skip to main content

Search for: All records

Creators/Authors contains: "Raymond, K."

Note: When clicking on a Digital Object Identifier (DOI) number, you will be taken to an external site maintained by the publisher. Some full text articles may not yet be available without a charge during the embargo (administrative interval).
What is a DOI Number?

Some links on this page may take you to non-federal websites. Their policies may differ from this site.

  1. Free, publicly-accessible full text available January 1, 2023
  2. Abstract

    Predicting rain from large-scale environmental variables remains a challenging problem for climate models and it is unclear how well numerical methods can predict the true characteristics of rainfall without smaller (storm) scale information. This study explores the ability of three statistical and machine learning methods to predict 3-hourly rain occurrence and intensity at 0.5° resolution over the tropical Pacific Ocean using rain observations the Global Precipitation Measurement (GPM) satellite radar and large-scale environmental profiles of temperature and moisture from the MERRA-2 reanalysis. We also separated the rain into different types (deep convective, stratiform, and shallow convective) because of their varying kinematic and thermodynamic structures that might respond to the large-scale environment in different ways. Our expectation was that the popular machine learning methods (i.e., the neural network and random forest) would outperform a standard statistical method (a generalized linear model) because of their more flexible structures, especially in predicting the highly skewed distribution of rain rates for each rain type. However, none of the methods obviously distinguish themselves from one another and each method still has issues with predicting rain too often and not fully capturing the high end of the rain rate distributions, both of which are commonmore »problems in climate models. One implication of this study is that machine learning tools must be carefully assessed and are not necessarily applicable to solving all big data problems. Another implication is that traditional climate model approaches are not sufficient to predict extreme rain events and that other avenues need to be pursued.

    « less
  3. In this paper a photovoltaic system is proposed that achieves high energy yield by integrating bifacial silicon cells into a spectrum-splitting module. Spectrum splitting is accomplished using volume holographic optical elements to spectrally divide sunlight onto an array of photovoltaic cells with different bandgap energies. Light that is reflected from the ground surface onto the rear side of the module is converted by the bifacial silicon cells. The energy yield of the system is optimized by tuning the volume holographic element parameters, such as film thickness, index modulation, and construction point source positions. An example is presented for utility-scale illumination parameters in Tucson, Arizona, that attains an energy yield of1010kw⋅<#comment/>hryr⋅<#comment/>m2, which is 32.8% of the incident solar insolation.