Machine-learning data and model for autoconversion and accretion rates

Chiu, J.-Y. Christine

doi:10.5439/1763390

Title: Machine-learning data and model for autoconversion and accretion rates

Dataset
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Abstract

This PI product includes two key components of a recently developed machine-learning model for predicting autoconversion and accretion rates. The training data set and the testing data set used to build our machine-learning model, containing sets of four input variables (cloud water content, cloud droplet number concentration, drizzle water content, and drizzle drop number concentration) and two output variables (autoconversion rate and accretion rate). The machine-learning model, containing information on the trained weights and biases, and the coefficients for scaling the inputs and outputs variables. The uncertainty in the new estimates of autoconversion and accretion rates is about 15% and 5%, respectively.

Authors:

Chiu, J.-Y. Christine

ORNL

Publication Date:: Wed Jun 21 04:00:00 UTC 2017

DOE Contract Number:: AC05-00OR22725

Research Org.:: Atmospheric Radiation Measurement (ARM) Archive, Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (US); ARM Data Center, Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)

Sponsoring Org.:: USDOE Office of Science (SC), Biological and Environmental Research (BER)

Collaborations:: PNNL, BNL, ANL, ORNL

Subject:: 54 ENVIRONMENTAL SCIENCES; Cloud water content,Cloud droplet number concentration,Rain water content,Rain drop number concentration,Autoconversion rate,Accretion rate, ARM, DOE.

OSTI Identifier:: 1763390

DOI:: https://doi.org/10.5439/1763390

Citation Formats


                    Chiu, J.-Y. Christine. Machine-learning data and model for autoconversion and accretion rates.  United States: N. p., 2017. 
        Web.  doi:10.5439/1763390.

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                    Chiu, J.-Y. Christine. Machine-learning data and model for autoconversion and accretion rates.  United States.  doi:https://doi.org/10.5439/1763390

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                    Chiu, J.-Y. Christine. 2017.  
"Machine-learning data and model for autoconversion and accretion rates".  United States.  doi:https://doi.org/10.5439/1763390.  https://www.osti.gov/servlets/purl/1763390. Pub date:Wed Jun 21 04:00:00 UTC 2017

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@article{osti_1763390,

  title        = {Machine-learning data and model for autoconversion and accretion rates},

  author       = {Chiu, J.-Y. Christine},

  abstractNote = {This PI product includes two key components of a recently developed machine-learning model for predicting autoconversion and accretion rates. The training data set and the testing data set used to build our machine-learning model, containing sets of four input variables (cloud water content, cloud droplet number concentration, drizzle water content, and drizzle drop number concentration) and two output variables (autoconversion rate and accretion rate). The machine-learning model, containing information on the trained weights and biases, and the coefficients for scaling the inputs and outputs variables. The uncertainty in the new estimates of autoconversion and accretion rates is about 15% and 5%, respectively.},

  doi          = {10.5439/1763390},

  journal      = {},

  number       = ,

  volume       = ,

  place        = {United States},

  year         = {Wed Jun 21 04:00:00 UTC 2017},

  month        = {Wed Jun 21 04:00:00 UTC 2017}

}

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DOI: https://doi.org/10.5439/1763390

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