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  • Data for: Using Bayesian Analysis to Quantify Uncertainty in Radiometer Measurements

    Description:
    We apply Bayesian inference to instrument calibration and experimental-data uncertainty analysis for the specific application of measuring radiative intensity with a narrow-angle radiometer. We develop a physics-based instrument model that describes temporally varying radiative intensity, the indirectly measured quantity of interest, as a function of scenario and model parameters. We identify a set of five uncertain parameters, find their probability distributions (the posterior or inverse problem) given the calibration data by applying Bayes’ Theorem, and employ a local linearization to marginalize the nuisance parameters resulting from errors-in-variables. We then apply the instrument model to a new scenario that is the intended use of the instrument, a 1.5 MW coal-fired furnace. Unlike standard error propagation, this Bayesian method infers values for the five uncertain parameters by sampling from the posterior distribution and then computing the intensity with quantifiable uncertainty at the point of a new, in-situ furnace measurement (the posterior predictive or forward problem). Given the instrument-model context of this analysis, the propagated uncertainty provides a significant proportion of the measurement error for each in-situ furnace measurement. With this approach, we produce uncertainties at each temporal measurement of the radiative intensity in the furnace, successfully identifying temporal variations that were otherwise indistinguishable from measurement uncertainty.
    Subject:
    Validation and Simulation
    Creator:
    Scheib, Kaitlyn, Spinti, Jennifer P., Smith, Sean T., Harding, N. Stanley, Smith, Philip J., and Draper, Teri S.
    Owner:
    Philip Smith
    Based Near Label Tesim:
    Salt Lake City, Utah, United States
    Language:
    English
    Date Uploaded:
    12/01/2020
    Date Modified:
    01/28/2022
    Date Created:
    November 2020
    Resource Type:
    Software or Program Code
    Identifier:
    https://doi.org/10.7278/S50D6AFQ84VP

  • Corrected Liquid Water Path Data and mascpy code for "Graupel Precipitating from Thin Arctic Clouds with Liquid Water Paths less than 50 g m-2"

    Description:
    Thin boundary layer Arctic mixed-phase clouds are generally thought to precipitate pristine and aggregate ice crystals. Here we present automated surface photographic measurements showing that only 35\% of precipitation particles exhibit negligible riming and that graupel particles $\geq1\,\rm{mm}$ in diameter commonly fall from clouds with liquid water paths less than $50\,\rm{g\,m^{-2}}$. A simple analytical formulation predicts that significant riming enhancement can occur in updrafts with speeds typical of Arctic clouds, and observations show that such conditions are favored by weak temperature inversions and strong radiative cooling at cloud top. However, numerical simulations suggest that a mean updraft speed of $0.75\,\rm{m\,s^{-1}}$ would need to be sustained for over one hour. Graupel can efficiently remove moisture and aerosols from the boundary layer. The causes and impacts of Arctic riming enhancement remain to be determined.
    Subject:
    Atmospheric sciences, Computational research, and Arctic research
    Creator:
    Garrett, Timothy J. and Fitch, Kyle E.
    Contributor:
    Shkurko, Konstantin , Talaei, Ahmad, Gaustad, Krista, and Maahn, Maximilian
    Owner:
    BRIAN MCBRIDE
    Based Near Label Tesim:
    Oliktok Point, Alaska, United States
    Language:
    English
    Date Uploaded:
    06/04/2020
    Date Modified:
    07/21/2020
    Date Created:
    Code creation dates from 20161208 to 20180609 and Processed between 20170627 and 20190320
    Resource Type:
    Software or Program Code and Dataset
    Identifier:
    https://doi.org/10.7278/s50dva5jk2pd