Remote sensing of smokestack emissions using a mobile environmental laboratory

Mapping Intimacies ◽

10.1117/12.140298 ◽

1993 ◽

Cited By ~ 3

Author(s):

Herbert W. Mosebach ◽

T. Eisenmann ◽

Y. Schulz-Spahr ◽

I. Neureither ◽

Hermann Bittner ◽

...

Keyword(s):

Remote Sensing ◽

Environmental Laboratory ◽

Smokestack Emissions

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Passive FTIR remote sensing of smokestack emissions

Field Analytical Chemistry & Technology ◽

10.1002/(sici)1520-6521(1999)3:2<111::aid-fact6>3.0.co;2-3 ◽

1999 ◽

Vol 3 (2) ◽

pp. 111-115 ◽

Cited By ~ 13

Author(s):

C. T. Chaffin ◽

T. L. Marshall ◽

N. C. Chaffin

Keyword(s):

Remote Sensing ◽

Smokestack Emissions

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A Comparison of Oceanic Precipitation Estimates in the Tropics and Subtropics

Journal of Applied Meteorology and Climatology ◽

10.1175/2009jamc2149.1 ◽

2009 ◽

Vol 48 (7) ◽

pp. 1335-1344 ◽

Cited By ~ 15

Author(s):

Kenneth P. Bowman ◽

Cameron R. Homeyer ◽

Dalon G. Stone

Keyword(s):

Remote Sensing ◽

Rain Rate ◽

Tropical Rainfall Measuring Mission ◽

Rain Gauge ◽

Geographical Patterns ◽

Gauge Data ◽

Precipitation Estimates ◽

Environmental Laboratory ◽

Remote Sensing Systems ◽

Microwave Imager

Abstract A number of Earth remote sensing satellites are currently carrying passive microwave radiometers. A variety of different retrieval algorithms are used to estimate surface rain rates over the ocean from the microwave radiances observed by the radiometers. This study compares several different satellite algorithms with each other and with independent data from rain gauges on ocean buoys. The rain gauge data are from buoys operated by the NOAA Pacific Marine Environmental Laboratory. Potential errors and biases in the gauge data are evaluated. Satellite data are from the Tropical Rainfall Measuring Mission Microwave Imager and from the Special Sensor Microwave Imager instruments on the operational Defense Meteorological Satellite Program F13, F14, and F15 satellites. These data have been processed into rain-rate estimates by the NASA Precipitation Measurement Mission and by Remote Sensing Systems, Inc. Biases between the different datasets are estimated by computing differences between long-term time averages. Most of the satellite datasets agree with each other, and with the gauge data, to within 10% or less. The biases tend to be proportional to the mean rain rate, but the geographical patterns of bias vary depending on the choice of data source and algorithm. Some datasets, however, show biases as large as about 25%, so care should be taken when using these data for climatological studies.

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