Abstract
Abstract. Satellite measurements are often compared with higher-precision ground-based measurements as part of validation efforts. The satellite soundings are rarely perfectly coincident in space and time with the ground-based measurements, so a colocation methodology is needed to aggregate "nearby" soundings into what the instrument would have seen at the location and time of interest. We are particularly interested in validation efforts for satellite-retrieved total column carbon dioxide (XCO2), where XCO2 data from Greenhouse Gas Observing Satellite (GOSAT) retrievals (ACOS, NIES, RemoteC, PPDF, etc.) or SCanning Imaging Absorption SpectroMeter for Atmospheric CHartographY (SCIAMACHY) are often colocated and compared to ground-based column XCO2 measurement from Total Carbon Column Observing Network (TCCON). Current colocation methodologies for comparing satellite measurements of total column dry-air mole fractions of CO2 (XCO2) with ground-based measurements typically involve locating and averaging the satellite measurements within a latitudinal, longitudinal, and temporal window. We examine a geostatistical colocation methodology that takes a weighted average of satellite observations depending on the "distance" of each observation from a ground-based location of interest. The "distance" function that we use is a modified Euclidian distance with respect to latitude, longitude, time, and midtropospheric temperature at 700 hPa. We apply this methodology to XCO2 retrieved from GOSAT spectra by the ACOS team, cross-validate the results to TCCON XCO2 ground-based data, and present some comparisons between our methodology and standard existing colocation methods showing that, in general, geostatistical colocation produces smaller mean-squared error.
Highlights
Carbon dioxide (CO2) is an important anthropogenic greenhouse gas, and quantifying the exchange of CO2 between the atmosphere and the Earth’s surface is a critical part of the global carbon cycle and an important determinant of future climate (Gruber et al, 2009)
One important measure of CO2 is total column carbon dioxide (XCO2 ), which is available from ground-based Total Carbon Column Observing Network (TCCON; Wunch et al, 2011a) and from space-based satellite instruments such as the Greenhouse gases Observing Satellite (GOSAT; Yokota et al, 2004; Hamazaki et al, 2005) and the SCanning Imaging Absorption SpectroMeter for Atmospheric CHartographY (SCIAMACHY; Bovensmann et al, 1999)
Having done averaging kernel correction to put the TCCON and Atmospheric CO2 Observations from Space (ACOS) retrievals on the same footing, we describe our methodology for optimally colocating ACOS-GOSAT observations to any TCCON location
Summary
Carbon dioxide (CO2) is an important anthropogenic greenhouse gas, and quantifying the exchange of CO2 between the atmosphere and the Earth’s surface is a critical part of the global carbon cycle and an important determinant of future climate (Gruber et al, 2009). The v3.3 data user’s guide recommends a linear bias correction to ACOS-GOSAT XCO2 based on the difference between the retrieved and prior surface pressure from the A-band cloud screen and the ratio of the signal in the strong CO2 band to that of the O2A band (Osterman et al, 2013). Since such bias correction was done through comparison of v3.3 ACOS retrievals with models and TCCON retrievals, we refrain from applying the v3.3 bias correction to avoid potential “feedback” in the comparison of our colocated v3.3 ACOS and TCCON values. The midtropospheric temperature field at 700 hPa should be directly proportional to the potential temperature at 700 hPa for the range of temperature of interest, and its inclusion as a covariate should allow us to construct better colocation metrics
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