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Satellite measurements are often compared with higher-precision ground-basedmeasurements as part of validation efforts. The satellite soundings arerarely perfectly coincident in space and time with the ground-basedmeasurements, so a colocation methodology is needed to aggregate "nearby"soundings into what the instrument would have seen at the location and timeof interest. We are particularly interested in validation efforts forsatellite-retrieved total column carbon dioxide (XCO2), whereXCO2 data from Greenhouse Gas Observing Satellite (GOSAT)retrievals (ACOS, NIES, RemoteC, PPDF, etc.) or SCanning Imaging AbsorptionSpectroMeter for Atmospheric CHartographY (SCIAMACHY) are often colocated andcompared to ground-based column XCO2 measurement from TotalCarbon Column Observing Network (TCCON). Current colocation methodologies for comparing satellite measurements oftotal column dry-air mole fractions of CO2 (XCO2) withground-based measurements typically involve locating and averaging thesatellite measurements within a latitudinal, longitudinal, and temporalwindow. We examine a geostatistical colocation methodology that takes aweighted average of satellite observations depending on the "distance" ofeach observation from a ground-based location of interest. The "distance"function that we use is a modified Euclidian distance with respect tolatitude, longitude, time, and midtropospheric temperature at 700 hPa. Weapply this methodology to XCO2 retrieved from GOSAT spectra by the ACOS team, cross-validate theresults to TCCON XCO2 ground-based data, and present somecomparisons between our methodology and standard existing colocation methodsshowing that, in general, geostatistical colocation produces smallermean-squared error

atmospheric measurement techniques

A method for colocating satellite &amp;lt;i&amp;gt;X&amp;lt;/i&amp;gt;&amp;lt;sub&amp;gt;CO&amp;lt;sub&amp;gt;2&amp;lt;/sub&amp;gt;&amp;lt;/sub&amp;gt; data to ground-based data and its application to ACOS-GOSAT and TCCON