The GEOS-5 Neural Network Retrieval (NNR) for AOD

One of the difficulties in data assimilation is the need for multi-sensor data merging that can account for temporal and spatial biases between satellite sensors. In the Goddard Earth Observing System Model Version 5 (GEOS-5) aerosol data assimilation system, a neural network retrieval (NNR) is used...

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Bibliographic Details
Main Authors: Da Silva, Arlindo, Castellanos, Patricia
Format: Other/Unknown Material
Language:unknown
Published: 2017
Subjects:
Earth Resources and Remote Sensing
Meteorology and Climatology
Aerosol Robotic Network
Online Access:http://hdl.handle.net/2060/20170012197
id ftnasantrs:oai:casi.ntrs.nasa.gov:20170012197
record_format openpolar
spelling ftnasantrs:oai:casi.ntrs.nasa.gov:20170012197 2023-05-15T13:06:04+02:00 The GEOS-5 Neural Network Retrieval (NNR) for AOD Da Silva, Arlindo Castellanos, Patricia Unclassified, Unlimited, Publicly available December 13, 2017 application/pdf http://hdl.handle.net/2060/20170012197 unknown Document ID: 20170012197 http://hdl.handle.net/2060/20170012197 Copyright, Public use permitted CASI Earth Resources and Remote Sensing Meteorology and Climatology GSFC-E-DAA-TN50487 AGU Fall Meeting: H34F: Machine Learning Applications in Earth Science and Remote Sensing II; 11-15 Dec. 2017; New Orleans, LA; United States 2017 ftnasantrs 2019-07-20T23:22:28Z One of the difficulties in data assimilation is the need for multi-sensor data merging that can account for temporal and spatial biases between satellite sensors. In the Goddard Earth Observing System Model Version 5 (GEOS-5) aerosol data assimilation system, a neural network retrieval (NNR) is used as a mapping between satellite observed top of the atmosphere (TOA) reflectance and AOD, which is the target variable that is assimilated in the model. By training observations of TOA reflectance from multiple sensors to map to a common AOD dataset (in this case AOD observed by the ground based Aerosol Robotic Network, AERONET), we are able to create a global, homogenous, satellite data record of AOD from MODIS observations on board the Terra and Aqua satellites. In this talk, I will present the implementation of and recent updates to the GEOS-5 NNR for MODIS collection 6 data. Other/Unknown Material Aerosol Robotic Network NASA Technical Reports Server (NTRS)
institution Open Polar
collection NASA Technical Reports Server (NTRS)
op_collection_id ftnasantrs
language unknown
topic Earth Resources and Remote Sensing
Meteorology and Climatology
spellingShingle Earth Resources and Remote Sensing
Meteorology and Climatology
Da Silva, Arlindo
Castellanos, Patricia
The GEOS-5 Neural Network Retrieval (NNR) for AOD
topic_facet Earth Resources and Remote Sensing
Meteorology and Climatology
description One of the difficulties in data assimilation is the need for multi-sensor data merging that can account for temporal and spatial biases between satellite sensors. In the Goddard Earth Observing System Model Version 5 (GEOS-5) aerosol data assimilation system, a neural network retrieval (NNR) is used as a mapping between satellite observed top of the atmosphere (TOA) reflectance and AOD, which is the target variable that is assimilated in the model. By training observations of TOA reflectance from multiple sensors to map to a common AOD dataset (in this case AOD observed by the ground based Aerosol Robotic Network, AERONET), we are able to create a global, homogenous, satellite data record of AOD from MODIS observations on board the Terra and Aqua satellites. In this talk, I will present the implementation of and recent updates to the GEOS-5 NNR for MODIS collection 6 data.
format Other/Unknown Material
author Da Silva, Arlindo
Castellanos, Patricia
author_facet Da Silva, Arlindo
Castellanos, Patricia
author_sort Da Silva, Arlindo
title The GEOS-5 Neural Network Retrieval (NNR) for AOD
title_short The GEOS-5 Neural Network Retrieval (NNR) for AOD
title_full The GEOS-5 Neural Network Retrieval (NNR) for AOD
title_fullStr The GEOS-5 Neural Network Retrieval (NNR) for AOD
title_full_unstemmed The GEOS-5 Neural Network Retrieval (NNR) for AOD
title_sort geos-5 neural network retrieval (nnr) for aod
publishDate 2017
url http://hdl.handle.net/2060/20170012197
op_coverage Unclassified, Unlimited, Publicly available
genre Aerosol Robotic Network
genre_facet Aerosol Robotic Network
op_source CASI
op_relation Document ID: 20170012197
http://hdl.handle.net/2060/20170012197
op_rights Copyright, Public use permitted
_version_ 1766402009280282624

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