martian orbit.

It is quite impressive that ISRO has pulled it up in such a short time. The next fuel-saving Hohmann transfer orbit opens  in 2016. If successful, ISRO would become the fourth space agency to reach Mars, after Roscosmos, NASA, and ESA.

ISRO in Facebook
https://www.facebook.com/pages/ISROs-Mars-Orbiter-Mission/1384015488503058?directed_target_id=0

Read more...

• http://www.nbcnews.com/science/india-plans-launch-first-mars-mission-test-large-rocket-year-6C10714150
• http://www.planetary.org/blogs/emily-lakdawalla/2013/04011229-isros-mars-mission-now.html
• http://iipdigital.usembassy.gov/st/english/texttrans/2013/03/20130322144724.html#axzz2PF1pO4Vc

LEGO Ultimate (Useless) Machine

Ultimate machine or useless machine are designed to operate in such a way that it will switch itself off, whenever turned on.

Here is one version:

Free Plans, Purchase Kits, Machines & More:http://frivolousengineering.com

There are variety of modifications made. This one is amusing:

And... this is an awesome construction LEGO3.

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