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
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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
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.
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| Presenting Poster |
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.
, 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).