Assessing Surface PM2.5 Estimates Using Data Fusion of Active and Passive Remote Sensing Methods

In this paper, we focus on estimations of fine particulate matter by combining MODIS satellite Aerosol Optical Depth (AOD) with Weather Research Forecast (WRF) PBL information using a neural network approach and assess its performance. As part of our analysis, we first explore the baseline effectiveness of AOD and PBL as relevant factors in estimating PM2.5 in passive radiometer and active LIDAR data at CCNY and demonstrate that the PBL height is the most critical additional parameter for accurate PM2.5. Furthermore, active measurements from both ground and satellite based lidar are used to show that summer WRF model PBL heights are most accurate. We then expand our analysis to a regional domain where daily estimations are obtained and compared with operational GEOS-CHEM PM2.5 product. Using our approach, we also create regional daily PM2.5 maps and compare against GEOS-CHEM outputs. Finally, we also consider additional improvements, where multiple satellite observations are used as regressors to predict PM2.5. These results illustrate the significant improvement we obtain within this framework in comparison to a “one size fits all continental scale approach”.

PM2.5 estimation for NY and surrounding states for a particular day.

Published in British Journal of Environment and Climate Change, ISSN: 2231–4784 ,Vol.: 3, Issue.: 4 (October-December)-Special Issue
See full article at: http://www.sciencedomain.org/abstract.php?iid=323&id=10&aid=2530

Survey On The Estimation Of Mutual Information Methods as a Measure of Dependency Versus Correlation Analysis

Link:
http://arxiv.org/abs/1401.3358

In this survey, we present and compare different approaches to estimate Mutual Information (MI) from data to analyze general dependencies between variables of interest in a system. We demonstrate the performance difference of MI versus correlation analysis, which is only optimal in case of linear dependencies. First, we use a piece-wise constant Bayesian methodology using a general Dirichlet prior. In this estimation method, we use a two-stage approach where we approximate the probability distribution first and then calculate the marginal and joint entropies. Here, we demonstrate the performance of this Bayesian approach versus the others for computing the dependency between different variables. We also compare these with linear correlation analysis. Finally, we apply MI and correlation analysis to the identification of the bias in the determination of the aerosol optical depth (AOD) by the satellite based Moderate Resolution Imaging Spectroradiometer (MODIS) and the ground based AErosol RObotic NETwork (AERONET). Here, we observe that the AOD measurements by these two instruments might be different for the same location. The reason of this bias is explored by quantifying the dependencies between the bias and 15 other variables including cloud cover, surface reflectivity and others.

And related:

Towards Identification of Relevant Variables in the observed Aerosol Optical Depth Bias between MODIS and AERONET observations

http://arxiv.org/abs/1302.2969


Estimation and bias correction of aerosol abundance using data-driven machine learning and remote sensing
http://ieeexplore.ieee.org/xpl/login.jsp?tp=&arnumber=6382197&url=http%3A%2F%2Fieeexplore.ieee.org%2Fxpls%2Fabs_all.jsp%3Farnumber%3D6382197
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Statistical Physics of Human Mobility: Paper

Statistical physics help understand relating the microscopic properties of atoms and molecules to the macroscopic properties of materials that can be observed in everyday life. As a result, it is able to explain thermodynamics as a natural result of statistics, classical mechanics, and quantum mechanics at the microscopic level. [1]

By looking into the GPS information, from vehicles (collected) in Italy, Gallotti et al have performed a study to apply ideas of statistical physics to describe the properties of human mobility.

The human mobility is an interesting research question. Understanding of human mobility can be useful in urban planning, and to understand spread of epidemic. In addition, the authors suggest that such studies may also be useful to discover possible "laws" that can be related to the dynamical cognitive features of individuals.

The average speed variance (on the left), the distribution (on the right) can be decomposed as a mixture of Gaussian. Two Gaussians with mean speed of around 20 Km/hr and 45 Km/hr emerges. This indicates the distinct behavior of drivers. I find this to be an interesting decomposition.

