Creating
High-Resolution Climate Meteorological Forecasts by Application of Machine
Learning Techniques
Nabin Malakar, PhD, Emmanuel Ekwedike, BS, Barry Gross, PhD, Jorge Gonzalez, PhD, and Charles Vorosmatry, PhD
The
City College of New York, New York, New York, United States;
In order to study the effects of global climate
change on a regional scale, the low resolution GCM forecast data needs to be intelligently
adapted (downscaled) so that it can be injected into high resolution models
such as terrestrial ecosystems. Our study region is the North East domain
[{35N, 45N} x {-85W,-65W}]. In particular, we focus on High and Low temperature
extremes within the Daymet data set, while the low resolution climatology (at
0.5 deg) MET data are obtained from the The Inter-Sectoral Impact Model
Intercomparison Project (ISI-MIP) climatology forecast database. Although the injection of regional
Meteorological Models such as Weather Research and Forecasting (WRF) can be
attempted where the GCM conditions and the forecasted land surface properties
are encoded into a future time slices, this approach is extremely computer
intensive. We present a two-step mechanism by using low resolution
meteorological data including both surface and column integrated parameters, and
then by combining high resolution land surface classification parameters to
improve on purely interpolative approaches by using machine learning techniques.
Application
of Machine-Learning for Estimation of PM2.5 by Data Fusion of Satellite Remote
Sensing, Meteorological Factors, and Ground Station Data
Lina Cordero, MS, Nabin Malakar, PhD,
Yonghua Wu, PhD, Barry Gross, PhD, Fred Moshary, PhD
Optical
Remote Sensing Laboratory, CCNY, New York, New York, United States;
Particulate matter with dimension less than 2.5
micrometers (PM2.5) can have adverse health effects. These particles can enter
into the blood streams via lungs, reach vital organs and cause serious damages
by oxidative inflammations. We present our latest progress in obtaining correct
estimates of PM2.5 on regional scale by using machine learning techniques.
Specifically, we apply a neural network method for better describing the
non-linear conditions surrounding the PM2.5-MODIS AOD while at the same time
investigating dependencies on additional factors or seasonal changes. 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. We
also show that further improvements are obtained if additional satellite
information and land surface reflection, is included. Finally, comparisons with
Community Multi-scale Air Quality Model (CMAQ)
PM2.5 are also presented.
Using NN
techniques to ingest Meteorological Weather Satellite data in support of
Defense Satellite Observations
Crae Sosa,
BS, Gary Bouton, MS, Sam Lightstone, MS, Nabin Malakar, PhD, Barry Gross, PhD and Fred Moshary, PhD
The
City College of New York, New York, New York, United States;
The need to observe thermal targets from space is
crucial to monitoring both natural events and hostile threats. Satellites must
choose between high spatial resolution with high sensitivity and multiple
spectral channels. Defense satellites ultimately choose high sensitivity with a
small number of spectral channels. This limitation makes atmospheric
contamination due to water vapor a significant problem which can not be
determined from the satellite itself. For this reason, we show how it is
possible to ingest meteorological satellite data using NN to allow for the
compensation of water absorption and re-emssion in near-real time
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