MS Defense by Manon Hugeunin

Event Details
  • Date/Time:
    • Friday November 30, 2018
      1:00 pm - 3:00 pm
  • Location: Collaborative Visualization Environment (CoVE) Weber Space Science and Technology Building (SST-II)
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Summaries

Summary Sentence: DEVELOPMENT AND VALIDATION OF 3-D CLOUD FIELDS USING DATA FUSION AND MACHINE LEARNING TECHNIQUES

Full Summary: No summary paragraph submitted.

Master’s Thesis Defense

 

By

 

Manon Huguenin

(Advisor: Prof. Dimitri N. Mavris)

01:00 PM, Friday, November 30, 2018

Weber Space Science and Technology Building (SST-II)

Collaborative Visualization Environment (CoVE)

 

DEVELOPMENT AND VALIDATION OF 3-D CLOUD FIELDS USING DATA FUSION AND MACHINE LEARNING TECHNIQUES

 

ABSTRACT:

Climate change predictions are currently achieved using Global Climate Models (GCMs), which are complex representations of the major climate components and their interactions. These predictions present high levels of uncertainty, which are mostly due to the way clouds are represented in climate models. Cloud-related phenomena, such as cloud-radiative forcing, are represented through physically-motivated parameterizations, which lead to uncertainties in cloud representations. Improving the parameterizations required for representing clouds in GCMs is thus a current focus of climate modeling research efforts. Integrating cloud satellite data into GCMs has been proved essential for achieving this goal. Yet, the availability of satellite data is limited, in particular for vertical data. In order for satellite cloud data to be usefully compared to global representations of clouds in GCMs, additional vertical cloud data has to be generated to provide a more global coverage. Consequently, the overall objective of this thesis is to support the validation of GCMs cloud representations through the generation of 3D cloud fields using cloud vertical data from space-borne sensors.

This has already been attempted by several studies through the implementation of physics-based and similarity-based approaches. However, such studies have a number of limitations, which motivate the need for novel approaches to the generation of 3D cloud fields. For this purpose, efforts have been initiated at ASDL to develop an approach that leverages data fusion and machine learning techniques to generate 3-D cloud field domains. In particular, these efforts have led to the development of a cloud predictive classification model that is based on decision trees and integrates atmospheric data to predict vertical cloud fraction. However, several limitations were identified in this model and the approach that led to it. First, its performance is lower when predicting lower-altitude clouds, and its overall performance could still be greatly improved. Second, the model has only been assessed at “on-track” locations, while the construction of data at “off-track” locations is necessary for generating 3D cloud fields. Last, the model has not been validated in the context of GCMs cloud representation, and no satisfactory level of model accuracy has been determined in this context.

This work aims at overcoming these limitations by taking the following approach. The model obtained from previous efforts is improved by integrating additional, higher accuracy data, by investigating the correlation within atmospheric predictors, and by implementing additional classification machine learning techniques. Then, profiles are predicted at “off-track” locations. Horizontal validation of these computed profiles is performed against an existing dataset containing comparable values. This leads to the generation of a coherent global 3D cloud fields dataset. Last, a methodology for validating this computed dataset in the context of GCMs cloud-radiative forcing representation is developed. The Fu-Liou code is implemented on sample vertical profiles from the computed dataset, and the output radiative fluxes are analyzed. This research significantly improves the model developed in previous efforts, as well as validates the computed global dataset against existing data. Such validation demonstrates the potential of a machine learning-based approach to generate 3D cloud fields. Additionally, this research provides a benchmarked methodology to further validate this machine learning-based approach in the context of study. Altogether, this thesis contributes to NASA’s ongoing efforts towards improving GCMs and climate change predictions as a whole.

 

Committee Members:

Prof. Dimitri Mavris (Advisor)

School of Aerospace Engineering, Georgia Institute of Technology

Dr. Olivia Pinon Fischer

School of Aerospace Engineering, Georgia Institute of Technology

Dr. Patrick Taylor

Climate Science Branch, NASA Langley Research Center

Additional Information

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Graduate Studies

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Keywords
ms defense
Status
  • Created By: Tatianna Richardson
  • Workflow Status: Published
  • Created On: Nov 26, 2018 - 1:43pm
  • Last Updated: Nov 26, 2018 - 1:43pm