The left figure shows the statistical distribution of the activity time. The presence of straight line indicates Benford's law. Figure on the right shows "total activity time". With the help of the "down time" i.e. the period for which the GPS is turned off, the authors suggest that at least three distinct peaks for full-time (~9 hrs), part-time (~4 hrs) jobs and night rest (~13 hrs). However, there is also one more peak around 1hr downtime. I guess the down-time for one hour peak shows short-term activities such as shopping behavior.

In the paper, using the travel time as a cost function, the authors show that the distribution between successive trips are indeed driven by an underlying Benford's law. The ranking of the the distribution of the average visitaion frequency may also help to understand how people organize their daily agenda. An interesting feature comes out when the average speed distribution for the recorded trip is decomposed as a mixture of two Gaussians: one with ≤ 5km. I think such characteristics distribution indicate the local constraint on the movements. Obviously, the motion is not free of constraints. The mobility data is strictly constrained by the road structures.
It would be interesting to see if there are such statistical phenomena as "phase transition" in such statistical law of human mobility.
This is an interesting paper. See [2].


At last, Why do we move from one place to another?
If we assume some aggregate effect on social scale; are we different than the gas molecules contained in a box? Moreover, it seems someone has to drive an extra mile since the system demands it!

References:
(Special thanks to Prof. Armando Bazzani for allowing me to use the figures.)
[1] http://en.wikipedia.org/wiki/Statistical_physics
[2] Towards a Statistical Physics of Human Mobility
Riccardo Gallotti, Armando Bazzani, Sandro Rambaldi
http://arxiv.org/abs/1207.5698

To the fans of Graphene: meet silicene...

Silicene is just one atom thick layer of silicon


  http://www.newscientist.com/article/mg21428625.400-move-over-graphene-silicene-is-the-new-star-material.html

The papers:
Physical Review Letters, DOI: 10.1103/PhysRevLett.108.155501
Appl. Phys. Lett. 97, 223109 (2010); http://dx.doi.org/10.1063/1.3524215
- --
Graphene is one-atom-thick planar sheets of carbon atoms packed in honeycomb-like structures. It has been of great research interest because of its unique physical properties. We already saw that papers with the term "graphine" was increasing drastically since 2006 [Link ].

Silicene is the silicon equivalent of graphene.
Because it can be integrated more easily into silicon chip production lines, newscientist.com speculates that its integration into electronic devices might help produce cheaper electronic devices.

Diffusive Nested Sampling: Brewer et. al.

Brendon et. al. has a newer version of nested sampling algorithm, they call it Diffusive Nested Sampling (DNS). As the name indicates, it principally differs from the "classic" nested sampling in presenting the hard constraint. It relaxes the hard evolving constraint and lets the samples to explore the mixture distribution of nested probability distributions, each successive distribution occupying e^-1 times the enclosed prior mass of the previously seen distributions. The mixture distribution is weighted at will (a hack :P) which is a clever trick of exploration. This reinforces the idea of "no peaks left behind" for multimodal problems.

On a test problem they claim that DNS "can achieve four times the accuracy of classic Nested Sampling, for the same computational effort; equivalent to a factor of 16 speedup".

I have not played with it yet. However, it seems worth trying. Just a note to myself.

PS:
What can grow out of side talks in a conference?
If you know the power of scrapping in the napkin paper, you would not be surprised.

The paper is available in arxiv:
http://arxiv.org/abs/0912.2380
The code is available at: http://lindor.physics.ucsb.edu/DNest/; comes with handy instructions.

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Thanks are due to Dr. Brewer for indicating typos in the draft and suggestions + allowing to use the figures.
 The original nested sampling code is available in the book by sivia and skilling: Data Analysis: A Bayesian Tutorial
 

Edit: Sep 5, 2013 An illustrative animation of Diffusive Nested Sampling (www.github.com/eggplantbren/DNest3) sampling a multimodal posterior distribution. The size of the yellow circle indicates the importance weight. The method can travel between the modes because the target distribution includes the (uniform) prior as a mixture component.