A method for colocating satellite X_(CO₂) data to ground-based data and its application to ACOS-GOSAT and TCCON

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 (X_(CO₂)), where X_(CO₂) 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 X_(CO₂) measurement from Total Carbon Column Observing Network (TCCON). Current colocation methodologies for comparing satellite measurements of total column dry-air mole fractions of CO₂ (X_(CO₂)) 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 X_(CO₂) retrieved from GOSAT spectra by the ACOS team, cross-validate the results to TCCON X_(CO₂) 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

Comparisons of the Orbiting Carbon Observatory-2 (OCO-2) XCO2_{CO_{2}} measurements with TCCON

NASA\u27s Orbiting Carbon Observatory-2 (OCO-2) has been measuring carbon dioxide column-averaged dry-air mole fraction, X$_{CO_{2}}$, in the Earth\u27s atmosphere for over 2 years. In this paper, we describe the comparisons between the first major release of the OCO-2 retrieval algorithm (B7r) and X$_{CO_{2}}$ from OCO-2\u27s primary ground-based validation network: the Total Carbon Column Observing Network (TCCON). The OCO-2 X$_{CO_{2}}$ retrievals, after filtering and bias correction, agree well when aggregated around and coincident with TCCON data in nadir, glint, and target observation modes, with absolute median differences less than 0.4 ppm and RMS differences less than 1.5 ppm. After bias correction, residual biases remain. These biases appear to depend on latitude, surface properties, and scattering by aerosols. It is thus crucial to continue measurement comparisons with TCCON to monitor and evaluate the OCO-2 X$_{CO_{2}}$ data quality throughout its mission

Improved retrievals of carbon dioxide from Orbiting Carbon Observatory-2 with the version 8 ACOS algorithm

Since September 2014, NASA's Orbiting Carbon Observatory-2 (OCO-2) satellite has been taking measurements of reflected solar spectra and using them to infer atmospheric carbon dioxide levels. This work provides details of the OCO-2 retrieval algorithm, versions 7 and 8, used to derive the column-averaged dry air mole fraction of atmospheric CO2 (XCO2) for the roughly 100&thinsp;000 cloud-free measurements recorded by OCO-2 each day. The algorithm is based on the Atmospheric Carbon Observations from Space (ACOS) algorithm which has been applied to observations from the Greenhouse Gases Observing SATellite (GOSAT) since 2009, with modifications necessary for OCO-2. Because high accuracy, better than 0.25&thinsp;%, is required in order to accurately infer carbon sources and sinks from XCO2, significant errors and regional-scale biases in the measurements must be minimized. We discuss efforts to filter out poor-quality measurements, and correct the remaining good-quality measurements to minimize regional-scale biases. Updates to the radiance calibration and retrieval forward model in version 8 have improved many aspects of the retrieved data products. The version 8 data appear to have reduced regional-scale biases overall, and demonstrate a clear improvement over the version 7 data. In particular, error variance with respect to TCCON was reduced by 20&thinsp;% over land and 40&thinsp;% over ocean between versions 7 and 8, and nadir and glint observations over land are now more consistent. While this paper documents the significant improvements in the ACOS algorithm, it will continue to evolve and improve as the CO2 data record continues to expand.</p

Comparisons of the Orbiting Carbon Observatory-2 (OCO-2) XCO2_{CO_{2}} measurements with TCCON

NASA\u27s Orbiting Carbon Observatory-2 (OCO-2) has been measuring carbon dioxide column-averaged dry-air mole fraction, X$_{CO_{2}}$, in the Earth\u27s atmosphere for over 2 years. In this paper, we describe the comparisons between the first major release of the OCO-2 retrieval algorithm (B7r) and X$_{CO_{2}}$ from OCO-2\u27s primary ground-based validation network: the Total Carbon Column Observing Network (TCCON). The OCO-2 X$_{CO_{2}}$ retrievals, after filtering and bias correction, agree well when aggregated around and coincident with TCCON data in nadir, glint, and target observation modes, with absolute median differences less than 0.4 ppm and RMS differences less than 1.5 ppm. After bias correction, residual biases remain. These biases appear to depend on latitude, surface properties, and scattering by aerosols. It is thus crucial to continue measurement comparisons with TCCON to monitor and evaluate the OCO-2 X$_{CO_{2}}$ data quality throughout its mission

Analyzing multi–domain learning for enhanced rockfall mapping in known and unknown planetary domains

Rockfalls are small–scale mass wasting events that have been observed across the solar system. They provide valuable information about the endo- and exogenic activity of their host body, but are difficult to identify and map in satellite imagery, especially on global scales and in big data sets. Past work implemented convolutional neural networks to automate rockfall mapping on the Moon and Mars with the caveat of (1) achieving sub–optimal performance and (2) requiring substantial manual image labeling efforts. Mixing annotated image data from the Moon and Mars while keeping the total number of labels constant, we show that including a small number (10%) of rockfall labels from a foreign domain (e.g. Moon) during detector training can increase performance in the home domain (e.g. Mars) by up to 6% Average Precision (AP) in comparison to a purely home domain-trained detector. We additionally show that using a large number of foreign domain training examples (90%) in combination with a small number (10%) of home domain labels can be as powerful or more powerful as exclusively (100%) using home labels in the home domain. We further observe that rockfall detectors trained on multiple domains outperform single–domain trained detectors in completely unknown domains by up to 16% AP, using image data from Ceres and comet 67P. We conduct an experiment varying only image resolution on a single planetary body (Mars) to test whether the improvement was due to training on differing resolutions specifically and show that none of the improvement can be explained by this effect alone. This means that the benefits of multi–domain training mostly draw from either variations in lighting condition, differing physical appearance/backgrounds around the target of interest for generalization purposes, or both. Our findings have important applications such as machine learning–enabled science discovery in legacy and new planetary datasets. The used dataset of martian and lunar rockfalls including a detailed description is available here: https://edmond.mpdl.mpg.de/imeji/collection/DowTY91csU3jv9S2

Comparisons of the Orbiting Carbon Observatory-2 (OCO-2) X\u3csub\u3eCO2\u3c/sub\u3e measurements with TCCON

NASA\u27s Orbiting Carbon Observatory-2 (OCO-2) has been measuring carbon dioxide column-averaged dry-air mole fraction, XCO2, in the Earth\u27s atmosphere for over 2 years. In this paper, we describe the comparisons between the first major release of the OCO-2 retrieval algorithm (B7r) and XCO2 from OCO-2\u27s primary ground-based validation network: the Total Carbon Column Observing Network (TCCON). The OCO-2 XCO2 retrievals, after filtering and bias correction, agree well when aggregated around and coincident with TCCON data in nadir, glint, and target observation modes, with absolute median differences less than 0.4 ppm and RMS differences less than 1.5 ppm. After bias correction, residual biases remain. These biases appear to depend on latitude, surface properties, and scattering by aerosols. It is thus crucial to continue measurement comparisons with TCCON to monitor and evaluate the OCO-2 XCO2 data quality throughout its mission