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David A. Short

Researcher at ENSCO, Inc.

Publications -  58
Citations -  3446

David A. Short is an academic researcher from ENSCO, Inc.. The author has contributed to research in topics: Precipitation & Radar. The author has an hindex of 26, co-authored 58 publications receiving 3269 citations. Previous affiliations of David A. Short include Goddard Space Flight Center.

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Evidence from Tropical Raindrop Spectra of the Origin of Rain from Stratiform versus Convective Clouds

TL;DR: In this article, an empirical stratiform-convective classification method based on N 0 and R (rainfall rate) is presented. But, the occurrence of precipitation was found to be 74% (stratiform) and 26% (convection) but total rainfall, on the other hand, was...
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Filtering of milankovitch cycles by earth's geography

TL;DR: In this paper, a linear, two-dimensional, seasonal energy balance climate model has been proposed to model the seasonal temperature response to orbital forcing on land, which has been shown to have a sensitivity comparable to general circulation models.
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Diurnal Variations in Tropical Oceanic Cumulus Convection during TOGA COARE

TL;DR: In this paper, the diurnal variations in atmospheric convection, dynamic/thermodynamic fields, and heat/moisture budgets over the equatorial Pacific warm pool region are analyzed based on data collected from different observation platforms during the Intensive Observation Period of the Tropical Ocean Global Atmosphere Coupled Ocean-Atmosphere Response Experiment (TOGA COARE).
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Simple Energy Balance Model Resolving the Seasons and the Continents' Application to the Astronomical Theory of the Ice Ages

TL;DR: In this paper, an analysis of the properties of a one-level seasonal energy balance climate model having explicit, two-dimensional land-sea geography, where land and sea surfaces are strictly distinguished by the local thermal inertia employed and transport is governed by a smooth, latitude-dependent diffusion mechanism.
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Bayesian Prediction of Transformed Gaussian Random Fields

TL;DR: The Bayesian transformed Gaussian model (BTG) provides an alternative to trans-Gaussian kriging taking into account the major sources of uncertainty, including uncertainty about the “normalizing transformation” itself, in the computation of the predictive density function.