Climate Monitoring from Space: Asynoptic Sampling Considerations

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  • 1 Department of Astrophysical, planetary, and Atmospheric Sciences, University of Colorado, Boulder, Colorado
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Abstract

Monitoring climate variability from space is considered from the standpoint of satellite sampling. Asynoptic sampling leads to well-defined limits in spatial and temporal resolution which are violated by behavior involving sufficiently small scales. Because of aliasing to larger scales, unresolved behavior can influence long-term and spatially averaged behavior important in climate.

From physical processes operating within the climate system, two classes of space-time variability which challenge the information content of asynoptic sampling are identified. Random fluctuations coherent on small space and time scales are characteristic of convective processes in the troposphere and of the dispersion of long-lived constituents such as ozone in the lower stratosphere. Diurnal variations are also an important component of tropospheric convection, as they are of short-lived chemical species such as ozone in the upper stratosphere. Each of these forms of variability can violate the sampling limitations inherent to satellite data from a single orbiting platform.

Implications of undersampling to instantaneous and long-term mean diagnostics are discussed for each of these classes of behavior for orbital and viewing geometries relevant to climate. When the field being monitored has significant variance beyond the Nyquist limits of asynoptic sampling, the complete space-time behavior cannot be recovered faithfully. Diagnostics such as synoptic maps and space-time spectra are prone to contamination from unresolved scales. Aliasing from unresolved random variability cancels in averages over a sufficiently long record, leading to accurate time-mean behavior provided no other forms of unresolved variability are present. A similar cancellation occurs for unresolved diurnal variability if the satellite orbit precesses through local time.

Through careful selection of sampling, long-term mean diagnostics can in principle be retrieved from a single orbiting platform even though the complete behavior may be seriously undersampled. Although such diagnostics represent the primary tool for studying climate, it may be necessary to observe behavior on shorter time scales (e.g., diurnal) to meaningfully interpret these quantities and understand how changes in the climate system occur. To do so will require measurements from multiple orbiting platforms. Sampling strategies and how such measurements can be assimilated so as to recover the full information content of the collective data are suggested.

Abstract

Monitoring climate variability from space is considered from the standpoint of satellite sampling. Asynoptic sampling leads to well-defined limits in spatial and temporal resolution which are violated by behavior involving sufficiently small scales. Because of aliasing to larger scales, unresolved behavior can influence long-term and spatially averaged behavior important in climate.

From physical processes operating within the climate system, two classes of space-time variability which challenge the information content of asynoptic sampling are identified. Random fluctuations coherent on small space and time scales are characteristic of convective processes in the troposphere and of the dispersion of long-lived constituents such as ozone in the lower stratosphere. Diurnal variations are also an important component of tropospheric convection, as they are of short-lived chemical species such as ozone in the upper stratosphere. Each of these forms of variability can violate the sampling limitations inherent to satellite data from a single orbiting platform.

Implications of undersampling to instantaneous and long-term mean diagnostics are discussed for each of these classes of behavior for orbital and viewing geometries relevant to climate. When the field being monitored has significant variance beyond the Nyquist limits of asynoptic sampling, the complete space-time behavior cannot be recovered faithfully. Diagnostics such as synoptic maps and space-time spectra are prone to contamination from unresolved scales. Aliasing from unresolved random variability cancels in averages over a sufficiently long record, leading to accurate time-mean behavior provided no other forms of unresolved variability are present. A similar cancellation occurs for unresolved diurnal variability if the satellite orbit precesses through local time.

Through careful selection of sampling, long-term mean diagnostics can in principle be retrieved from a single orbiting platform even though the complete behavior may be seriously undersampled. Although such diagnostics represent the primary tool for studying climate, it may be necessary to observe behavior on shorter time scales (e.g., diurnal) to meaningfully interpret these quantities and understand how changes in the climate system occur. To do so will require measurements from multiple orbiting platforms. Sampling strategies and how such measurements can be assimilated so as to recover the full information content of the collective data are suggested.

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