Proceedings of the Symposium on Global and Environmental Monitoring: Proceedings of the Symposium on Global and Environmental Monitoring

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Persistent identifier:: 856665355

Title:: Proceedings of the Symposium on Global and Environmental Monitoring

Sub title:: techniques and impacts ; September 17 - 21, 1990, Victoria Conference Centre, Victoria, British Columbia, Canada

Year of publication:: 1990

Place of publication:: Victoria, BC

Publisher of the original:: [Verlag nicht ermittelbar]

Identifier (digital):: 856665355

Language:: English

Document type:: Multivolume work

Volume

Persistent identifier:: 856669164

Title:: Proceedings of the Symposium on Global and Environmental Monitoring

Sub title:: techniques and impacts; September 17 - 21, 1990, Victoria Conference Centre, Victoria, British Columbia, Canada

Scope:: XIV, 912 Seiten

Year of publication:: 1990

Place of publication:: Victoria, BC

Publisher of the original:: [Verlag nicht ermittelbar]

Identifier (digital):: 856669164

Illustration:: Illustrationen, Diagramme, Karten

Signature of the source:: ZS 312(28,7,1)

Language:: English

Usage licence:: Attribution 4.0 International (CC BY 4.0)

Editor:: International Society for Photogrammetry and Remote Sensing, Commission of Photographic and Remote Sensing Data

Publisher of the digital copy:: Technische Informationsbibliothek Hannover

Place of publication of the digital copy:: Hannover

Year of publication of the original:: 2016

Document type:: Volume

Collection:: Earth sciences

Chapter

Title:: [TP-2 SPECTRAL SIGNATURES]

Document type:: Multivolume work

Structure type:: Chapter

Chapter

Title:: ON THE POSSIBLE USE OF SPATIAL VARIABILITY IN AVHRR DATA TO ESTIMATE LOW LEVEL MOISTURE AND TEMPERATURE. John C. Price

Document type:: Multivolume work

Structure type:: Chapter

71 ON THE POSSIBLE USE OF SPATIAL VARIABILITY IN AVHRR DATA TO ESTIMATE LOW LEVEL MOISTURE AND TEMPERATURE John C. Price Agricultural Research Service Beltsville, Maryland, USA Abstract The Advanced Very High Resolution Radiometers (AVHRR) onboard afternoon NOAA satellites (NOAA 7,9,11) acquire data at 10.9 and 11.8 pm in the thermal infrared window. This permits estimation of surface temperature by derivation of a correction for the major atmospheric absorber, water vapor. Although two factors (total water vapor, and its mean temperature) combine to produce the atmospheric effect, it is possible to solve two equations in three unknowns due to the special form of the radiative transfer equation. Thus two thermal IR measurements yield surface temperature and the product of atmospheric moisture and low level temperature. The formalism developed here permits estimation of the separate atmospheric terms, assuming that ground temperatures are nonuniform. The method derives formulas for the variability of the 10.8 pm measurements (channel 4) and the 11.9 pm measurements (channel 5), and thus does not apply in regions of uniform surface temperature, such as the oceans, where spatial variability is very small. Key Words: Atmospheric moisture, soundings, AVHRR, spatial variability INTRODUCTION The increasing interest in measuring long term weather and climate variability has prompted many studies of the exchange of energy and moisture between the earth and atmosphere. Although such studies have been routine in micrometeorology for many years, present interest is directed toward measuring this interaction over a substantial fraction of the surface to the globe. Almost by definition these efforts require the analysis of satellite data, with frequent and global coverage providing the essential perspective. The instrument of choice for analysis of Earth surface effects is the AVHRR onboard NOAA satellites. Five spectral channels in the visible (0.6-0.7 pm), near infrared (0.7-1.1 pm), mid infrared (3.6-3.9 pm) and thermal infrared (10.3-11.3, 11.5-12.5 pm) provide the key observations for assessing surface variability and its interaction with the atmosphere. In this paper we utilize principles applicable to regions of surface variability (Price, 1989, 1990), to the thermal infrared channels of AVHRR (called channels 4 and 5) to estimate of lower atmospheric moisture and temperature. As will become clear, uncertainties associated with the derived results for the atmospheric state are relatively large: the analysis should be linked to coincident data from the atmospheric sounder unit (HIRS) onboard the NOAA satellites. ESTIMATING OPTICAL DEPTH AND MEAN ATMOSPHERIC TEMPERATURE The discussion is based on earlier work by Price, 1984, in which an estimate was obtained for the ratio of the absorption coefficients of channels 4 and 5 of the AVHRR's. The ratio of absorption in channel 5 to that in channel 4, called R, stands out in the analysis of data from these thermal infrared channels. Through simplification of the radiative transfer equation by treating as small (first order) the atmospheric effect on satellite radiances, we obtain , T . ; 4 air (1) Rr.T . 4 air (2) where T. and T c are the satellite observed 4 5 radiance temperatures, T g is the surface radiance temperature, is the transmittance = Jdz k^q(z), and T^^ - Jdz k^q(z)T(z) . Also k^ is the absorption coefficient and q the density of atmospheric water vapor, and z is the path from surface to satellite through the atmosphere. At this level of approximation R is a constant, of order 1.35, so that one may solve the two equations (1, 2) for T g by multiplying the T^ equation by R and subtracting from the T<- equation. The result is T H ± — "4 (R-l) (T, (3) We may regard as fortuitous the fact that both and T ^ are eliminated by this process. One may produce a second result by eliminating T^ from equations 1 and 2: T. - r.T . 4 4 air 1 - r, T c - Rr. T . 5 4 air 1 - Rr, (4) In general a single equation in two unknowns (r. , T . ) is not useful. However IF surface 4 air temperature is variable, this produces variability in T^ and T,.. Provided that a statistically valid correlation may be found over some limited area between T^ and T<_, e.g. by linear regression, then we may identify derived values of the slope and offset, i.e. T. - slope • T + offset, with the values of r, and T 3 4 ai: Thus slope (1 - r 4 )/(l - Rr 4 ) (5)

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