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  • Author or Editor: Robert L. Mahoney x
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Yoram J. Kaufman
,
Robert S. Fraser
, and
Robert L. Mahoney

Abstract

Emission from burning of fossil fuels and biomass (associated with deforestation) generates a radiative forcing on the atmosphere and a possible climate chaw. Emitted trace gases heat the atmosphere through their greenhouse effect, while particulates formed from emitted SO2 cause cooling by increasing cloud albedos through alteration of droplet size distributions. This paper reviews the characteristics of the cooling effect and applies Twomey's theory to cheek whether the radiative balance favors heating or cooling for the cases of fossil fuel and biomass burning. It is also shown that although coal and oil emit 120 times as many CO2 molecules as SO2 molecules, each SO2 molecule is 50–1100 times more effective in cooling the atmosphere (through the effect of aerosol particles on cloud albedo) than a CO2 molecule is in heating it. Note that this ratio accounts for the large difference in the aerosol (3–10 days) and CO2 (7–100 years) lifetimes. It is concluded, that the cooling effect from coal and oil burning may presently range from 0.4 to 8 times the heating effect. Within this large uncertainty, it is presently more likely that fossil fuel burning causes cooling of the atmosphere rather than heating. Biomass burning associated with deforestation, on the other hand, is more likely to cause heating of the atmosphere than cooling since its aerosol cooling effect is only half that from fossil fuel burning and its heating effect is twice as large. Future increases in coal and oil burning, and the resultant increase in concentration of cloud condensation nuclei, may saturate the cooling effect, allowing the heating effect to dominate. For a doubling in the C02 concentration due to fossil fuel burning, the cooling effect is expected to be 0.1 to 0.3 of the heating effect.

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Janice L. Bytheway
,
Mimi Hughes
,
Kelly Mahoney
, and
Robert Cifelli

Abstract

The Russian River in northern California is an important hydrological resource that typically depends on a few significant precipitation events per year, often associated with atmospheric rivers (ARs), to maintain its annual water supply. Because of the highly variable nature of annual precipitation in the region, accurate quantitative precipitation estimates (QPEs) are necessary to drive hydrologic models and inform water management decisions. The basin’s location and complex terrain present a unique challenge to QPEs, with sparse in situ observations and mountains that inhibit remote sensing by ground radars. Gridded multisensor QPE datasets can fill in the gaps but are susceptible to both the errors and uncertainties from the ingested datasets and uncertainties due to interpolation methods. In this study a dense network of independently operated rain gauges is used to evaluate gridded QPE from the Multi-Radar Multi-Sensor (MRMS) during 44 precipitation events occurring during the 2015/16 and 2016/17 wet seasons (October–March). The MRMS QPE products matched the gauge estimates of precipitation reasonably well in approximately half the cases but failed to capture the spatial distribution and intensity of the rainfall in the remaining cases. ERA-Interim reanalysis data suggest that the differences in performance are related to synoptic-scale patterns and AR landfall location. These synoptic-scale differences produce different rainfall distributions and influence basin-scale winds, potentially creating regions of small-scale precipitation enhancement or suppression. Data from four profiling radars indicated that a larger fraction of the precipitation in poorly captured events occurred as shallow stratiform rain unobserved by radar.

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Keith D. Hutchison
,
Robert L. Mahoney
,
Eric F. Vermote
,
Thomas J. Kopp
,
John M. Jackson
,
Alain Sei
, and
Barbara D. Iisager

Abstract

A geometry-based approach is presented to identify cloud shadows using an automated cloud classification algorithm developed for the National Polar-orbiting Operational Environmental Satellite System (NPOESS) program. These new procedures exploit both the cloud confidence and cloud phase intermediate products generated by the Visible/Infrared Imager/Radiometer Suite (VIIRS) cloud mask (VCM) algorithm. The procedures have been tested and found to accurately detect cloud shadows in global datasets collected by NASA’s Moderate Resolution Imaging Spectroradiometer (MODIS) sensor and are applied over both land and ocean background conditions. These new procedures represent a marked departure from those used in the heritage MODIS cloud mask algorithm, which utilizes spectral signatures in an attempt to identify cloud shadows. However, they more closely follow those developed to identify cloud shadows in the MODIS Surface Reflectance (MOD09) data product. Significant differences were necessary in the implementation of the MOD09 procedures to meet NPOESS latency requirements in the VCM algorithm. In this paper, the geometry-based approach used to predict cloud shadows is presented, differences are highlighted between the heritage MOD09 algorithm and new VIIRS cloud shadow algorithm, and results are shown for both these algorithms plus cloud shadows generated by the spectral-based approach. The comparisons show that the geometry-based procedures produce cloud shadows far superior to those predicted with the spectral procedures. In addition, the new VCM procedures predict cloud shadows that agree well with those found in the MOD09 product while significantly reducing the execution time as required to meet the operational time constraints of the NPOESS system.

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