Assessing satellite based PM2.5 estimates against CMAQ model forecasts

Lina Cordero; Nabin Malakar; Yonghua Wu; Barry Gross; Fred Moshary; Mike Ku

doi:10.1117/12.2029320

17 October 2013 Assessing satellite based PM2.5 estimates against CMAQ model forecasts

Lina Cordero, Nabin Malakar, Yonghua Wu, Barry Gross, Fred Moshary, Mike Ku

Proceedings Volume 8890, Remote Sensing of Clouds and the Atmosphere XVIII; and Optics in Atmospheric Propagation and Adaptive Systems XVI; 88900U (2013) https://doi.org/10.1117/12.2029320
Event: SPIE Remote Sensing, 2013, Dresden, Germany

Abstract

In this work, we focus on estimations of fine particulate matter using MODIS AOD as part of a neural network scheme and compare this to both simple linear regressions and GEOS-CHEM products. In making this comparison, it is well known the seasonal and geographical dependences observed in the PM2.5-AOD relationship; thus, to enhance our predictions, we apply WRF PBL information to our neural network method and assess its performance. 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 obtained based on site location and season. In our local test, we find the high efficiency of the neural network estimations when AOD, PBL and seasonality are primarily assessed (R~0.94 in summer). Later, we test our regional network and compare it with the GEOS-CHEM PM2.5 product. From this, we see better estimations from our experiment using urban/non-urban stations and applying different spatial schemes for training the neural network (R_NN~0.80, R_GEOS-CHEM~0.57 in an urban station with a distance radius of 0.1 degree; R_NN~0.74, R_GEOSCHEM~0.69 in a non-urban station with a distance radius of 0.3 degree). Finally, we create regional daily PM2.5 maps and compare them to GEOS-CHEM outputs, evaluating the corresponding estimations using ground readings.

Citation Download Citation

Lina Cordero, Nabin Malakar, Yonghua Wu, Barry Gross, Fred Moshary, and Mike Ku "Assessing satellite based PM2.5 estimates against CMAQ model forecasts", Proc. SPIE 8890, Remote Sensing of Clouds and the Atmosphere XVIII; and Optics in Atmospheric Propagation and Adaptive Systems XVI, 88900U (17 October 2013); https://doi.org/10.1117/12.2029320

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KEYWORDS

Neural networks

MODIS

Satellites

Phase modulation

Picosecond phenomena

Data centers

Aerosols

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